Disruptive Emerging Transportation Technologies 0784415986, 9780784415986

Table of contents :
Book_5160_C000
Half Title
Title Page
Copyright Page
Contents
List of Chapter Authors
Preface
Acknowledgments
Book_5160_C001
Chapter 1: Emerging Technologies Impacting the Future of Transportation
1.1 Transportation Artificial Intelligence and Machine Learning
1.1.1 Introduction to Artificial Intelligence and Machine Learning Techniques for Transportation Application
1.1.2 Introduction to Transportation Systems Management and Operation
1.1.3  Use Cases for Artificial Intelligence and Machine Learning in Transportation
1.1.3.1 Traffic Control
1.1.3.2 Decentralized Congestion Mitigation
1.1.3.3 Smart Work Zone Management
1.1.3.4 Wrong-Way Driver Detection and Mitigation
1.1.3.5 Cybersecurity Threat Detection and Mitigation
1.1.4 Conclusions of Section 1.1
1.2 Edge Computing, Fog Computing, and Cloud Computing Technologies
1.2.1 The Demand on the Existing Transportation Infrastructure
1.2.2 Cloud Computing as an Alternative Solution
1.2.3 Demand of Edge Computing
1.2.4 Overview of Edge Computing Technologies
1.2.5 Cloudlet
1.2.6 Mobile Edge Computing
1.2.7 “Fog” Computing
1.2.8 Development of Edge Computing and Associated Technologies
1.2.8.1 Edge Computing and Cloud Computing
1.2.8.2 Edge Computing and Internet of Things
1.2.8.3 Edge Computing and 5G
1.2.9 Transportation Scenarios of Applying Edge Computing
1.2.10 Building Decentralized ITS Infrastructure
1.2.11 Impact of Edge Computing on Connected and Automated Vehicle Roadside Infrastructure Migration
1.2.12 Summary of Section 1.2
1.3 Fifth-Generation Innovative Communications Technology
1.3.1 Review of 5G Data Services
1.3.2 Impact of 5G Data Services on Smart Transportation Infrastructure Enhancement
1.3.2.1 Enhanced Mobile Broadband Service Impact
1.3.2.2 Massive Machine-Type Communications Service Impact
1.3.2.3 Ultrareliable and Low-Latency Communications Service Impact
1.3.3 Impacts of 5G Data Services on Connected and Automated Vehicle Migration
1.3.4 Impact of Continuous Evolution on 5G Standards
1.3.5 Testing and Demonstration of 5G Cellular V2X
1.3.6 Challenges in the United States with 5G Cellular V2X
1.3.7 Summary of Section 1.3
1.4 Design and Development of Virtual Reality–Based Driving Simulation
1.4.1 Virtual Reality
1.4.2 Simulation of the Real World
1.4.3 Interactivity and Interface
1.4.4 Hardware
1.4.5 Software and Scenario Creation
1.4.5.1 Planning Stage
1.4.5.2 VR Creation Stage
1.4.5.3 Data Collection and Analysis
1.4.6 Demonstrated Study of Urban Mobility in Driving Simulation
1.4.7 Conclusion and Challenges to Section 1.4
1.5 Applied Internet of Things Technologies in Transportation
1.5.1 Overviewing of Internet of Things Technologies
1.5.2 IoTs Communication Technologies and Protocols
1.5.3 Standardization Migration of Internet of Things Technologies
1.5.3.1 Internet of Things Sensors
1.5.3.2 Internet of Things Supporting Cloud Services and Application Layer Protocols
1.5.3.3 Internet of Things Application Domains
1.5.3.4 Linking Internet of Things with Other Technologies
1.5.3.5 Impact of 5G Migration
1.5.3.6 Impact of Edge Computing
1.5.4 Transportation Scenarios of Applying Internet of Things
1.5.4.1 Transportation Infrastructure Monitoring and Asset Management by Internet of Things
1.5.4.2 Bridge Monitoring by Internet of Things
1.5.4.3 Smart City and ITS Applications with Internet of Things
1.5.4.4 Connected and Automated Vehicles and Internet of Things
1.5.5 Conclusion of Section 1.5
References
Book_5160_C002
Chapter 2: Surface Transportation Automation
2.1 Concepts of Vehicles in Compliance with Society of Automobile Engineers Automation Levels
2.1.1 Society of Automobile Engineers Automation Levels
2.1.2 Connected Vehicle
2.1.3 Autonomous Vehicle
2.1.4 Cooperative Vehicles with Automation
2.1.5 Autonomous Shuttle
2.1.5.1 Operation Design Domain
2.1.5.2 Deployment of Autonomous Vehicles/Shuttles
2.1.5.3 Autonomous Shuttle as Micro Transit
2.2 Key Supportive Systems of Connected Vehicles
2.2.1 Safety Systems
2.2.2 Mobility Systems
2.2.3 Environment Systems
2.3 Key Design Elements of Autonomous Vehicles
2.3.1 Perception
2.3.2 Navigation
2.3.3 Localization
2.3.4 Command and Control
2.3.5 Health Monitoring
2.3.6 Behavior Architecture
2.3.7 World Model
2.3.8 Advantages of Lower Levels of Automated Driving
2.3.8.1 Collision Avoidance and Emergency Braking
2.3.8.2 Steering and Lane Keeping
2.3.8.3 Bus Platooning
2.3.8.4 Managed Lanes for Automated Shuttles
2.4  Distributed Ledger Technologies for Connected and Autonomous Vehicle Systems
2.4.1 An Introduction to Distributed Ledger Technology
2.4.2 Use of Distributed Ledger Technology in Transportation
2.5 Application of Transportation Automation Technologies
2.5.1 Connected and Automated Vehicle Applications
2.5.2 Mobility Smart Contracts
2.5.3 Cooperative Driving Automation
2.5.4 Security Considerations
2.6 Driving Automation Definition and Autonomous Vehicle Laws
2.7 Summary
References
Book_5160_C003
Chapter 3: Autonomous Vehicle Testing
3.1 Introduction
3.2 Autonomous Vehicle Technology Testing
3.3 Mechanical Testing
3.3.1 Safety Systems
3.3.2 Engine and Drivetrain
3.4 Software and Cyber Security Data Testing
3.4.1 Driving Model
3.4.2 Sensor Interfaces
3.4.3 Cybersecurity
3.4.4 Cyber Data Testing
3.4.5 System of Software Systems Testing
3.5 Combined System Testing
3.6 Complete Vehicle Testing
3.7 System of Systems Testing
3.8 Version Testing
3.9 Simulated versus Real-World Testing
3.10 Analysis Frameworks
3.11 Software Simulation
3.11.1 Design Simulation
3.11.2 Software in the Loop Simulation
3.11.3 Hardware in the Loop Simulation
3.11.4 Driving Simulator
3.11.5 Environment Simulation
3.11.6 Virtual Reality–Based Simulation
3.12 DOT-Approved AV Proving Grounds
3.13 Testing Facilities
3.13.1 MCity (Michigan)
3.13.2 Transportation Research Center (Ohio)
3.13.3 Area X.O (Ottawa, Canada)
3.13.4 GoMentum Station (California)
3.13.5 Automated Driving Systems for Rural America (Iowa)
3.14 Upcoming Testing Facilities
3.14.1 SunTrax (Florida)
3.14.2 Curiosity Lab (Georgia)
3.15 Current Deployments
3.16 Impact of Policies on AV Testing
3.17 Critical AV Testing Issues for Future Deployment
3.18 Summary
References
Book_5160_C004
Chapter 4: Emerging Delivery and Mobility Services
4.1 Automated Delivery and Logistics
4.1.1 Background
4.1.2 Benefits of Automation of Delivery and Logistics
4.1.3 Automated Delivery and Logistic Applications
4.1.3.1 Last-Mile Transportation
4.1.3.2 Automated Freight Ports
4.1.3.3 Automated Warehouse Management
4.1.3.4 Automated Fleet Management
4.1.3.5 Automated Reverse Logistics
4.1.4 Technology in Automated Delivery and Logistics
4.1.4.1 Technologies Used in Freight Delivery
4.1.4.2 Technology Used in Warehouse Management
4.1.4.3 Future Technologies in Automated Delivery and Logistics
4.1.5 Policy Considerations
4.1.6 Future Research Directions
4.2 Mobility as a Service
4.2.1 Role of Mobility as a Service in the Context of Smart Cities
4.2.2 Implementation Features of Mobility as a Service
4.2.2.1 Core Characteristics of Mobility as a Service
4.2.2.2 Mobility as a Service Integration
4.2.2.3 Key Elements of Mobility as a Service Ecosystem
4.2.3 Review of Mobility as a Service Initiatives around the World
4.2.4 Application of Technologies in Mobility as a Service
4.2.5 Potential Research Areas
4.2.5.1 Research Needs for Understanding Customers
4.2.5.2 Research Needs for Business Models
4.2.5.3 Research Needs for Policy Implications
4.3 Mobility on Demand
4.3.1 Importance of Mobility on Demand Services
4.3.1.1 Mobility Needs
4.3.1.2 Travel Behaviors
4.3.1.3 Existing Transportation Services
4.3.2 Implementation Features of Different Mobility on Demand Business Models for Passenger and Goods Movement
4.3.2.1 Business-to-Consumer
4.3.2.2 Business-to-Government
4.3.2.3 Business to Business
4.3.2.4 Peer-to-Peer Mobility Marketplace
4.3.2.5 Peer-to-Peer Delivery Marketplace
4.3.3 Technologies Enabling Mobility on Demand Services
4.3.4 Contribution of Mobility on Demand in Shared Mobility Ecosystem
4.3.5 Future Research Direction
4.4 Summary
References
Book_5160_C005
Chapter 5: Shared Sustainable Mobility
5.1 Shared Vehicle Services
5.1.1 Background
5.1.2 Shared Vehicle Services and Transformed Mobility Patterns
5.1.2.1 Ride-Sharing Service Models
5.1.2.2 Ride-Sharing Policy Considerations
5.1.2.3 Carsharing Service Models
5.1.2.4 Carsharing Policy Considerations
5.1.2.5 Parking Regulations
5.1.2.6 Insurance and Taxes
5.1.2.7 Other Shared Vehicle Services
5.1.3 Use of Technology in Shared Vehicle Services
5.1.4 Future Research Directions
5.2 Shared Bicycle Service
5.2.1 What is Shared Bicycle Service?
5.2.2 How is Shared Bicycle Service Operated?
5.2.2.1 First Generation
5.2.2.2 Second Generation
5.2.2.3 Third Generation
5.2.2.4 Fourth Generation
5.2.3 Engineering Issues
5.2.4 Urban Planning Issues
5.2.4.1 Stakeholders in Planning Shared Bicycle Service
5.2.4.2 Planning Shared Bicycle Service within an Auto-Oriented Urban Structure
5.3 First Mile/Last Mile Solutions
5.3.1 Common Transportation Means Used for Connecting First Mile/Last Mile
5.3.2 First Mile/Last Mile Strategies
5.3.2.1 Land-Use Planning
5.3.2.2 Integration between Public Transit and Other Feeder Modes
5.3.2.3 Innovative Motilities as Potential First Mile/Last Mile Connectors
5.3.3 Technologies Powering First Mile/Last Mile Connection
5.4 Summary
References
Book_5160_C006
Chapter 6: Cooperative and Automated Traffic Control
6.1 Traffic Signal Control Methods in Connected and Automated Vehicle Environment
6.2 Self-Organized Intelligent Adaptive Traffic Control
6.2.1 Introduction
6.2.2 System Elements
6.2.3 Optimizing Traffic Signals
6.2.4 Self-Adaptive Signal Controls
6.2.4.1 AALONS-D
6.2.4.2 Genetic Algorithms
6.2.4.3 Video Imaging
6.2.4.4 Sustainable Controls
6.2.5 Signal-Free Autonomous Intersection Control
6.2.5.1 Centralized Intersection Traffic Control
6.2.5.2 Decentralized Traffic Control
6.2.5.3 Intelligent Roundabout
6.2.6 Cooperative Coordinated Adaptive Corridor Signal Timing Optimization
6.2.6.1 Modeling Traffic Flow Parameters Using Aggregate Connected Vehicle Mobility Datasets
6.2.6.2 Identification of Deceleration and Acceleration Points within a Queue
6.2.6.3 Deceleration Points Rearranged in Descending Order
6.2.6.4 Optimization Model Formulation
6.2.6.5 Dynamic Programming Procedure for Offset Optimization
6.3 Safe Interactions of Pedestrians/Cyclists with Automated Vehicles
6.3.1 Background
6.3.2 General Considerations of Transition Effect
6.3.3 Pedestrian and Cyclist Reactions to Automated Vehicles
6.3.4 Communication in Interactions between Roader Users and Automated Vehicles
6.3.5 Automated Vehicle Communication with Pedestrians
6.4 Eco-Driving and Traffic Control
6.4.1 Eco-Signal Control
6.4.2 Eco-Driving Control with Connected and Automated Vehicle Technologies
6.4.2.1 Eco-Driving Control Using Uncertain Signal Timing
6.4.2.2 Eco-Driving Using V2X-Driven Signal Control
6.4.3 Engine Restart Method
6.5 Integrated Ramp and Corridor Control
6.5.1 Overview of Advanced Ramp Metering Technologies
6.5.2 Conceptual Methodology for Integrated Ramp and Corridor Control
6.5.2.1 First Priority Objective
6.5.2.2 Second Priority Objective
6.5.2.3 Third Objective
6.6 Summary
References
Book_5160_C007
Chapter 7: Unmanned Aerial Vehicle and Vertical Takeoff and Landing Technologies
7.1 Unmanned Aerial Vehicle
7.1.1 Introduction
7.1.2 Unmanned Aircraft History and Scope
7.1.3 Multirotor Design and Technologies
7.2 Urban Air Mobility
7.2.1 Unmanned Aerial Vehicle Traffic Management
7.2.2 Federal Aviation Administration Regulations for Small UAVs
7.2.3 Unmanned Aerial System Path Planning
7.2.4 Detect-and-Avoid Systems
7.2.5 Conclusions of Sections 7.1 and 7.2
7.3 Overview of Vertical Takeoff and Landing Aviation
7.3.1 Overview of Current Vertical Takeoff and Landing Technology
7.3.2 Need for Automated Flight Systems
7.3.2.1 Safety
7.3.2.2 Airframe Design
7.3.2.3 Integration
7.3.2.4 Struggles with Propulsion
7.3.2.5 Propellers
7.3.2.6 Electronics
7.3.2.7 Design of Battery Pack
7.4 Summary
References
Book_5160_Index

Citation preview

Disruptive Emerging Transportation Technologies

Technical Committee on Connected and Automated Vehicles Impacts EDITED BY

Heng Wei, Ph.D., P.E. Yinhai Wang, Ph.D., P.E. Jianming Ma, Ph.D, P.E.

Disruptive Emerging Transportation Technologies

Other Titles of Interest Engineering for Sustainable Communities: Principles and Practices, edited by William E. Kelly, Barbara Luke, and Richard N. Wright. (ASCE/Committee on Sustainability 2017). This book defines and outlines sustainable engineering methods for real-world engineering projects. (ISBN 978-0-7844-1481-1) Permeable Pavements, by the Permeable Pavements Task Committee; edited by Bethany Eisenberg, Kelly Collins Lindow, and David R. Smith. (ASCE/ EWRI 2015). This book provides the most current guidance available for the design, construction, and maintenance of permeable pavement systems that provide transportation surfaces and manage stormwater and urban runoff. (978-0-7844-1378-4) Failure to Act: The Economic Impact of Current Investment Trends in Surface Transportation Infrastructure, by the American Society of Civil Engineers. (ASCE 2011). This Failure to Act report provides an objective analysis of the economic implications of underinvestment in U.S. infrastructure related to surface transportation, including highways, bridges, railroads, and transit systems. (978-0-7844-7880-6) Airfield Safety and Capacity Improvements: Case Studies on Successful Projects, edited by Geoffrey S. Baskir and Edward L. Gervais. (ASCE/T&DI 2012). This technical report presents six case studies focused on the planning, engineering, and management of major construction projects at active airports. (ISBN 978-0-7844-1256-5) Biodiesel Production: Technologies, Challenges, and Future Prospects, R. D. Tyagi, Rao Y. Surampalli, Tian C. Zhang, Song Yan, and Xiaolei Zhang. (ASCE/ EWRI 2019). This book provides in-depth information on fundamentals, approaches, technologies, source materials and associated socio-economic and political impacts of biodiesel production. (ISBN 978-0-7844-1534-4) Impacts of Future Weather and Climate Extremes on United States Infrastructure, by the Task Committee on Future Weather and Climate Extremes; edited by Mari R. Tye and Jason P. Giovannettone. (ASCE/Committee on Adaptation to a Changing Climate 2021). This report provides prioritization frameworks in accommodating projected future weather and climate extremes for policy makers and engineers involved in infrastructure planning and design. (978-0-7844-1586-3)

Disruptive Emerging Transportation Technologies Prepared by the Technical Committee on Connected and Automated Vehicles (CAV) Impacts of the Transportation & Development Institute of the American Society of Civil Engineers

Edited by Heng Wei, Ph.D., P.E. Yinhai Wang, Ph.D., P.E. Jianming Ma, Ph.D., P.E.

Published by the American Society of Civil Engineers

Library of Congress Cataloging-in-Publication Data Names: Transportation & Development Institute (American Society of Civil Engineers). Connected and Automated Vehicles Impacts Committee, author. | Wei, Heng (Civil engineer), editor. | Wang, Yinhai, editor. | Ma, Jianming (Engineer), editor. Title: Disruptive emerging transportation technologies / prepared by the Technical Committee on Connected and Automated Vehicles (CAV) Impacts of the Transportation & Development Institute of the American Society of Civil Engineers ; editors, Heng Wei, Yinhai Wang, Jianming Ma. Description: Reston, Virginia : American Society of Civil Engineers, [2022] | Includes bibliographical references and index. | Summary: “This title provides a forward-looking overview of the relevant 4IR technologies and their potential impacts on the future disruptive emerging transportation”-- Provided by publisher. Identifiers: LCCN 2021055813 | ISBN 9780784415986 (paperback) | ISBN 9780784483909 (pdf) Subjects: LCSH: Transportation--Technological innovations. | Traffic engineering-Technological innovations. | Industry 4.0. Classification: LCC TA1145 .T725 2022 | DDC 629.04--dc23/eng/20211220 LC record available at https://lccn.loc.gov/2021055813 Published by American Society of Civil Engineers 1801 Alexander Bell Drive Reston, Virginia 20191-4382 www.asce.org/bookstore | ascelibrary.org Any statements expressed in these materials are those of the individual authors and do not necessarily represent the views of ASCE, which takes no responsibility for any statement made herein. No reference made in this publication to any specific method, product, process, or service constitutes or implies an endorsement, recommendation, or warranty thereof by ASCE. The materials are for general information only and do not represent a standard of ASCE, nor are they intended as a reference in purchase specifications, contracts, regulations, statutes, or any other legal document. ASCE makes no representation or warranty of any kind, whether express or implied, concerning the accuracy, completeness, suitability, or utility of any information, apparatus, product, or process discussed in this publication, and assumes no liability therefor. The information contained in these materials should not be used without first securing competent advice with respect to its suitability for any general or specific application. Anyone utilizing such information assumes all liability arising from such use, including but not limited to infringement of any patent or patents. ASCE and American Society of Civil Engineers—Registered in US Patent and Trademark Office. Photocopies and permissions. Permission to photocopy or reproduce material from ASCE publications can be requested by sending an email to [email protected] or by locating a title in the ASCE Library (https://ascelibrary.org) and using the “Permissions” link. Errata: Errata, if any, can be found at https://doi.org/10.1061/9780784415986. Copyright © 2022 by the American Society of Civil Engineers. All Rights Reserved. ISBN 978-0-7844-1598-6 (print) ISBN 978-0-7844-8390-9 (PDF) ISBN 978-0-7844-8419-7 (ePub) Manufactured in the United States of America. 27 26 25 24 23 22    1 2 3 4 5

Contents List of Chapter Authors...........................................................................................................xi Preface........................................................................................................................................ xiii Acknowledgments................................................................................................................. xix Chapter 1 Emerging Technologies Impacting the Future of Transportation............................................................................ 1 Paul A. Avery, Ken Yang, Ming Tang 1.1 Transportation Artificial Intelligence and Machine Learning..................1 1.1.1 Introduction to Artificial Intelligence and Machine Learning Techniques for Transportation Application.....................................1 1.1.2 Introduction to Transportation Systems Management and Operation.....................................................................................................4 1.1.3 Use Cases for Artificial Intelligence and Machine Learning in Transportation.......................................................................................5 1.1.4 Conclusions of Section 1.1................................................................... 14 1.2 Edge Computing, Fog Computing, and Cloud Computing Technologies.......................................................................................................... 15 1.2.1 The Demand on the Existing Transportation Infrastructure........................................................................................... 16 1.2.2 Cloud Computing as an Alternative Solution...............................17 1.2.3 Demand of Edge Computing..............................................................17 1.2.4 Overview of Edge Computing Technologies............................... 18 1.2.5 Cloudlet..................................................................................................... 18 1.2.6 Mobile Edge Computing..................................................................... 19 1.2.7 “Fog” Computing................................................................................... 19 1.2.8 Development of Edge Computing and Associated Technologies............................................................................................ 21 1.2.9 Transportation Scenarios of Applying Edge Computing......... 23 1.2.10 Building Decentralized ITS Infrastructure..................................... 24 1.2.11 Impact of Edge Computing on Connected and Automated Vehicle Roadside Infrastructure Migration............ 25 1.2.12 Summary of Section 1.2....................................................................... 25 1.3 Fifth-Generation Innovative Communications Technology................. 27 1.3.1 Review of 5G Data Services................................................................. 28 1.3.2 Impact of 5G Data Services on Smart Transportation Infrastructure Enhancement.............................................................. 30 v

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1.3.3 Impacts of 5G Data Services on Connected and Automated Vehicle Migration............................................................ 31 1.3.4 Impact of Continuous Evolution on 5G Standards..................... 32 1.3.5 Testing and Demonstration of 5G Cellular V2X........................... 33 1.3.6 Challenges in the United States with 5G Cellular V2X.............. 33 1.3.7 Summary of Section 1.3....................................................................... 34 1.4 Design and Development of Virtual Reality–Based Driving Simulation............................................................................................................... 35 1.4.1 Virtual Reality........................................................................................... 35 1.4.2 Simulation of the Real World.............................................................. 36 1.4.3 Interactivity and Interface................................................................... 37 1.4.4 Hardware................................................................................................... 37 1.4.5 Software and Scenario Creation....................................................... 39 1.4.6 Demonstrated Study of Urban Mobility in Driving Simulation................................................................................................. 42 1.4.7 Conclusion and Challenges to Section 1.4.................................... 45 1.5 Applied Internet of Things Technologies in Transportation................. 46 1.5.1 Overviewing of Internet of Things Technologies....................... 46 1.5.2 IoTs Communication Technologies and Protocols..................... 47 1.5.3 Standardization Migration of Internet of Things Technologies............................................................................................ 48 1.5.4 Transportation Scenarios of Applying Internet of Things....... 54 1.5.5 Conclusion of Section 1.5.................................................................... 56 References......................................................................................................................... 56 Chapter 2  Surface Transportation Automation......................................63 Heng Wei, Paul A. Avery, Hao Liu, Gaurav Kashyap, Jianming Ma 2.1 Concepts of Vehicles in Compliance with Society of Automobile Engineers Automation Levels................................................. 63 2.1.1 Society of Automobile Engineers Automation Levels.............. 63 2.1.2 Connected Vehicle................................................................................. 65 2.1.3 Autonomous Vehicle............................................................................. 67 2.1.4 Cooperative Vehicles with Automation......................................... 68 2.1.5 Autonomous Shuttle............................................................................. 69 2.2 Key Supportive Systems of Connected Vehicles....................................... 77 2.2.1 Safety Systems......................................................................................... 77 2.2.2 Mobility Systems..................................................................................... 79 2.2.3 Environment Systems........................................................................... 82 2.3 Key Design Elements of Autonomous Vehicles......................................... 83 2.3.1 Perception................................................................................................. 84 2.3.2 Navigation................................................................................................. 85 2.3.3 Localization.............................................................................................. 86 2.3.4 Command and Control......................................................................... 86 2.3.5 Health Monitoring.................................................................................. 86 2.3.6 Behavior Architecture........................................................................... 86

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vii

2.3.7 World Model............................................................................................. 87 2.3.8 Advantages of Lower Levels of Automated Driving.................. 87 2.4 Distributed Ledger Technologies for Connected and Autonomous Vehicle Systems......................................................................... 89 2.4.1 An Introduction to Distributed Ledger Technology.................. 91 2.4.2 Use of Distributed Ledger Technology in Transportation....... 92 2.5 Application of Transportation Automation Technologies..................... 92 2.5.1 Connected and Automated Vehicle Applications...................... 92 2.5.2 Mobility Smart Contracts.................................................................... 94 2.5.3 Cooperative Driving Automation..................................................... 95 2.5.4 Security Considerations....................................................................... 96 2.6 Driving Automation Definition and Autonomous Vehicle Laws......... 97 2.7 Summary................................................................................................................. 99 References.......................................................................................................................100 Chapter 3  Autonomous Vehicle Testing.................................................105 Jonathan Corey, Heng Wei 3.1 Introduction.........................................................................................................105 3.2 Autonomous Vehicle Technology Testing.................................................106 3.3 Mechanical Testing............................................................................................107 3.3.1 Safety Systems.......................................................................................108 3.3.2 Engine and Drivetrain.........................................................................109 3.4 Software and Cyber Security Data Testing................................................109 3.4.1 Driving Model........................................................................................ 110 3.4.2 Sensor Interfaces.................................................................................. 112 3.4.3 Cybersecurity......................................................................................... 112 3.4.4 Cyber Data Testing............................................................................... 113 3.4.5 System of Software Systems Testing............................................. 114 3.5 Combined System Testing.............................................................................. 115 3.6 Complete Vehicle Testing................................................................................ 116 3.7 System of Systems Testing...............................................................................117 3.8 Version Testing.....................................................................................................117 3.9 Simulated versus Real-World Testing.......................................................... 118 3.10 Analysis Frameworks......................................................................................... 119 3.11 Software Simulation.......................................................................................... 119 3.11.1 Design Simulation................................................................................ 120 3.11.2 Software in the Loop Simulation.................................................... 120 3.11.3 Hardware in the Loop Simulation.................................................. 121 3.11.4 Driving Simulator................................................................................. 121 3.11.5 Environment Simulation....................................................................122 3.11.6 Virtual Reality–Based Simulation...................................................123 3.12 DOT-Approved AV Proving Grounds...........................................................123 3.13 Testing Facilities..................................................................................................125 3.13.1 MCity (Michigan)...................................................................................125 3.13.2 Transportation Research Center (Ohio)........................................ 126

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3.13.3 Area X.O (Ottawa, Canada)................................................................ 126 3.13.4 GoMentum Station (California)........................................................ 126 3.13.5 Automated Driving Systems for Rural America (Iowa)............ 127 3.14 Upcoming Testing Facilities............................................................................ 129 3.14.1 SunTrax (Florida)................................................................................... 129 3.14.2 Curiosity Lab (Georgia)....................................................................... 129 3.15 Current Deployments.......................................................................................130 3.16 Impact of Policies on AV Testing................................................................... 131 3.17 Critical AV Testing Issues for Future Deployment................................... 132 3.18 Summary...............................................................................................................134 References....................................................................................................................... 135 Chapter 4  Emerging Delivery and Mobility Services............................ 139 Deogratias Eustace, Kakan Dey, Md Tawhidur Rahman, Baraah Qawasmeh 4.1 Automated Delivery and Logistics............................................................... 139 4.1.1 Background............................................................................................ 139 4.1.2 Benefits of Automation of Delivery and Logistics.................... 139 4.1.3 Automated Delivery and Logistic Applications......................... 141 4.1.4 Technology in Automated Delivery and Logistics................... 143 4.1.5 Policy Considerations..........................................................................150 4.1.6 Future Research Directions...............................................................150 4.2 Mobility as a Service.......................................................................................... 151 4.2.1 Role of Mobility as a Service in the Context of Smart Cities............................................................................................. 153 4.2.2 Implementation Features of Mobility as a Service...................154 4.2.3 Review of Mobility as a Service Initiatives around the World......................................................................................................... 159 4.2.4 Application of Technologies in Mobility as a Service..............162 4.2.5 Potential Research Areas...................................................................164 4.3 Mobility on Demand.........................................................................................165 4.3.1 Importance of Mobility on Demand Services............................ 167 4.3.2 Implementation Features of Different Mobility on Demand Business Models for Passenger and Goods Movement............................................................................................... 172 4.3.3 Technologies Enabling Mobility on Demand Services........... 174 4.3.4 Contribution of Mobility on Demand in Shared Mobility Ecosystem.............................................................................. 175 4.3.5 Future Research Direction................................................................. 176 4.4 Summary............................................................................................................... 177 References....................................................................................................................... 178 Chapter 5  Shared Sustainable Mobility................................................. 185 Kakan Dey, Deogratias Eustace, Na Chen, Ting Zuo, Heng Wei, Md Tawhidur Rahman 5.1 Shared Vehicle Services....................................................................................185

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5.1.1 Background............................................................................................185 5.1.2 Shared Vehicle Services and Transformed Mobility Patterns....................................................................................................185 5.1.3 Use of Technology in Shared Vehicle Services...........................194 5.1.4 Future Research Directions............................................................... 197 5.2 Shared Bicycle Service...................................................................................... 197 5.2.1 What is Shared Bicycle Service?......................................................198 5.2.2 How is Shared Bicycle Service Operated?...................................200 5.2.3 Engineering Issues...............................................................................204 5.2.4 Urban Planning Issues........................................................................205 5.3 First Mile/Last Mile Solutions.........................................................................207 5.3.1 Common Transportation Means Used for Connecting First Mile/Last Mile...............................................................................208 5.3.2 First Mile/Last Mile Strategies..........................................................209 5.3.3 Technologies Powering First Mile/Last Mile Connection............................................................................................. 214 5.4 Summary............................................................................................................... 216 References....................................................................................................................... 217 Chapter 6  Cooperative and Automated Traffic Control........................223 Heng Wei, Gaurav Kashyap, Zhixia Li 6.1 Traffic Signal Control Methods in Connected and Automated Vehicle Environment.........................................................................................223 6.2 Self-Organized Intelligent Adaptive Traffic Control...............................226 6.2.1 Introduction...........................................................................................226 6.2.2 System Elements...................................................................................229 6.2.3 Optimizing Traffic Signals..................................................................230 6.2.4 Self-Adaptive Signal Controls..........................................................232 6.2.5 Signal-Free Autonomous Intersection Control.........................234 6.2.6 Cooperative Coordinated Adaptive Corridor Signal Timing Optimization...........................................................................240 6.3 Safe Interactions of Pedestrians/Cyclists with Automated Vehicles..................................................................................................................247 6.3.1 Background............................................................................................247 6.3.2 General Considerations of Transition Effect...............................248 6.3.3 Pedestrian and Cyclist Reactions to Automated Vehicles......249 6.3.4 Communication in Interactions between Roader Users and Automated Vehicles....................................................................250 6.3.5 Automated Vehicle Communication with Pedestrians...........251 6.4 Eco-Driving and Traffic Control.....................................................................251 6.4.1 Eco-Signal Control................................................................................251 6.4.2 Eco-Driving Control with Connected and Automated Vehicle Technologies..........................................................................256 6.4.3 Engine Restart Method......................................................................260 6.5 Integrated Ramp and Corridor Control......................................................260 6.5.1 Overview of Advanced Ramp Metering Technologies...........260

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6.5.2 Conceptual Methodology for Integrated Ramp and Corridor Control....................................................................................263 6.6 Summary...............................................................................................................267 References.......................................................................................................................268 Chapter 7 Unmanned Aerial Vehicle and Vertical Takeoff and Landing Technologies....................................................277 Rumit Kumar, Aditya M. Deshpande, Drew Scott, James Z. Wells, Manish Kumar, Shaaban Abdallah 7.1 Unmanned Aerial Vehicle................................................................................277 7.1.1 Introduction...........................................................................................277 7.1.2 Unmanned Aircraft History and Scope........................................278 7.1.3 Multirotor Design and Technologies.............................................280 7.2 Urban Air Mobility..............................................................................................284 7.2.1 Unmanned Aerial Vehicle Traffic Management.........................284 7.2.2 Federal Aviation Administration Regulations for Small UAVs..............................................................................................287 7.2.3 Unmanned Aerial System Path Planning.....................................288 7.2.4 Detect-and-Avoid Systems...............................................................291 7.2.5 Conclusions of Sections 7.1 and 7.2................................................292 7.3 Overview of Vertical Takeoff and Landing Aviation...............................293 7.3.1 Overview of Current Vertical Takeoff and Landing Technology.............................................................................................293 7.3.2 Need for Automated Flight Systems.............................................294 7.4 Summary...............................................................................................................299 References.......................................................................................................................300 Index.............................................................................................................307

List of Chapter Authors Chapter 1: Emerging Technologies Impacting the Future of Transportation Paul A. Avery, M.ASCE, AECOM Ken Yang, AECOM Ming Tang, LEED AP, University of Cincinnati Chapter 2: Surface Transportation Automation Heng Wei, Ph.D., P.E., F.ASCE, University of Cincinnati Paul A. Avery, M.ASCE, AECOM Hao Liu, Ph.D., PATH Program, University of California at Berkeley Gaurav Kashyap, CT Consultants Jianming Ma, Ph.D., P.E., M.ASCE, Texas Department of Transportation Chapter 3: Autonomous Vehicle Testing Jonathan Corey, Ph.D., University of Cincinnati Heng Wei, Ph.D., P.E., F.ASCE, University of Cincinnati Chapter 4: Emerging Delivery and Mobility Services Kakan Dey, Ph.D., P.E., West Virginia University Deogratias Eustace, Ph.D., P.E., M.ASCE, University of Dayton Md Tawhidur Rahman, West Virginia University Baraah Qawasmeh, University of Dayton Chapter 5: Shared Sustainable Mobility Kakan Dey, Ph.D., P.E., West Virginia University Deogratias Eustace, Ph.D., P.E., M. ASCE, University of Dayton Na Chen, Ph.D., Sun Yat-sen University, China Ting Zuo, Ph.D., Ohio-Kentucky-Indian Regional Council of Governments Heng Wei, Ph.D., P.E., F.ASCE, University of Cincinnati Md Tawhidur Rahman, West Virginia University Chapter 6: Cooperative and Automated Traffic Control Heng Wei, Ph.D., P.E., F.ASCE, University of Cincinnati Gaurav Kashyap, CT Consultants Zhixia Li, Ph.D., University of Louisville Chapter 7: Unmanned Aerial Vehicle and Vertical Takeoff and Landing Technologies Rumit Kumar, University of Cincinnati Aditya M. Deshpande, University of Cincinnati Drew Scott, University of Cincinnati James Z. Wells, University of Cincinnati Manish Kumar, Ph.D., University of Cincinnati Shaaban Abdallah, Ph.D., University of Cincinnati xi

Preface We are facing plenty of challenges in transportation, infrastructure, and land-use development and how technologies transform the mobility of people and goods into new paradigms. The Fourth Industrial Revolution (4IR) is characterized by a fusion of technologies that is blurring the lines between the physical, digital, and biological spheres, collectively referred to as cyber-physical systems. It is marked by innovative technology breakthroughs in a number of fields, including transportation, which has been influenced more and more by emerging technologies such as the Internet of Things (IoT), fifth-generation (5G) wireless technologies, artificial intelligence, robotics, and connected and automated vehicles (CAVs). Integrated applications of such emerging technologies will not only provide great potential to mitigate the existing transportation problems, but also bring revolutionary changes to the ownership models (e.g., privately owned vehicles and shared mobility) and cooperative automation-adaptive infrastructures, as well as emerging transportation modes such as urban air mobility. The rapid evolution of disruptive technologies and their convergence with the developments in the field of transportation-related data analytics will profoundly affect many aspects of education and workforce development over the next 5 to 20 years. Although such disruptive emerging transportation technologies may provide a windfall of research and development opportunities, it is imperative and high time to foster an advanced and a proactive understanding of these new areas that can accelerate innovations through a mixture of synthetic research and education. As foreseen by Paul Mason, director of Emerging & Enabling Technologies for Innovate UK, the most adaptable education of disruptive technologies will play a key role in helping people develop new survival skills to augment foundational learning, which necessarily will look very different from most educational offerings we are seeing today. This new and exciting area has no guiding text or repository of experience. This edited book, titled Disruptive Emerging Transportation Technologies Primer, is hence aimed to fill this gap and, thus, accelerate the rapid growth in the area of disruptive transportation.

OVERALL GOAL OF THE BOOK The ASCE T&DI’s Technical Committee on CAV Impacts is motivated to publish this book in response to the needs to bring critical fundamental understandings of relevant disruptive emerging technologies and their potential impacts on smart transportation infrastructure and systems, to a broader audience. This book is xiii

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intended to inform the civil engineering community to be prepared for adapting to the potential changes brought by the emerging transportation technologies. It is expected to be a critically valuable reference for relevant educators to reimagine their roles, update or redesign their curricula, and adopt very different pedagogical strategies to address this inevitability. Overall, our goal is to produce a book that educators and researchers in this area must reference.

TARGET AUDIENCE AND STRUCTURE Audiences for the book include educators, researchers, and students interested in the smart transportation area. The book also targets at professionals of public and private sectors, including engineers, managers, planners, and policymakers, as well as specialists of all areas, whose work will be affected by the smart transportation trends. Because this book touches on so many overlapping fields, we expect interest from a broader audience.

CONTENT OF THE BOOK The book consists of seven chapters, and a brief introduction of each chapter is described as follows.

Chapter 1: Emerging Technologies Impacting the Future of Transportation This chapter provides fundamentals to the emerging technologies that are expected to profoundly impact the future of transportation systems. The subjects involved in this chapter are categorized into five sections. Section 1.1 emphasizes key artificial intelligence (AI) and machine learning (ML)–related techniques and possible applications in transportation systems as CAV technologies are entering the domain of innovative transportation system management and operations. In Section 1.2, “edge,” “fog”, and “cloud” computing technologies are reviewed from multiple perspectives. These technologies include edge computing along with the alternative solution of Cloud Computing and their demands on existing transportation infrastructures; mobile edge computing and fog computing; transportation application scenarios that the aforementioned technologies can support; decentralized intelligent transportation systems (ITS) infrastructure; and impact of the edge computing technologies on CAV-based roadside infrastructure migration. In Section 1.3, detailed components are presented for the design and development of virtual reality–based driving simulation, along with a demonstrated study of urban mobility. Lastly, Section 1.5 gives an overview of applied IoT technologies in transportation, combined with 5G wireless and edge computing technologies, such as transportation infrastructure monitoring, smart city and ITS applications, and CAV-related applications.

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Chapter 2: Surface Transportation Automation Under the categories of driving automation levels as defined by the SAE International, this chapter provides a comprehensive overview of the connected vehicle (CV) and autonomous vehicle (AV) technologies and other automationbased vehicles such as cooperative vehicle and autonomous shuttle. Key supportive systems for CVs are overviewed in detail under the categories of safety systems, mobility systems, and environment systems. Key components of AV designs are presented in detail with multiple functions such as perception, navigation, localization, command and control, health monitoring, behavior architecture, and world model, followed by the advantages of automated operation. In addition, the chapter presents a special technology for CAV systems, that is, the distributed ledger technology (DLT) that has emerged as a potentially revolutionary approach across a variety of industries, including transportation, financial, supply chain management and logistics, and energy. The use of a cryptographic architecture (e.g., blockchain or tangle) within a distributed ledger provides a mechanism for enabling peer-to-peer transactions and maintaining records in an immutable form distributed across a system of nodes to be auditable by anyone. DLT can also potentially provide inherent security against attack and the capability to self-heal when a part of the system is damaged, lost, or otherwise compromised. Section 2.5 gives a comprehensive profile of the potential to effectively apply CAV technologies and associated systems to achieve the envisioned future of transportation in terms of enhanced safety, increased mobility, and improved environment; mobility smart contracts, cooperative driving automation, and security considerations. Last but not least, the definition of driving automation and AV laws are briefly discussed in compliance with the SAE International’s defined driving automation levels, which have been adopted by The US Department of Transportation’s National Highway Traffic Safety Administration.

Chapter 3: Autonomous Vehicle Testing Testing and validation of AV technologies, systems, and vehicles is a highly complex field of endeavor. This complication stems from the difficulty in fully specifying the requirements that need to be satisfied by the design and the massive variability in conditions that an AV may encounter in the field. Another challenge inherent to AV testing is the statistical issues associated with proving reliability against events and conditions that occur rarely. To better understand the features and challenges of AV testing, this chapter is specially designed to introduce the identified seven areas of testing that fall into the broad categories of simulation and field testing. These seven areas are as follows: AV technologies, mechanics, software and cybersecurity data, combined system, complete vehicle certification, system of systems, and version testing. In addition, other important information about typical testing systems that are underway or under deployment is introduced, including DOT–approved AV proving grounds, and testing facilities involved in several typical testing systems in the United States and Canada. It is

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worth noting that as AV technologies mature, standards and versioning changes over time will threaten the AV fleet with fragmentation. Fragmentation in the AV fleet may cause cybersecurity vulnerabilities, divergences in vehicle behavior, and reliability issues. Therefore, impacts of policies on AV testing and critical AV testing issues for future deployment are also discussed in the last two sections.

Chapter 4: Emerging Delivery and Mobility Services This chapter begins with an introduction of methods and technologies associated with automated delivers and logistics, warehouse management, fleet management, reverse logistics, as well as policy considerations. The trends of automation are also leading to the use of unmanned delivery systems such as air delivery drones, robots, and self-driving trucks in the near future. This chapter comprehensively introduces a new constitution of the transportation options, called MaaS, which intends to shift the mindsets of individuals from using privately owned vehicles as their main mode of transportation to other on-demand types of mobility services. The MaaS-based integrated mobility service is effective and efficient for users and travelers through a single virtual service layer or user interface that can be accessible via the user’s smartphone to pay once and use all these modes in one trip. Mobility on demand or MOD is an innovative emerging transportation concept wherein individuals can access mobility and goods services on demand by dispatching or using shared mobility, courier services, and public transportation solutions to make their journeys more efficient. MOD for passenger modes can be enabled through shared modes, public transportation, and other emerging transportation solutions. MOD for goods delivery can be provided through smartphone app-based and aerial delivery services (e.g., drones). New technologies and widespread use of smartphones are behind the disruptive MOD systems such as Uber and Lyft, which have made safe and convenient individualized travels affordable. This chapter delivers a comprehensive overview of the MOD-related implementation features, including B2C, B2G, B2B, P2P mobility and delivery marketplace, and technologies and factors contributing to MOD in the shared mobility ecosystem. Lastly, future research needs are discussed.

Chapter 5: Shared Sustainable Mobility This chapter focuses on shared vehicle services, shared bicycle service (SBS), and first mile/last mile (FM/LM) as a solution to foster public transportation. Section 5.1 introduces the shared vehicle service models following the concept of sharing economy, that is, the idea of renting and borrowing goods and services instead of owning them. Advanced applications of communication, internet and mobile technologies, and computing infrastructures are enhancing the capabilities of location-based services and social networking sites, such as enabling real-time on-demand shared vehicle services to mass populations. Carsharing policy, parking regulations, insurance and tax issues, urbanization, and microeconomic growth are among the factors influencing the shared vehicle service market in the United States.

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Section 5.2 unfolds a scroll of SBS from its definition to the operation mechanism and to the main issues with respect to both engineering and urban planning perspectives. SBS is discussed in three dimensions: responsible party; location choice and connectivity; and design, provision, and maintenance. To this end, this section specifically presents and discusses the operation mechanism of SBS for four generations, covering who is taking charge of the system, where and how shared bikes are located, and how shared bikes are designed and provided from the 1960s to the present day. SBS-related engineering and urban planning issues are summarized under different social, political, and urban contexts. Section 5.3 reviews first-mile-and-last mile (FM/LM) issues in public transportation (PT) services and commonly used FM/LM connectors such as walk, bike, and auto. FM/LM access patterns and characteristics of their suitability in urban environment are overviewed. The planning countermeasures to FM/LM problems are presented, including transit-oriented land-use planning, improved network connectivity between feeder modes and PT, convenient parking facilities at PT stops/stations, and emerging shared mobility options as FM/LM connectors. Potential applications of information and communication technologies in minimizing FM/LM gaps and facilitating FM/LM trips are also overviewed.

Chapter 6: Cooperative and Automated Traffic Control This chapter proactively provides a comprehensive overview of the possibly foreseen scenarios of changes in traffic control strategies owing to the introduction of CAV technologies into infrastructural systems. The chapter also makes a summary of traffic signal control methods in a CAV environment. Specifically, the following subjects are discussed in detail: • Self-organized intelligent adaptive traffic control strategy, including details about system components, involved data analytics and relevant technologies, and optimization models; • Interactions between AVs and pedestrians or cyclists, including the state-ofthe-art development of relevant communication means for the autonomous transportation systems; • Eco-driving and traffic control systems with specific technological considerations; and • Integrated ramp metering and the local arterial traffic control system. Although the CAV data-driven and V2X-based traffic control innovations are anticipated to bring evolutionary changes in traffic control technologies, and even a revamping of traffic control infrastructures, a lot of converging challenges remain or come out as the emerging transportation technologies evolve over time. Nonetheless, the future of the road traffic control systems may become more and more cooperative among CAVs, infrastructures, and road users, especially vulnerable users (e.g., pedestrians and cyclists) at crossings, through information or communication-based intelligent interactions among them. This chapter could provide insightful information to researchers and practitioners of interest.

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Chapter 7: Unmanned Aerial Vehicle and Vertical Takeoff and Landing Technologies In this chapter, various unmanned aerial vehicle (UAV) technologies and their possible impacts on transportation are discussed, and the design and control strategies of these systems are reviewed. We also discuss the integration of the UAVs into the National Airspace System (NAS) and emerging concepts on urban air mobility that focus on applications of drones such as package delivery and freight transport, air metro, and air taxis. We briefly describe UAV traffic management and Federal Aviation Administration regulations for UAV integration into national airspace. Technological advancements in terms of path planning and obstacle avoidance in the dynamic environment of these unmanned vehicles are also reviewed. The section on VTOL aviation discusses the importance of VTOL systems in urban areas to enable the use of flying vehicles in future transportation systems. The UAV has great potential in various applications such as the development of smart cities to create a positive impact on society. The UAV can be used for a multitude of applications in cities, including traffic and crowd monitoring. In particular, the UAV is suitable for monitoring gridlock places where traditional fixed-places monitoring technologies are difficult to capture the infrastructure inspection, transportation emergency response or natural disaster monitoring, transportation and civil security control, merchandise delivery, as well as environmental and pollution monitoring. However, we have to be aware of the emerging challenges during the integration of UAVs into smart cities because of issues and concerns related to safety, privacy, and ethical/legal use. From licensing and certification issues to privacy and security qualms, there is currently no way to seamlessly integrate UAVs into smart cities. Nonetheless, the review of the UAV and VTOL technologies provides an advanced knowledge for the readers of interest. This content can also be used as educational materials to supplement relevant subjects in teaching. Heng Wei, Ph.D., P.E., F.ASCE Professor and Director of Civil Engineering Program and ART-EngineS Transportation Research Laboratory University of Cincinnati, Ohio Yinhai Wang, Ph.D., P.E., F.ASCE Professor and Director of PacTrans University of Washington, Washington Jianming Ma, Ph.D., P.E., M.ASCE Senior Engineer Texas Department of Transportation, Texas

Acknowledgments We would like to extend our deep appreciation to all the wonderful authors for their invaluable time and great efforts in contributing to this book. We are really grateful to them for providing us the opportunity to work with them on such a pathbreaking issue as rethinking the future of transportation. Their strong expertise and deft skills in revealing the state-of-the-art development of emerging technologies brought to the table brilliant ideas and understandings that imply new trends of movement of people and goods in the future. Despite the pandemic, their persistence and diligence made this work highly productive. We especially want to express our deep and sincere gratitude to the following reviewers for spending their invaluable time on reviewing the whole manuscript, offering helpful comments, and making constructive suggestions for improving the quality of the book. • David A. Noyce, Ph.D., P.E., F.ASCE, Dr. Arthur F. Hawnn Professor of Transportation Engineering and Executive Associate Dean, College of Engineering, University of Wisconsin at Madison. • Chandra R. Bhat, Ph.D., M.ASCE, Joe J. King Endowed Chair in Engineering, University Distinguished Professor, University of Texas at Austin • Cong Chen, Ph.D., P.E., M.ASCE, Research Associate, Center for Urban Transportation Research, University of South Florida. • Muhammad Amer, M.ASCE, Managing Director, Transportation & Development Institute (T&DI) and Future World Vision, ASCE. In addition, many thanks go to the ASCE T&DI’s Technical Committee on connected and automated vehicles (CAV) Impacts. This book would not have seen the light of day without the sponsorship of the CAV Impacts Committee. Last but not least, we would like to extend our special thanks to Muhammad Amer, managing director of ASCE (T&DI) and Donna Dickert, senior manager and acquisitions editor of ASCE Books. Their effective assistance and strong support helped us immensely, making our work enjoyable and rewarding.

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Emerging Technologies Impacting the Future of Transportation Paul A. Avery, Ken Yang, Ming Tang

1.1 TRANSPORTATION ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING 1.1.1 Introduction to Artificial Intelligence and Machine Learning Techniques for Transportation Application The fields of artificial intelligence (AI) and machine learning (ML) have enjoyed a renewed focus over the past decade. They have been largely driven by an exponential increase in computing power available at reasonable cost and by advances in AI, ML, data science, and predictive analytics tools and techniques, which when combined with expert domain knowledge, are powerful tools for analyzing large data sets that contain complex patterns and associations and for enabling systems to learn from their experiences. These technologies have the potential to enable a more proactive and efficient management of transportation systems, particularly with the introduction of emerging technologies such as connected and automated vehicles (CAVs). The importance of expert domain knowledge cannot be understated when it comes to harnessing the power of data science, AI, and ML. Properly constructed, these tools can enable a more comprehensive approach in the application of predictive analytics and decision support systems (DSS) to more quickly grasp complex phenomena like the formation and behavior of traffic congestion. For example, rather than simply reacting to incidents once they have occurred, these tools can be deployed to detect subtle changes in key indicators that may precede some incident types and can attempt to mitigate the risk of an incident through proactive measures such as dynamically reducing speed limits (Kaviani 2019, Antonio 2019). Similarly, these tools can also play a pivotal role in the active management of signalized intersections and variable speed limits, enhancing transit schedule 1

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reliability, optimizing freight distribution and logistics, managing special and emergency events, and improving asset management. The following sections will explore ways in which the broad umbrella of these techniques and tools can be applied to transportation systems. One point of confusion is how exotic-sounding terms such as AI and ML are used, which conflates their relationship and are often used interchangeably. Similarly, ML is often where data science is the more appropriate term. For the purposes of this discussion, AI is a broad term encompassing a system that is modeled after the decision-making ability of a human being, whereas ML is specifically focused on methods and algorithms automating analytical model building and decision making and for providing a mechanism to self-improve (learning). Data science is the set of tools and techniques used for discovering patterns in large data sets and extracting relevant insights. Further, when AI is mentioned, it is usually in the sense of narrow AI versus general AI, and the difference between these two types of AI is significant. Narrow AI is best suited for solving specific tasks with high accuracy, and even some complex tasks that require some degree of adaptation, and this is what is usually meant when the term AI is mentioned. General AI is at the moment a concept, which represents an AI system that is capable of understanding related concepts in a way we normally associate with human intelligence and is capable of a general adaptability. General AI is also considered dangerous by many, as it can represent a system capable of adaptation beyond what our intelligence can even comprehend and which may have motivations counter to our own. Data science techniques, specifically, enable the processing and analysis of large amounts of data on which AI and ML solutions depend. An application does not necessarily need the added complexity of AI/ML if it can be managed using more traditional tools and techniques; however, modern transportation systems already generate large amounts of data, which is often ignored, or stored and forgotten. The introduction of technologies such as CAVs, which have the potential to generate orders of magnitude greater amounts of data than current ITS devices, will greatly compound this issue. ML techniques, including genetic, deep, reinforcement, and adversarial, significantly improve a system’s ability to learn through its own experience, based on the data it encounters, and to improve its future performance. Each technique has its own set of strengths and weaknesses and must be selected based on the nature of the challenge. Figure 1-1 illustrates a number of the main subclassifications of ML techniques (supervised, unsupervised, and reinforcement), and each of these subclasses has unique capabilities, limitations, and appropriate use cases. For example, in the case of reinforcement learning, a system of reward and punishment is constructed such that the learning algorithm is rewarded or punished based on how well its actions produce results in line with the goals of the system. As a thought exercise, imagine a virtual robot wandering around in an environment, which contains red and green spheres. We want the algorithms controlling the movements of the robot to learn to seek out only the green spheres, so we set up a system where the robot has a goal that can take any number of forms, but for the sake of this example, let us say the robot has an internal currency, and

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Figure 1-1.  Machine learning is a collection of techniques. its goal is to increase this currency. Internally, it assigns a very positive reaction for increasing the currency amount and a very negative reaction for decreasing it. A separate algorithm then decrements the robot’s currency any time the robot picks up a red sphere and increments it with green spheres. The learning algorithm must be able to monitor the state of the current currency level in the robot and detect any changes to it. The algorithm must also be able to associate state data, which, for the purposes of this example, include only the color of the sphere that has been picked up, with changes in the currency level. Now as the robot begins to explore its environment, it initially makes no distinction between the colored spheres. However, over time, as it picks up green spheres and is rewarded, and red spheres and is punished, the reinforcement learning algorithm begins to associate the red spheres with the negative result and will begin to systematically avoid the red spheres, and conversely, systematically seek out the green spheres for the reward they offer in the form of the increased internal currency. Systems that utilize these techniques have the potential to harness the very large data sets currently transmitted through ITS devices and those expected to come from connected and automated vehicles (CV/AVs), which are collected asynchronously from disparate sources and which exhibit stochasticity, to enable transportation agencies to manage their physical and digital infrastructures more efficiently and securely. This topic is further segmented subsequently into the following application-specific use cases: • Traffic signal optimization and coordination, • Decentralized congestion mitigation,

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• Asset detection and management, • Smart work zone management, • Wrong-way driver (WWD) detection and mitigation, and • Cybersecurity threat detection and mitigation. Before these topics are discussed in more detail, a brief discussion of transportation systems management and operation (TSMO) is presented, because this concept ties together the use cases previously listed, and the concept of employing AI/ML techniques in this space.

1.1.2 Introduction to Transportation Systems Management and Operation The US Department of Transportation (DOT) Organizing and Planning for Operations Program describes TSMO as a set of plans for strategic, programmatic, and tactical operation and maintenance of existing infrastructure through multimodal and multiagency programs and projects (FHWA 2019b). The operation and maintenance of modern transportation systems relies on the integration of significant amounts of data that are provided by a variety of sources at different timescales and at a rate that surpasses the abilities of individual human operators or centralized supervisory DSS to effectively process in realor near-real time. Traditionally, these data are gathered to a centralized Traffic Management Center (TMC) where it is displayed on numerous screens in a variety of ways for the consumption of human operators, as shown in Figure 1-2. Some

Figure 1-2.  Illustrative data flow for traffic incident management. Source: https://ops.fhwa.dot.gov/publications/fhwahop19004/ch2.htm.

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pre- or post-processing occurs on some of the data feeds, but much of these are simply passed directly to the screens, placing the burden of assimilation and interpretation on human beings, who really have the ability to focus on only one thing at a time and may have trouble identifying important patterns or events in the data feeds until these patterns or events have become major incidents. The introduction of CV/AV systems will compound this issue with the introduction of devices that have the ability to exchange large amounts of data over relatively short time frames. The fundamental operation of the transportation system will need to shift from a model in which the intelligence is centralized within the TMC to a model in which some amount of intelligence is pushed out to the periphery (edge) of the system, in which devices themselves are enabled to make certain decisions without checking with a centralized system, thus greatly reducing latency as well as data transfer requirements across the system and reducing the computational burden on the TMC operators. AI/ML tools and techniques, then, are applicable in this system at multiple levels: at the edge in the devices themselves and within the backhaul system, which includes the TMC. The benefit of this multitiered system is that edge devices can make decisions more rapidly, reducing latency between sensing and action, which is a primary source of oscillation in complex systems (Bratsun et al. 2005, Stépán 1999), and the central core of the system can focus on processing only the data of significance emanating from the edge devices. Meaning, the central core does not need, nor does it want, to have all data flow back to it; rather, only situationally relevant data such as periodic status updates, specific events, and anomalies should be transmitted back to the core. Once data are in the core, they can be further processed and analyzed, and if appropriate, they then become a digital asset for the edge devices. In the following use cases, a number of specific applications will be presented and discussed within the framework of applying AI/ML techniques.

1.1.3  Use Cases for Artificial Intelligence and Machine Learning in Transportation Current intelligent transportation system technologies are able to provide large amounts of data to traffic management centers; however, currently, these data are often discarded or are used only to provide information to a human operator, who ultimately performs the activity of data assimilation and interpretation. As impressive as the human brain is, it is physically unable to process the amount of data being presented from these systems, and the effect is that the vast majority of data flowing into a TMC are not utilized. Advanced AI/ML-enabled transportation systems will thrive on this type of extensive data capture, and their ability to process these data will be limited only by the efficacy of the technique(s) applied and computational efficiency. With the introduction of CVs and AVs into the transportation system, the amount and frequency of data will only increase; and as connectivity increases in a system, like the transportation system, the potential for catastrophic failure also increases, and so does the need for a nearreal-time management system (Carlson and Doyle 2002, NARCSG 2004).

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This section examines a few application use cases in which the deployment of AI/ML techniques can improve traffic operations, road maintenance, asset management, and public safety.

1.1.3.1  Traffic Control The efficient movement of people and goods within urban environments has always been a challenge, but with the introduction of automobiles, this challenge took on new dimensions, requiring new ideas for everything, from the layout of streets to pavement technologies to movement control technologies at intersecting paths. As the number of vehicles increased, so did their interactions, and as they became heavier and faster, and collisions were more difficult to avoid and more significant in their impact. Controlling the movement of vehicles through these intersections quickly became a necessity and could initially be accomplished with the help of a human being and with arm gestures. Visual traffic control mechanism eventually replaced the human being, becoming what we recognize as a traffic signal. Entire industries are now devoted to the development, programming, installation, monitoring, and maintenance of traffic control devices. Some solutions utilize a static program in which the signal phase and timing (SPaT) is divided into regular slices of time, and opposing directions of travel alternate between green and red cycles. Other designs incorporate a more dynamic interaction with the environment, using additional sensors such as pedestrianinterrupt call buttons, cameras, radars, or inductive loops embedded within the roadway at the intersection, all of which serve to detect the presence of road users. In addition, the signal controllers can be connected back to a TMC or another supervisory system for the purpose of monitoring the performance of the signal and for sending command updates such as modifications to the SPaT plan. Figure 1-3 illustrates the concept of connected vehicles utilizing SPaT. With increasing density, the frequency of intersecting roadways increases to a point where the flow of vehicles through one intersection affects the flow of vehicles through adjacent intersections. In the extreme case of this, we can arrive at a scenario where a traffic system becomes locked, meaning individual vehicles move very slowly or not at all, although the signalized intersections are progressing through their SPaT cycles appropriately. Situational awareness at the level of the individual intersection becomes insufficient for vehicle movement requirements and, therefore, must be expanded to include additional intersections as component nodes in a system. This is typically achieved via connecting all intersections to a central authority where the intelligence lies in an attempt to coordinate the signals to improve vehicle movement across the system. This effort is often referred to as adaptive signal timing, and although its application taken in context of only the intersections of interest would seem trivial, the fact is that these intersections are connected to others that may not be inside the group of signals that are cooperating. So, optimizing a group of signals may actually drive significant inefficiencies in the broader system.

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Figure 1-3.  Illustration of connected vehicles utilizing SPaT. Source: https://www.its.dot.gov/resources/fastfacts.htm.

This model has been deployed in a number of ways with a variety of results, and its success at managing traffic flow and preventing or minimizing long vehicle queues depends on the ability of the central system to aggregate all the incoming data from the intersections, predict the effect of various changes to signal timing, select the optimal solution, and deploy the changes back to the devices. Between the time that data are generated at the intersection and a new signal timing program is initialized from the TMC, the vehicles have moved, potentially changing the conditions at the intersections. This latency between sensing and action is a very common problem for complex systems and is the primary cause of temporal oscillation (Bratsun et al. 2005). Some of this latency can be overcome by fast data transfer speeds and fast computing models, but the benefits of this approach would not necessarily scale to larger systems. In a decentralized approach, the nodes (intersections) negotiate directly with their neighbors, enabling them to exchange information directly regarding local conditions, and making changes to signal timing with lower overall latency, but without the benefit of more global knowledge such as how those changes may affect the broader traffic control system. If negotiation can take place at the edge (the traffic signal device), then only state information regarding the current SPaT cycle for each intersection need to be shared with a central TMC, which can occur at a much lower frequency (1 Hz or less). These data are provided as a status update rather than as part of a sense-plan-act cycle. Shifting the decision-making

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capability from a centralized system to the devices at the periphery enables the system as a whole to adapt more quickly to changing conditions and to optimize performance; however, to prevent each intersection (system node) from attempting to simply optimize traffic flow for its locality, which inevitably leads to suboptimal system-level behavior, the nodes must also have an awareness of the goals of the broader system, which may be implicit within the design of the behavior rules for the nodes or may be explicit instruction. This is the crux of system of systems engineering, also known as multiagent systems engineering, and requires two different paradigms. If the nodes are globally aware of the system’s target behavior or performance, provided by the central system or TMC, then their behaviors might include negotiation with a wide set of other nodes to ensure that their local behavior is consistent with the local behavior of others for the emergence of the desired system-level behavior. Alternatively, if each node is unaware of global target performance, then the design of their behavior rules will need to have had this in mind during initial development. Thus, each node behaves according to its own rules, unaware of how its local interactions manifest as system-level results. The first paradigm somewhat recreates the centralized intelligence model, as the intended actions of each node must first be cross-checked against the likely system-level outcome before action can be taken; however, the intelligence is still at the node level. The second paradigm, however, is a fully decentralized system, governed by the temporal interactions of the nodes and the behavior rules by which they respond. Systems are also possible using hybrid approaches, and of course, the TMC has a role to play in each. The application of AI/ML techniques here is useful for avoiding the necessity of explicitly stating the required behaviors for each node under each circumstance, which inevitably leads to edge cases, or situations that had not been a priori specified. A potential AI/ML solution here might start with deep learning techniques to automatically detect objects such as stop signs and traffic lights and identify and classify the behavior of nodes (traffic signals) and the corresponding behavior of vehicles (flow or other metrics). Then, once these patterns, as well as the relationships between system components, can be identified, a reinforcement learning system can be developed to enable the nodes to cooperate such that the emergent system behavior is optimized, versus any single intersection. In addition, pedestrians and/or bicyclists are cane-detected using the deep learning techniques at intersections to help decrease accidents. In the implementation of reinforcement learning, a reward function must be identified. This gives the algorithm a framework for its behavior, and essentially it will observe the state or condition of the system, including intersections (SPaT) and vehicles (flow, average speed, travel times, etc.) and make a change to one part of the system. For this example, let us consider just the intersection nodes and then observe the result. To the extent that the actual results do not match the desired results, the system realizes a negative reward. As its goal is to obtain positive rewards, the system makes another adjustment and observes the result once again, and repeats this iteratively, each time learning through reinforcement how each change pushes the system closer to, or farther away from, the desired results.

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Reinforcement learning is often used in systems that must adapt to conditions that cannot be known a priori and where it would be unreasonable to try to explicitly describe each potential state for each node. This is a primary factor for selecting this approach, versus, for example, supervised learning in which the answer is already known and you are just trying to train a system to recognize a known pattern. However, in this case, it may be desirable to overlay another learning or supervisory system on top of the RL system. Otherwise, the system may continue to learn in a direction that is counter to other desirable system behaviors or that negatively impacts other operations. So, a supervisory system that takes in the behavior of the RL system as an input would probably be needed to ensure that its performance is consistent with broader system goals.

1.1.3.2  Decentralized Congestion Mitigation The technologies and capabilities of CV/AV systems will fundamentally change how the transportation system operates, including the requirements placed on the system for data aggregation and management, and for real- or near-real-time operation. A centralized intelligence and management model does not scale well as we start to add thousands of devices, each of which has the ability to produce and consume large amounts of data. With automated vehicles, the human driver will no longer be the primary target of information meant to inform or modify. In the lower levels of automation (Levels 1 to 3) as defined by SAE (SAE International 2016), the driver is still considered an active participant and is expected to take over operation of the vehicle in certain circumstances. However, at higher levels of automation (Levels 4 and 5), the driver is the system itself, and any human occupants are passengers with little or no ability to control the behavior of the vehicle. This transition will drive additional structural changes in the information availability and management of the entire system. Similarly, connected vehicles have the ability to communicate with one another as well as infrastructure devices and have high-fidelity localized knowledge along with lower-fidelity global knowledge. The TMC then becomes akin to a central nervous system, absorbing information from many sources, but acting only on information that requires a global response, which requires that the inflow of information be consumed in a relevant time frame. For example, by distributing intelligence to the periphery of the system, as discussed previously, the devices need only share low-fidelity data with the TMC, reducing the amount of data needing to be processed centrally, while still providing a sufficient snapshot of system behavior that if something of interest occurs in the system, the TMC can detect this event or anomaly. At this point, if needed, a central process or algorithm can request higher-fidelity data from the relevant devices. Continuing with the use case of traffic control and signalized intersections, intersections can begin by sensing the vehicles and pedestrians in their environment and modifying their timing plans to improve traffic flow through the intersection. Next, they can communicate with a central office, which can combine its state information with that of others in the area to provide coordinating timing plans. The modified timing plans are then sent back down

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to the device or node level, and the intersections can communicate with one another directly to further negotiate or refine timing plans based on rapidly changing localized conditions. The results of this device-level negotiation can then be provided as a status update back to the TMC, which becomes its next input in its own sense-plan-act cycle. Each of these steps demonstrates increasing complexity of the system and illustrates how the data management needs change and how distributing the intelligence of the system is a critical part of the solution. Adding connected vehicles to this mix, there is the ability for vehicles and infrastructure to negotiate directly and for multiple learning and supervisory systems to be overlaid. An example of this is the applications of priority and pre-emption in which a vehicle requests priority through the intersection (a green phase more quickly than the current SPaT plan based on the lane of approach) or requests the SPaT cycle to immediately provide a green phase (pre-emption). This negotiation must be managed at the device level because of latency requirements and can be executed either by passing the request to a central TMC for approval or by utilizing built-in behavior rules that are consistent with global performance targets. Taking this process one step further, we find that the infrastructure is not even needed if all vehicles are properly equipped for this dynamic negotiation and can simply negotiate with one another for passing through an intersection. This scenario addresses congestion within an urban environment with intersecting roadways, but another potent example of decentralized congestion management is on highways. Congestion is literally an emergent property of our traffic systems and is largely caused by variations in human-controlled vehicle behavior. Eliminating or minimizing this variability through coordination among vehicles and tighter vehicle control using an automated driving system eliminate or significantly reduce congestion, as illustrated in Figure 1-4.

Figure 1-4.  USDOT connected vehicle speed harmonization. Source: https://www.its.dot.gov/communications/media/11environment.htm.

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Through modeling and simulation, we can see that when just 10% to 20% of vehicles coordinate their actions, for example in slowing traffic, the behavior of the whole system can be modified. Prior to this scenario becoming a reality, however, vehicle platooning was also a mechanism in which individual (connected and automated) vehicles could coordinate their speeds and headway in a way that reduced congestion. The central core or TMC has a role to play in these scenarios by setting some of the rules by which platoons may operate, such as the maximum and minimum speeds that the platoon is allowed to set, the maximum number of vehicles allowed in the platoon based on vehicle type, and whether platooning is even allowed.

1.1.3.3  Smart Work Zone Management Work zones are a common sight for modern transportation networks and are a key tenant of TSMO because of their critical role in affecting the flow of traffic. Smart work zones are a concept by which sensors are placed in the work zone that, for example, provide traffic counts and speeds, and through which a back-office system can communicate with drivers via portable dynamic message signs. Again, this system is a fit case for the application of AI/ML, which can learn over time what type of messages affect traffic behavior and how the placement of PDMSs improves or detracts this behavior. A supervisory system overlaid on an RL system can then be alerted when traffic behavior is changing, and using previously learned responses, respond through sign message and placement changes more rapidly than human operators monitoring the data feeds. Incident management is similar to work zones in that one or more lanes of traffic are blocked, and a rapid and coordinated response is needed across multiple agencies to quickly clear the incident to allow traffic to return to free-flow conditions. Connected vehicles within this scenario can communicate rapidly and directly with ITS devices, and at sufficient market penetration, can communicate directly with one another to efficiently transit through an area where capacity is otherwise limited because of a lane closure or crash, or because of unusually heavy traffic on account of an event. The TMC does not need to be informed of the messaging passing among vehicles, but it may be interested in a subsampling of these messages to determine overall system performance. With connectivity and automation, however, comes the threat of cyberattacks, which has not been a serious concern for transportation systems so far and which is discussed in more detail subsequently. Work zones often have lowered speed limits, narrower lanes, shifted lane geometries, and temporary lane closures, which in and of themselves will affect the capacity of that section of roadway, but these changes in the roadway parameters can also cause incidents (secondary crashes) as the behaviors of drivers change, and particularly at the end of queue, the point where vehicles have slowed to a speed that is some fraction of the free-flow speed. Other incidents that are not related to work zones are minor and major crashes, disabled vehicles, debris, and nonemergency events like sporting events and concerts (FHWA 2019a). To address these issues, the areas of work zone management and traffic incident

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management (TIM) have been developed, and these are a critical component to an overall TSMO philosophy. The area of intelligent transportation systems (ITS) has also developed along these lines of providing situational awareness for the highway system, and ITS devices often include sensors like radars and cameras for counting the number and size of passing vehicles, as well as determining their speed. ITS systems also typically include dynamic message signs (DMS) to communicate various messages to oncoming drivers using simple text messages. ITS devices can be permanently mounted or portable for temporary placement, and these provide the backbone of work zone management (WZM) and TIM operations for most TMCs (FHWA 2014). So, similar to the traffic control operation previously described, this system also contains a number of nodes (ITS devices) that have the ability to sense their environment and a rudimentary ability to actively communicate with vehicles via visual indicators that may or may not be received and understood by the human drivers of the vehicles. When there is a work zone, particularly a lane closure, or an incident that is, or has the ability to affect traffic flow, DMS and portable DMS signs will be engaged to post text indicating something such as “Slow (or Stopped) Traffic Ahead,” “Accident Ahead,” “Alternate Route Advised,” and so on. These messages are an attempt to divert traffic before it becomes part of a traffic jam (vehicle queue) or get people to start slowing down so they do not find themselves in a situation where they are approaching slow or stopped vehicles at a high rate of speed, which is the cause of many “secondary crashes,” and can prove fatal. The rules that govern the behavior of these signs are largely controlled by the TMC, but some intelligence has been afforded to the devices themselves in the presentation of messages related to, for example, travel times. The rules governing how far away drivers should be informed depends on many factors, many of which are dynamic in nature, and so depend on real- or near-real-time data. Again, we find that issues arise if the delay from sensing to action is above a critical threshold. For example, if a sign says “Stop, Traffic Ahead” when you are already stopped, or if there is the same sign when there is no traffic because the vehicle queue has already cleared out, confusion is bound to occur in the minds of vehicle users. A variety of efforts are underway across the country to integrate the sensor data from ITS devices, and other data from sources such as INRIX, HERE, and Waze, to provide more rapid response time to roadway incidents as well as evaluate the effectiveness of TSMO policies and procedures. The availability, quality, and density of data within the system, along with the intelligence of management systems, drives the capabilities of TSMO, and the systems in place today are starting to provide the necessary mechanisms for intelligently managing work zones and incidents. However, there is still sufficient latency in the system to cause oscillations in information relevancy for vehicles on the roadway, and there is very little intelligence located locally at the devices. Therefore, the devices themselves are not able to coordinate their actions without the direction of a centralized TMC system.

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1.1.3.4  Wrong-Way Driver Detection and Mitigation Several potential implementations are available for a CV wrong-way driver warning system (Kockelman et  al. 2016a, b). One implementation utilizes an external wrong-way detection sensor as an input to trigger when a vehicle is detected traveling the wrong direction in a specific area. A process on a roadside equipment (RSE) that receives the wrong-way detection generates and broadcasts a warning message to all vehicles nearby. A second implementation utilizes a process on an RSE to monitor the basic safety messages broadcast from vehicles to identify vehicles traveling in the opposite direction of the defined roadway network. This requires that the roadway network, including intended direction of travel, be defined in the system and that the definition is accessible to the process on the RSE. When a vehicle is detected traveling against the defined flow of traffic, a warning is broadcast to all vehicles nearby with information about the vehicle, specifically its speed, heading, and location. In both cases, vehicles receiving the message analyze the content to determine if it is applicable to them; that is, if they are approaching the vehicle that was detected, in which case a message is displayed to the driver indicating that they should exercise caution ahead and be aware of the vehicle traveling in the wrong direction. In addition, if there are law enforcement vehicles within the range of the RSE, they will be notified regardless of their direction of travel.

1.1.3.5  Cybersecurity Threat Detection and Mitigation Vehicles and other devices will need on-board mechanisms for detecting and mitigating malicious intrusions from a variety of threat vectors; however, the central TMC will also need the ability to detect and mitigate threats at a systemwide level, and so robust anomaly detection will need to be developed to discern the difference between normal and compromised system behavior, however subtle. NHTSA is leading the efforts to develop a comprehensive security credential management system for CVs (NHTSA 2019), and a recognized critical component of this architecture is the detection of anomalous behavior at a system level. Within the ITS domain, this is known as global misbehavior detection, and is a challenging problem because the global dynamics of a system comprising numerous interacting individuals is an emergent phenomenon. Increasing connectivity among vehicles in urban traffic systems provides opportunity for beneficial impacts such as congestion reduction; however, it also creates security risks with the potential for targeted disruption. Security algorithms, protocols, and procedures must take into account the unique aspects of vehicle and highway systems. Security for a CV environment must go beyond message authentication to consider the broader issue of message trust, which is particularly important if the message can trigger a safety-critical response, potentially creating a risk to drivers and passengers. Numerous scenarios are thrown up, in which false information inserted into a CV system may cause widespread system disruption.

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The advantage of this system is that it enables vehicles to trust one another regardless of whether they are near an RSE. The most difficult problem with the PKI approach is the distribution, management, and revocation of security certificates. In theory, a certificate will be revoked if it is used to “spoof” another device’s identity or to send incorrect data caused either by equipment malfunction or by a deliberate act. Periodically, the Certificate Authority will issue a certificate revocation list that enables vehicles to detect when a message comes from a bad actor. Even with this brief description, it is clear that there are substantial technical problems in designing a PKI system for 250 million privately owned and operated vehicles with no central registration, licensing, or administrative authority. Further complicating the system, vehicles may be registered in one locale while connecting to ITS infrastructure in a different region, state, or country. Privacy concerns also require message anonymity to deter tracking and monitoring of individual vehicles. Multiple methods have been proposed for detecting bad actors, ranging from onboard hardware checks to global sampling of reports from multiple vehicles. For example, the OBE can compare ECU component identification to detect when it is installed in a different vehicle. At a local level, the OBE can check its sensor data with incoming messages to check for consistency and plausibility. At a global level, the infrastructure can randomly collect messages from vehicles to determine if multiple messages contain certificates issued to the same OBE. Regardless of the mechanisms deployed, misuse and intrusion detection is critical to the success of CVs and to the effective operation of the transportation system. The application of AI/ML techniques is particularly critical in this area because of the nature of cybersecurity in which the very watchdog systems that have been designed to detect malicious activity can themselves become compromised. Thus, layer on layer of supervisory and learning systems must be employed to detect anomalous behavior, which may be extremely subtle, and must not only be the “watchers” but the “watchers of the watchers.”

1.1.4  Conclusions of Section 1.1 Transportation systems are complex systems of interacting devices and subsystems on which we all rely. The application of AI and ML tools and techniques has tremendous potential to disrupt the way these systems are managed and operated; however, care must be taken in the selection and application of AI/ML techniques because of the tendency of specific techniques to become overtrained or to settle on suboptimal solutions. A layered approach will probably be needed in most applications to ensure that the system is performing within the context of the broader system and to ensure that human operators understand the behavior and decisions of these AI systems. Transportation systems also follow a predictable evolution as complexity increases, and a part of that evolution is that the intelligence of the system shifts from centralized to decentralized along a continuum. For TSMO and

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the operations of TMCs, work zone management, and TIM, this shift is already underway as devices become more capable of sensing their environments and processing the data locally to make more rapid decisions for effecting change, and this process will continue as more devices become available, especially as CAVs begin to appear, transforming fundamental relationships between vehicle and infrastructure. The deployment of CV technology has the potential to provide a number of benefits to individuals and society as a whole. However, without a careful consideration of deployment strategies, including data management, cybersecurity, maintenance processes and costs, and usage demand and patterns, these benefits will not be realized. CV functionality will need to be integrated into the traffic management systems to support the various infrastructure-based CV applications that are and will be available, as well as to provide a valuable source of real-time vehicle probe data to DOTs from CV users within the range of installed RSEs. A phased approach to the deployment of CV technology on Texas roadways is recommended, which will enable DOTs to minimize the risk and cost of implementation, while also following trends in vehicle-based CV technology adoption. Research and development projects should also continue to be aggressively pursued by DOTs to understand the core and emerging AI/ML tools and techniques that can be applied for the benefit of all.

1.2 EDGE COMPUTING, FOG COMPUTING, AND CLOUD COMPUTING TECHNOLOGIES Cloud Computing is referred to an on-demand computing architecture that is used to deliver computing services over the Internet, where all the required computing resources, data applications, and storage are all hosted on the infrastructure that is remote to users. This enables the computing resources to be available to multiple users simultaneously. Edge Computing is a developing computing architecture that is promising to work together with Cloud Computing by trying to balance the demand of computing intelligence, network resources, and storage crossing the network (Shi et al. 2016). Edge devices offload some of the processing that would normally be done within a centralized architecture. “Fog Computing” is a type of computing architecture developed based on the “Edge Computing” concept. The existing transportation infrastructures have adopted the Cloud Computing solution as an alternative to dedicate ITS communication frameworks. There is also an emerging demand in incorporating the Edge Computing technology into the transportation community, especially because of the recent development of connected and automated vehicle (CAV) technology. It is expected that future full deployment of CAV technology will generate large amounts of data and challenge the data processing capability of existing ITS infrastructure.

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This section will introduce the Edge Computing technology from the following aspects: • Review of the demand on existing transportation infrastructure, • Overview of Edge Computing technology, • Identification of transportation application scenarios that the Edge Computing technology can support, and • Impact of Edge Computing on CAV-based roadside infrastructure migration.

1.2.1  The Demand on the Existing Transportation Infrastructure Figure 1-5 shows the typical framework of the traditional ITS infrastructure, which was built as a centralized system, and exhibits common features such as the following: • Dedicated communication systems; • Centralized data collection, data processing, and data storage; • Normally, it is a client/server-based centralized software architecture; • Highly customized, vendor-specific hardware and software systems; • High cost of deployment and limited coverage, and • Lack of intersystem compatibility and data exchanging capability.

Figure 1-5.  Traditional ITS TMC solution diagram.

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In terms of data processing, the centralized ITS framework traditionally requires that every piece of data needs to be collected from the field and routed back to the Traffic Operation Center (TOC) or Transportation Management Center (TMC). The TOC or TMC holds the necessary computing power. This would also require that the centralized ITS solution desires well-built communications systems to handle the center-to-field (C2F) communications and powerful computer server clusters at the TOC to satisfy the data processing demands.

1.2.2  Cloud Computing as an Alternative Solution In recent years, cloud-based solutions have been used as an alternative option to support the traditional ITS infrastructure. One of the primary benefits of cloud technology is its flexibility and cost-effectiveness because of its service-oriented architecture (SOA) (Botta et al. 2015, Anand et al. 2015). The major advantages of Cloud Computing include the following: • Highly scalable distributed environment that enables easy system configuration, • High reliability with lower maintenance cost, • Easy application deployment, and • Flexibility of expansion and migration. Examples of ITS deployment with the aid of cloud computing technology include the following: • Camera monitoring systems, • Travel time monitoring system, • Traffic signal control system, • Asset management system, and • Work zone management systems.

1.2.3  Demand of Edge Computing Limitations still exist for usage of the cloud solutions to ITS, mainly because most current cloud systems still operate in a centralized architecture, where any piece of data needs to be uploaded to the cloud before any manipulation can occur. Primally, the current cloud systems are featured with the following characteristics: Most cloud systems now rely on public internet to deliver their services, which might be bogged down during critical times such as heavy congestion peaks, emergency, or disaster events. Most computing power of cloud systems is still resident-centralized with the deployment of remote sensors on a large scale. It demands all the collected data to be uploaded to the cloud because of the lack of remote computing power. This will create two major issues (Shi et al. 2019): The communications bandwidth will demand a large amount of data transmission and The latency of the system response time when demanding the cloud side data processing results.

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In particular, new development of CAV technologies demands connected vehicle data to be relayed from roadside units (RSUs) to remote centers. Currently, most CAV systems are still at their predeployment or testing phases. Therefore, their existing system configurations might address only small-scale connected vehicle deployments with limited applications. With the increasing deployment of CAVs on the road, the roadside infrastructure may become congested shortly with high penetration rate of CAV vehicle deployment. However, the current computing power residing at the roadside cabinets is very limited. Conceptualized to solve this challenge, the idea of Edge Computing started to emerge in 2014 (Shi et al. 2019, Ai et al. 2018), which emphasized on establishing new computing architecture in balancing the demands of the computing power, network routing, storage distribution, as well as algorithms residence and privacy/ security across the entire network between the Cloud and the Edge sides. This reduces the network latency and the demand of data transmission bandwidth.

1.2.4  Overview of Edge Computing Technologies The term Edge refers to the remote side of Cloud. As massive amounts of data are generated from the field, transferring huge amounts of data to the central system is a challenge. This is where the Edge Computing concept comes into play. Shi et al. (2016) defined edge computing as follows: Edge computing refers to the enabling technologies allowing computation to be performed at the edge of the network, on downstream data on behalf of cloud services and upstream data on behalf of IoT services. “Edge” is defined as any computing and network resources along the path between data sources and cloud data centers, and edge is a continuum. Edge Computing is intended to enhance the capability of the Edge devices by moving the computing intelligence and data handling capability closer to the Edge side instead of centralizing it in the cloud. Compared with the traditional centralized/cloud computing, Edge Computing reduces the demand of data transmission to the cloud while reducing latency. Furthermore, ML and AI algorithms may be built into edge nodes for carrying out data mining and analysis functions before reaching the cloud (Botta et al. 2015, Shi et al. 2019, Soni 2018, O’Donnell 2018). Of the Edge Computing frameworks that have been developed worldwide, three mainstream Edge Computing solutions have been widely recognized. • Cloudlet, • Mobile Edge Computing (MEC), and • Fog Computing.

1.2.5 Cloudlet The Cloudlet framework is intended to address the end-to-end latency issues within the cloud computing environments through internet (Shi et al. 2019, Ai et al. 2018). It is proposed as

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a mobility-enhanced small-scale cloud Data Center (DC) that is located at the edge of the internet…. Where User Equipment’s (UEs) can access the computing resources in the nearby cloudlet through a one-hop highspeed wireless local area network. (Ai et al. 2018) Cloudlet can support any low-latency mobile-based applications such as edge-based real-time internet data analysis (Shi et  al. 2019, Ai et  al. 2018). Corresponding experimental results show that the cloudlet solution can improve the end-to-end latency by 51% in a mobile device, in comparison with a pure cloud system (Ai et al. 2018).

1.2.6  Mobile Edge Computing The MEC framework leverages Edge Severs that are located between the cloud and the users’ edge devices to ensure the networking quality of service in improving bandwidth usage and reducing transmission latency (Shi et al. 2019, Ai et al. 2018). MEC holds technical promise in many aspects, for example, • Low networking delays, • High bandwidth, • Location awareness, and • Proximity. MEC standards have been developed and promoted by European Telecommunications Standards Institute (ETSI) since 2017 (Shi et  al. 2019, Ai et al. 2018). These can work with various communication solutions, from 3G through the forthcoming 5th- Generation (5G) wireless technologies. It is expected that MEC could be widely used in many mobile applications (e.g., live video streaming), augmented reality (AR) mobile, and smart city applications (e.g., smart grids, vehicular networking, and traffic management). It has been considered as one of the key frameworks to enable the IoTs applications (Ai et al. 2018).

1.2.7  “Fog” Computing “Fog Computing” was initialized by Cisco and was founded by the OpenFog Consortium as a system-level horizontal architecture that distributes resources and services of computing, storage, control and networking anywhere along the continuum from the cloud to things. (Ai et al. 2018) Compared with other types of Edge Computing architecture in which the attention is focused on shifting the computing power close to the Edge side, Fog Computing is intended to propose the distributed architecture in which the computing intelligence, storage, and network resources can be placed on the edge devices and the cloud, as well as the crossing networking places in between (Shi et al. 2019, Ai et al. 2018, Soni 2018, Linthicum 2018). Figure 1-6 shows the Edge Computing architecture.

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Figure 1-6.  Concept of edge computing architecture.

Under the Edge Computing architecture, multiple layers exist; each access node might play a different functional role (Figure 1-6). The nodes within proximity to the Edge might focus on sensor data collection, data normalization, and sensor control (Ai et al. 2018), whereas the nodes on the top layers might concentrate on data filtering, aggregation, and transformation (Botta et al. 2015, Ai et al. 2018, Giang et al. 2016, Stojmenovic and Wen 2014). At each node point, the node element can play different networking roles such as gateways, edge devices, network router, and so on. In this way, the large amount of data collected do not need to be fully transmitted to the cloud side for centralized processing. The initial processing can be conducted at the “Edge” side, and only the necessary data will be shipped back to the cloud. This will eliminate the time and distance of data sending and, therefore, reduce the demand of the communication bandwidth and system responding latencies as well (Linthicum 2018). Furthermore, ML and AI algorithms can also be built into fog nodes for carrying out data mining and analysis functions before reaching the cloud (Botta et al. 2015, Ai et al. 2018). This will increase the efficiency in the fast data process to support close-to-edge decision-making. In addition, fog computing is an open architecture to enable more interoperability in supporting emerging 5G, the IoTs, and AI innovations (Tsai 2018, Tchernitski 2018, Laa et al. 2019). Table 1-1 summarizes the major features of these three Edge Computing solutions.

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Table 1-1.  Comparison of Edge Computing Solutions. MEC Goals

Cloudlets

Fog computing

Open radio access; edge computing

Enables both Hi-performance; computeinteroperability intensive and and security latency-sensitive computing Network Three-tier Three-tier Three or more architecture hierarchy radio hierarchy; mobile tiers; fog nodes access device— located away networks Cloudlet—Cloud from cloud toward edge toward edge Access media Mobile network Wi-Fi Mobile network; Wi-Fi, Bluetooth Common features Extension of cloud; decentralized computing; geodistributed; wireless access; low latency; outdoor usage; end device mobility support; context awareness of the applications Node devices MEC servers (base Data center in a Routers/switches/ station) box access points/ gateways Software Mobile Cloudlet agent Fog abstraction architecture orchestrator layer Internode Partial Partial Supported communication User cases Security industry; Health/security IoTs; smart city; content sectors; IoTs connected delivery vehicle; wireless industry; IoTs sensors network Supported ETSI MEC Open edge Open fog organization computing consortium

1.2.8 Development of Edge Computing and Associated Technologies As a new computing framework, the Edge Computing technology has been rapidly growing, as shown in Figure 1-7 (Shi et al. 2019).

1.2.8.1  Edge Computing and Cloud Computing Edge Computing is not a replacement of Cloud Computing; instead, they shall be cooperatively integrated at the function level (Shi et al. 2019). The well-designed

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Figure 1-7.  Development of edge computing technologies. Source: Shi et al. (2019).

hybrid system is supposed to improve the traditional cloud system performance through the following capabilities: • Capability of processing the necessary data close to the data collection points, which can reduce the pressure of data transmission for a traditional cloudbased system. • Capability of building intelligent algorithms close to the data collection points as well, which can further improve the system responding time and decision-making intelligence. On the other hand the cloud system will still provide necessary backend data storage and processing support, but also focus on higher-level data aggregation and data mining. Such a hybrid solution is expected to reduce network congestion and speed up system operations and consequently reduce the associated costs (O’Donnell 2018).

1.2.8.2  Edge Computing and Internet of Things As one of the major application domains that naturally push the demand of Edge Computing, the Internet of Things (IoTs) is built on low-cost, low-energy, and largescale edge sensors and devices, which will generate a large amount of raw data. Edge Computing can assist the IoTs applications in dealing with raw data processing at a remote place and transferring only necessary data back to the IoTs backend cloud.

1.2.8.3  Edge Computing and 5G Edge Computing desires reliable and high-speed communications between the Cloud side and the “Edge” side in support of different case/application scenarios using 5G technology, such as the following: • Enhanced mobile broadband (eMBB) will support high-bandwidth/ throughput data transmission as the backbone connections of network nodes between Edge and Cloud.

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Figure 1-8.  Edge Computing along with other innovation technologies. Source: Shi et al. (2019).

• Massive machine-type communications (mMTCs) support massive machineto-machine communications to promote high-density industrial demands of the IoTs and work together with the IoTs technologies to optimize data processing capability crossing the network. • Ultrareliable and low-latency communications (uRLLC) will support ultrareliable and low-latency communications to meet the needs of emergency and sensitive services. Edge Computing can enhance CAV data processing capability in conjunction with uRLLC service. Under the 5G wireless technology standard, Edge Computing is recognized as one of the important technologies under the 5G application scenarios (Sabella et al. 2017). Figure 1-8 illustrates the relationship of Edge Computing with Cloud Computing, the IoTs, and 5G (Shi et al. 2019).

1.2.9  Transportation Scenarios of Applying Edge Computing The Edge Computing technology can provide supportive solutions to transportation applications with the following promises: Edge Computing can provide scalable and flexible system deployment solutions that are needed from transportation applications. Edge Computing can provide pure distributed network solutions that further lower the bandwidth demand and the low-latency requirements of mission-critical transportation applications. Along with expanding on the existing transportation infrastructure, a high demand exists in the system architecture, in which the existing centralized ITS architecture shall be gradually migrated toward the distributed systems (Botta et al. 2015, Stojmenovic and Wen 2014, Rose 2018). The Edge Computing

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Disruptive Emerging Transportation Technologies

technology is expected to contribute more at least in the following transportation application domains: • Building decentralized ITS infrastructure with Edge Computing through enhancing the computing intelligence of the remote Edge side of the existing ITS systems. • Supporting the emerging infrastructure need for adapting CAVs.

1.2.10  Building Decentralized ITS Infrastructure Besides the benefit of data processing brought up by Edge Computing, an additional bonus that Edge Computing architecture can offer is to build more intelligence at the edge side, which is termed Edge Intelligence (Zhou et al. 2019). The Edge Computing architecture can embed advance algorithms with each edge device crossing the network. The fusion of AI and ML technologies embedded within the algorithms will further promote the intelligent decision-making capability at the system level (O’Donnell 2018, Zhou et  al. 2019). In addition, such a single edge note intelligence enhancement can be further assembled into the higher level cooperative intelligent decision-making process. This concept is illustrated in Figure 1-9. When incorporating such concepts into traditional ITS infrastructure, several centralized decision-making processing can be easily migrated to Edgebased decision-making, particularly with safety-related applications or missioncritical applications in the field. Already several types of applications have been in use in recent years, including the following: Using Edge Computing along with computer vision analysis technologies in helping capture public safety information (Zhang et al. 2019). Building AI algorithms with live video analysis in supporting near-crash event warning (Dunn 2019).

Reduced amount of shorten path of data offloading

Training on the could

Figure 1-9.  Concept of ML/AI within an edge computing framework.

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25

1.2.11 Impact of Edge Computing on Connected and Automated Vehicle Roadside Infrastructure Migration As previously discussed, the advantage of Edge Computing is to build and enhance the computing intelligence and data manipulation capability close to the Edge devices to be better coordinated with the cloud. Impending expansive data generated by CAVs necessitates the implementation of Edge Computing solutions as part of the roadside infrastructure. If CAV vehicles are considered as an Edge node (Ai et al. 2018), with the aid of V2X communication, it is expected that the RSU could relay on-board data to the remote cloud server more smoothly (Stojmenovic and Wen 2014). In terms of the data generated by CAV vehicles, such a data amount would be huge by the following estimations: • It was estimated that a connected vehicle can generate 25 GB of data every hour, which may be sent to the cloud (Kdespagniqz 2015). • UMTRI Safety Pilot Model Deployment ran roughly 3,000 connected vehicles with 25 RSU installations, which generated more than 70 billion records with a total database size of 5.6 TB data for a period of 23 months (Di 2014). • Today, more sensors are being built for autonomous vehicles (AVs). It was also estimated that a common AV can generate 4 TB data per day (Sabella et al. 2017, Winter 2017). Table 1-2 summarizes the existing computing powers at traffic signal inter­­sections. As can be observed, the existing roadside infrastructures that can support CAV applications are very limited in terms of computing power and storage space. With a higher penetration rate of CAVs on the road, the roadside infrastructure will soon become a bottleneck of the ecosystem in connecting CAV vehicles, CAV applications or services, and remote centers. Edge Computing will provide the opportunity by building edge nodes into existing roadside infrastructure to bring more computing intelligence for the CAV applications. Figure 1-10 shows the exemplary concept of Edge Computing solution of CAV roadside infrastructure, which was inspired by Stuhlfauth (2019) and Liu et al.’s study (2017). The combination of edge node and RSU can help deal with a large amount of CAV data locally. With the assistance of 5G services, the Edge Computing–based CAV roadside infrastructure can build on advanced AI/ML algorithms with the edge nodes. Possible uses range from supported real-time CAV applications to enhanced real-time traffic safety and mobility capabilities (O’Donnell 2018). Because the edge computing infrastructure supports a variety of low-cost data storage and data network routing functions, it can also assist traffic management centers to fulfill various advanced latency-sensitive functions such as assisting safety maneuvers at the intersection and distributing TIM messages with minimum delays.

1.2.12  Summary of Section 1.2 The continuing migration of transportation infrastructure is always open to new technologies. Cloud Computing has been used as an alternative of ITS

iMX6 quad core processor Due core 800 MHz

1 GHz Freescale/ NXP i.MX 6

PowerQUICC 2 Pro 400 MHz

Econolite Coprocessor Siemens RSU

Commsignia ITS-RS4

Cubic/Trafficware commander controller EDI iCITE DA-300/ DA-400

Gridsmart Gs2 processor

PowerQUICC 223 MHz

Econolite cobalt

Dual processor 600 MHz 100 MHz —

MPC 8270 266 MHz

Siemens M60 Linux

CPU

Linux

1 GB RAM

80 GB HDD

1 GD FLASH 4 GB

2 GB DDR3 SDRAM4 GB eMMc Dual micro SD 128–512 MB DRAM FLASH 256 MB

Linux 3.X

—

Linux (Ubuntu)

Linux 3.4.118

Linus/RTOS (V2X)

Linux 2.6.3X

Linux 2.6.39

OS

64 MB DRAM 512 MB FLASH 2 MB SRAM 128 MB DDR2 DRAM 64 MB FLASH 2 MB Cache 1–2 GB DDR3 DRAM

Memory

Table 1-2.  Summary of Existing Computing Power at Traffic Signal Intersections.

100 Mbps

10/100 Base-T

10/100 Ethernet

10/100 MBit ethernet 10/100/1,000 Mbps ethernet

—

10 Base-T ethernet With 5 10/100 TCT/ IP ports 10/100 Base ethernet

Network

Additional cabinet computing unit

Additional cabinet computing unit

Traffic signal controller

Roadside unit (RSU)

Roadside unit (RSU)

Coprocessor

Traffic signal controller

Traffic signal controller

Device type

26 Disruptive Emerging Transportation Technologies

Emerging Technologies Impacting the Future of Transportation

27

Figure 1-10.  Fusing of edge computing and CAV applications. communication solutions. In addition, being an emerging and developing computing architecture, Edge Computing has a lot of potential to foster smart transportation solutions as follows: • Edge Computing can provide scalable and flexible system deployment solutions. • Edge Computing can provide pure distributed network solutions that further reduce the bandwidth demand and low-latency requirements of missioncritical transportation applications. • Edge Computing can bring advanced AI/ML algorithms to further promote the intelligence of the transportation system.

1.3 FIFTH-GENERATION INNOVATIVE COMMUNICATIONS TECHNOLOGY Cellular technologies have a long history of evolution driven by commercial demands and are guided by an industrial standard organization, 3rd Generation Partnership Project (3GPP), via its series standard release (otherwise called Release X). Data services based on cellular networking have been evolving from the earlier limited 2G/3G (2nd-Generation/3rd-Generation) data services (short message service and cellular digital packet data) to the LTE (long-term evolution)-based data services since the 3GPP Release 8 and then improving and migrating along with the new 3GPP standards development to the latest LTE Advanced-Pro (3GPP Release 14) under the 4G (4th Generation) cellular network. Along with the exploration of next-generation wireless technology, an investigation of new use cases suggests new demands for advanced cellular data

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service, which call for further expansion of the current cellular data services to cover the following new domains (Lodolo 2019): • Enhanced mobile broadband, • Critical communications, • Massive machine-type communications, • Network operations, and • Enhancement of vehicle-to-everything. Building on these demands, 5th -Generation wireless technologies (5G) have been evolving since the 3GPP Release 15 in 2017 and are expected to be the first commercial launching in 2020 (Lodolo 2019). This section focuses on the innovation of 5G technologies and its potential impacts on the transportation industry, including the following aspects: • Review of 5G Data Services (via different spectrum and application scenarios); • Impact of 5G Data Services on smart transportation infrastructure enhancement; and • Impact of 5G Data Services on connected and automated vehicle (CAV) migration.

1.3.1  Review of 5G Data Services 5G technology is considered as the next generation of mobile system technology after the current 4G system. 5G is expected to promote a highly reliable, secure, and resilient wireless data service network that can deliver communication services anywhere at any time (Sabella et al. 2017). 5G will cover a wide range of existing licensed/unlicensed frequency spectrum in supporting different bandwidth/capacity demands (Lodolo 2019), such as the following: • Low bands (below 1 GHz) for extended coverage, • Mid bands (1 to 6 GHz) for capacity and cost, and • High bands (above 24 GHz) in the millimeter wave for ultracapacity. Correspondingly, 5G defines three major application scenarios (Lanner 2018, Asrar et al. 2018, ITU-R 2015) in three different key domains: • eMBB will support the high-bandwidth and- throughput data transmission, with peak data rates greater than 10 Gbps. The eMBB service intends to satisfy the growing broadband demands of digital lifestyle applications such as HD video, virtual reality, and augmented reality. • Massive machine-type communications (mMTC) will support massive machine-to-machine communications to promote industrial applications of the IoTs, which allows the deployment of sensors and devices in high density (a device connection density of more than 1 million km2).

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• Ultrareliable and low-latency communications (uRLLC) will support ultrareliable and low-latency communications in matching the needs of emergency and sensitive services with a latency lower than 1 ms. CAVs belong to this category. Table 1-3 summarizes the main features of these application scenarios. According to 3GPP, in general, 5G system implementation will consist of two deployment stages: 5G-NR (or 5G-NSA, 5G nonstand-alone)—The deployment of 5G services is overlapping with the existing 4G LTE cellular infrastructure to achieve 5G’s high performances. 5G-SA (5G stand-alone)—The deployment of a stand-alone 5G cellular network is expected with new 3GPP releases in the near future. In addition, the key wireless technologies on the basis of which the 5G technology is being implemented are summarized as follows (Tsirtsis 2016): • Scalable OFDM base air interface that effectively covers diverse spectra, deployments, and services; • Flexible slot-based framework that helps obtain low latency and ensure forward compatibility; • Advanced channel coding that effectively supports large data blocks and a reliable control channel; Table 1-3.  Main Features of 5G-Supported Application Scenarios. Technology

Key features

Example of key Application performances scenarios

Enhanced mobile broadband (eMBB)

High data rate High capacity Spectral efficiency

Peak data rates faster than 10 Gbps

Mobile broadband, HD video, virtual reality, and augmented reality Massive, machine-type Low cost Device IoTs such as communication Low power connection smart grid, (mMTC) Large-scale density of smart building, coverage more than remote High deployment 1 million metering density km−2 Ultrareliable low-latency Very low latency Latency lower Connected and communications Very high than 1 ms automated (uRLLC) reliability vehicles, Very fast emergency handoffs response High mobility services

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Disruptive Emerging Transportation Technologies

• Massive MIMO that effectively utilizes a large group of antennas to increase coverage and/or capacity; and • (Mobile) millimeter Wave (mmWave) that gains more bandwidth for extreme capacity and data throughput.

1.3.2 Impact of 5G Data Services on Smart Transportation Infrastructure Enhancement Transportation practitioners are advised to closely follow the development of 5G technology because it will be a key enabling technology for the optimization of existing ITS communication infrastructures. Mature 5G deployment can significantly improve communication capacity and further reduce communication delays for many transportation/ITS use cases. In general, all three 5G use case/application scenarios will potentially impact the evolution and enhancement of future smart transportation infrastructure.

1.3.2.1  Enhanced Mobile Broadband Service Impact 5G eMBB intends to support the delivery of a “mobile fiber”–type wireless broadband service to the world. It is built on the existing 4G LTE infrastructure since 3GPP Release 8 and is being continuously improved to provide higher data rates (Lodolo 2019, Mobile World 2019). Apart from assisting the next generation of smartphone data service, the fixed wireless access (FWA) will potentially help enhance the existing communication infrastructure of the transportation system. In the past, ITS system deployment was frequently bogged down with issues such as achieving a balance between the cost and range of communication infrastructure deployment to support critical functions such as traffic data collection and CCTV monitoring. The large deployment of the mmWave 5G network can not only be used as an alternative of the agency-owned fiber optic or broadband wireless network, but also expanded from urban to rural areas. The secured virtual private network (VPN) that builds on large commercial mobile data services has seen speedy development and has been widely used with the existing 4G LTE network for many years, and, therefore, VPN can be easily adopted by the emerging 5G eMBB FWA services. 5G eMBB FWA services have already been tested and widely deployed nationwide by major mobile carriers since 2018 (AT&T 2018, IEEE ComSoc 2018, Segan 2019).

1.3.2.2  Massive Machine-Type Communications Service Impact The IoTs as a bundle of technologies has been widely used in many industrial domains other than transportation. The IoTs is fitted with low-cost and lowenergy sensors to support large-scale data collection and monitoring such as remote metering, smart grid, smart home, and smart logistics. The emerging 5G is promoting the mMTC service in supporting high density, low or high bandwidth, also called low-power wide area (LPWA), and the IoTs

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deployment down the path. The mMTC-type service is expected to be available in 2020 along with the 3GPP release (Mobile World 2019). The IoTs technologies are still at their early stage of adoption within the transportation community. Many application scenarios unfold for building smart transportation infrastructure that can take advantage of the IoTs technologies and support 5G communications in the future, such as the following: asset management with the IoTs sensors, and transportation infrastructure monitoring with the IoTs sensors (Gurvey 2019).

1.3.2.3  Ultrareliable and Low-Latency Communications Service Impact The 5G uRLLC service, which will be accompanied by major improvements in 3GPP Release 16 and the coming Release 17+, will improve the redundancy of the data delivery crossing the mobile network. It will target mission-critical applications such as CAV applications, otherwise called C-V2X (Cellular V2X) applications, automation for manufacture, and some smart city applications (Mobile World 2019).

1.3.3 Impacts of 5G Data Services on Connected and Automated Vehicle Migration As mentioned previously, CAV application has been a part of 5G use cases, called C-V2X, which is a bundle of comprehensive road safety and traffic applications. The “C” in C-V2X refers to both 4G LTE and 5G radios and “X” refers to multiple things that vehicles may connect with (Flament 2018). C-V2X will be (Figure 1-11) supported by direct communications, including vehicle-to-vehicle (V2V),

Figure 1-11.  C-V2X within a 5G vision.

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Disruptive Emerging Transportation Technologies

vehicle-to-pedestrian (V2P), and vehicle-to-infrastructure (V2I) communications; and supported by the mobile network, including vehicle-to-network (V2N), pedestrian-to-network (P2N), and infrastructure-to-network (I2N). C-V2X is being promoted since 3GPP Release 14 as LTE-V2X and is continuously evolving toward 5G C-V2X via the forthcoming Release 16 and other future releases. Basically, two interfaces are defined with 5G C-V2X applications, as shown in Figure 1-11 (5GAA 2020). PC5 is specifically defined as V2V and V2I communication interfaces without any assistance from any infrastructure towers. It is featured with a short range, high speed, high reliability, and low latency and may be used in mission-critical and safety-related CAV functions such as crash avoidance (Lopez 2016, Springer 2019, Bazzi et al. 2019). Uu is a V2I interface to support other non-safety-related CAV functions. It allows CAV vehicles to communicate in between and with the infrastructure via the mobile network towers (Springer 2019).

1.3.4  Impact of Continuous Evolution on 5G Standards 3GPP Release 14 was a significant milestone to include the functions of C-V2X direct communications. 3GPP Release 15 introduced the first 5G version of the communication standard (5G NR), which further improved the C-V2X interfaces by promoting high data rates, low latencies, and highly reliable communications. 3GPP Release 16 will make additional improvements in 5G NR and also bring C-V2X into the purview of 5G uRLCC (5GAA 2019) in the following ways: 1. Supporting high-speed and low-latency information exchange between vehicles such as the following: a. Vehicular sensing data in sharing situational awareness. b. Cooperative adaptive cruise control for a platoon of vehicles. c. Cooperative maneuver of AVs for emergency situations such as collision avoidance. 2. Supporting high-speed information exchange between vehicles and infrastructure, for example, a. HD map and local information updated from roadside infrastructure. b. Supporting the use of CAV trajectory data for traffic operations. c. Updating CAV software on-the air and other after-market services. Along with the evolution of C-V2X, series 3GPP releases expected to support backward compatibility to ensure sustained migration. In addition, 5G is flexible for supporting the C-V2X communication networking organization. For instance, a 5G system with 3GPP Release 16 can allow range-based grouping of vehicular communications with the integrity of message exchange and a defined security boundary. The 5G system can support up to five vehicles for a user group supporting the V2X application. For a Vehicle Platooning scenario, the 5G system can support reliable V2 V communications between a specific leading vehicle and

Emerging Technologies Impacting the Future of Transportation

33

up to 19 other following vehicles in exchanging relative longitudinal position with an accuracy of less than 0.5 m.

1.3.5  Testing and Demonstration of 5G Cellular V2X Along with the continuous migration of 5G standards, many alliances/partnership coalitions have been established to promote C-V2X applications and testing, such as 5GAA (5G automotive alliance), which is one of the domain alliances that contribute significantly to C-V2X migrations (Finill and Banjo 2019). Meanwhile, C-V2X trials and proof-of-concept tests have been conducted across multiple industry domains. The following is the summary of such milestone events: • First introduction of a C-V2X chipset by Qualcomm in September 2017 (Takahashi 2017). • First regional C-V2X trials conducted jointly with a chip manufacturer (Qualcomm), a wireless carrier (AT&T), an automobile OEM (Ford), and a transportation vendor (McGain) in San Diego, California, in October 2017 (Howard 2017). • First release of a C-V2X RSU to support PC5 communication interfaces in the summer of 2018. • 5GAA is expected to undergo regional deployment: the first commercial deployments of C-V2X are expected to take place in China and Europe, and deployments in the United States and other parts of Asia will follow closely.

1.3.6  Challenges in the United States with 5G Cellular V2X In the United States, still several challenges exist, acting as a stumbling blocking to C-V2X deployment. The first challenge is the availability of the C-V2X frequency spectrum. C-V2X is proposed to support the ITS spectrum band (75 MHz bandwidth in 5.9 GHz), which was assigned to traffic safety–related applications about 20 years ago by the Federal Communications Commission (FCC), as a Dedicated Short-Range Communication (DSRC) bandwidth. DSRC was used in V2X applications prior to the emergence of C-V2X. Unless the government brings in any changes in policy or rules to allow the use of this frequency spectrum in C-V2X applications, C-V2X cannot be deployed in the United States. There was also a proposal for sharing the ITS bandwidth between C-V2X and other applications, where the DOT via NHTSA would have to agree that spectrum sharing does not pose any threat to safety in critical applications. 5GAA submitted a related petition in December 2018 requesting to use a part of the ITS band for C-V2X deployment (Conway and Knauer 2018). However, as of December 2019, the FCC released a new proposal for public comments, which was planning to create a 20 MHz bandwidth within the 75 MHz ITS band for dedicated C-V2X applications and an additional 10 MHz bandwidth optional for the potential use of C-V2X or DSRC.

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Disruptive Emerging Transportation Technologies

According to the FCC’s recent notice on the use of the wireless communication spectrum in the auto industry, traffic safety remains a critical aspect of the debate over the future of the 5.9 GHz band (Calabrese and Nasr 2020). In all, 75 MHz at 5.9 GHz has been preferred as “the “option value” for application by the auto industry and DOT; in reality, however, critical auto safety communications via V2X technology does not need even half of the 75 MHz that has been allocated for ITS at 5.9 GHz. On the contrary, because the auto industry will not voluntarily commit to the ubiquitous deployment of C-V2X, “NHTSA concluded that without a mandate, not even the single 10 megahertz channel it proposed to dedicate to basic V2V signaling would be put to effective use” (Buskirk 2020). Although the auto industry understandably would like additional free, exclusive-use spectrum for non-critical driving and commercial applications, it would be more consistent with a path to 5G network convergence and the broader public interest to use a combination of unlicensed spectrum and bands other than 5.9 GHz. (Calabrese and Nasr 2020) Eventually, the FCC made a final decision with unanimous approval on November 18, 2020, that the 75 MHz ITS band would be officially taken off the lower 45 MHz for the Wi-Fi industry’s usage, as the proposal described above, where only the maximum 30 MHz band was left for C-V2X applications. The rule was published in the Federal Register on May 23, 2021, which gives limited time for existing DSRC to be migrated to the C-V2X solution.

1.3.7  Summary of Section 1.3 5G is still a developing technology but has a huge potential impact on the future development of smart transportation infrastructures as follows: • 5G eMBB service shall help transportation/ITS communication infrastructures to be an alternative of the broadband solution in enhancing current communication infrastructure, and to expand coverage and improve service quality. • 5G mMTC service shall bring the IoTs technologies together to help transportation community to provide 1. High density and large-scale transportation asset management, 2. Highly reliable and more effective transportation infrastructure monitoring along with the IoTs sensors and devices, and 3. Incorporation of transportation services with other application domains within the smart city territory. • 5G uRLLC service shall benefit the following transportation services: CAV applications with 5G C-V2X technology will deliver high-speed, low-latency, and highly reliable V2V and V2N services, and 5G uRLLC service can also benefit other mission-critical ITS application domains such as the emergency response community.

Emerging Technologies Impacting the Future of Transportation

35

1.4 DESIGN AND DEVELOPMENT OF VIRTUAL REALITY–BASED DRIVING SIMULATION With the recent development of wearable head-mounted displays (HMD) such as Oculus Rift, Quest, HTC Vive, Microsoft Mixed Reality, and numerous Virtual Reality (VR) apps on powerful mobile phones, including 360 imaging and video, VR is being reintroduced as a fully immersive training instrument to consumers in the engineering and manufacturing industries. Unlike the traditional semiimmersive cave automatic virtual environment (CAVE) system and large screen– based car simulator, it is not easy for architects and engineers to design, visualize, and interact with the simulation in the virtual world with an affordable price. “The global VR market was valued at nearly $2.3 billion in 2016 and is expected to reach $39.4 billion by 2022” (BCC Research 2018). Immersive technology accounted for the largest share in the VR component market. It was valued at $1.3 billion in 2016 and is expected to reach $23.6 billion by 2022. Engineers strive to tangibly enhance humanity’s well-being through the development of simulated training systems. (BCC Research 2018) As we continue to design the virtual and physical worlds, the unanswered question is, how can VR bridge these domains through simulation? As VR increasingly interconnects the virtual and physical worlds through training scenarios, how will this relationship influence engineers to augment the hardware and software to solve complicated problems?

1.4.1  Virtual Reality VR is a computer-generated environment created through a combination of hardware and software applications, allowing users to interact physically through a digitally rendered 3D environment with the use of a head-mounted display (HMD) unit and corresponding input tracking devices. With the use of a range of pertinent systems such as headsets, gloves, and so on and computer technology, VR is implemented by stimulating the user’s sensorial responses, including through visual, audible, and/or haptical experiences. Nonetheless, ironically, having been a promising visualization tool since the 1950s, VR has not been widely used in the simulation and training process because of the high cost of equipment and necessary complicated programming processes. With the recent development of affordable HMD units such as Oculus Rift, Quest, HTC Vive, and other easy-touse VR-based engines, VR is being reintroduced as a training instrument in the engineering industry. VR has a significant impact on the fields of architecture, civil engineering, and construction because of the rapid advancement of computational technologies. It also affects the way professionals plan, design, and construct cities and dependent infrastructure, which, by effect, will never be the same. For instance, VR technology allows engineers to design and evaluate the performance of transportation systems within a simulated environment in a short period of time. Before the advent of VR technology, a new idea/concept would typically be tested

36

Disruptive Emerging Transportation Technologies

Figure 1-12.  Exemplary driving simulation. through the production of mockup models and prototypes. Because engineers no longer need to depend solely on such analog models, pertinent development can start sooner and be combined with multiple iterations for evaluation and experimentation throughout the design process. On the completion of the creation of 3D scenarios, VR allows a test drive of a vehicle for safety training in an immersive environment (Figure 1-12). Such an environment is constructed by using computer-generated sensory inputs such as visual, sound, and haptic feedback. Through sensory perception and the motor response of users, VR helps a person to perform sensorimotor and cognitive activities in a virtual world. Drivers can virtually experience various vehicles, routes, time periods, and weather and react accordingly in a VR environment.

1.4.2  Simulation of the Real World Bicocca discusses “essential copy” and “physical transcendence” as the two main reasons behind the formation of all virtual worlds (Biocca and Lecy 1995). They describe the searching for the “essential copy” as “a mean to fool the senses, a display that provides a perfect illusory deception.” They illustrate the “physical transcendence” as “… an ancient desire for escape from the confines of the physical world, free the mind from the ‘prison’ of a body” (Biocca and Levy 1995). According to Kalay (2004), it is essential to enable the viewer to “control his or her own actions, especially to look around and see the environment at will.” To embrace new real-time visualization methods, the research team at the University of Cincinnati (UC) investigated the technologies in the present, fastgrowing game industry, specifically focused on the newly developed HMDs such as Oculus Rift and HTC Vive Pro Eye, which provide sensor-based head tracking and eye tracking in an immersive environment. The gaming industry is one of the fastest-growing, technology-intensive industries with the latest development of HMDs and the human–computer interface devices that are pushing VR to a new level. VR in HMDs can provide superior graphics quality using real-time reflection, depth of field, displacement maps, and normal maps with global illumination. Game engines are capable of handling highly complicated, highpolygon geometries with high frame rates and can compile them for subsequent VR display. Supported by GPU power, VR engines allow people to perceive virtual content with simulated shape, light/shadows, textures, and relative dimensions

Emerging Technologies Impacting the Future of Transportation

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with a high rendering frame rate. As a result, the powerful VR engines have blurred the line between scientific simulation and interactive gaming in the industry. To compress the design timeline and maximize the efficiency of the workflow, the research team at the University of Cincinnati managed various types of 3D modeling software, which allowed 3D modelers to quickly generate parametric models and correspondingly load them into game engines while compiling the entire environment for VR simulation. Rendering materials in such simulation are usually generated procedurally in the game engine with shader networks to mimic the physical properties of the real materials. Reflection probes, light probes, and real-time ray-trace technology were used to simulate reflective materials and dynamic lighting. Sunlight and skylight arrangements were constructed to generate an indirect light and dynamic daylight system. Point light, spotlight, and area light components with IES light profiles were added to simulate artificial interior light systems. Like a video game, to facilitate the VR training purpose, a symbolic world layer was usually added above the real world. The symbolic world includes allegorical representations to improve the understanding of reality, such as route map, traffic data, and highlighted speed warning. This added layer can create an enhanced mental projection of the real-world environment.

1.4.3  Interactivity and Interface Virtual Reality supports perception, decision, and action loops, which are identical with those in the physical world. VR engines need to compute a minimum of 30 frames (images) per second (FPS) for monoscopic images or a minimum of 60 FPS for stereoscopic images to allow a person to perceive and/or interact with a digital world smoothly. In an immersive digital environment, VR supports virtual behavioral primitives (VBPs) that include an observation of the environment, navigation through the environment, and manipulation of the environment. For instance, in a VR-based driving simulation, the user observes, drives, and acts in the VR world and communicates with others in the real world. These behavioral primitives are identical with real-world driving experiences. The behavioral interfaces can be classified into two groups, as shown in Figure 1-13. The first group is a motor interface including joysticks and data gloves to support gesture and treadmills to support movement and voice control. In general, people use real-world schema, a structured set of characteristics of an action that can be generalized, to repeat the action or apply it to new contexts, to apply decision-based actions through the motor interface in VR (Fuchs 2017). The second behavioral interface is the sensorial interface, which includes feedback from image and sound. The sensorimotor interface is formed by the visual interface, tactile feedback, and audio interface to allow vision, hearing, touch, smell, and (even) taste in the VR world.

1.4.4 Hardware Compared with the expensive IMAX cinema, or CAVE system, a low-cost HMD can provide people with a virtual environment with a higher presence because fully immersive technology, combined with full-body motion tracking, offers the highest

38

Disruptive Emerging Transportation Technologies

Figure 1-13.  Interactivity and interface of virtual behavioral primitives. level of immersion. It is the behavioral interface (motor and sensorial interface) that makes the users feel that they belong there, by providing users the VBPs, including observation, navigation, and manipulation. Alternative technologies such as semiimmersive and nonimmersive technologies cannot match the level of presence provided by fully immersive technologies. Figure 1-14 illustrates the conceptual comparison of three types of virtual reality based on the immersion level. However, when compared with new HMD-based VR technologies, the traditional semi-immersive systems are much more expensive, but comparatively fully developed implementations in the VR training simulation. In a semiimmersive training experience, a driver/trainee is partly immersed in a VR environment. For instance, a truck simulator is set up to consist of multiple screens, a steering wheel, pedals, and corresponding control panels, which is identical with panoramic screen experiences involving high-end projectors like IMAX Cinemas.

Figure 1-14.  Three types of virtual reality based on the immersion level.

Emerging Technologies Impacting the Future of Transportation

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Figure 1-15.  Semi-immersive and fully immersive VR driving system. In widescreen projections, the semi-immersive technology covers the driver’s field of view and heightens the driver’s experience by increasing the feeling of immersion. Mixed with physical interface elements, a semi-immersive system provides a higher rendering resolution than HMDs, better control of the steering wheel, and superior human interaction with the trainer sitting in the second front seat (Figure 1-15). It is still a long way to go for the newly developed HMD fully immersive VR to match the haptic and human interactions of traditional semiimmersive VR. The emerging development of full-body motion tracking and haptic feedback system such as TeslaSuit are still in their infancy phase.

1.4.5  Software and Scenario Creation In recent years, with the development of high-end game engines such as Unreal and Unity and numerous real-time rendering engines such as Enscape, Lumion, and Twinmotion, traditional 3D visualization has been transformed into realtime renderings. A wide range of tools are available to help people to design, visualize, and interact with 3D models in a real-time environment throughout the planning and creation stages.

1.4.5.1  Planning Stage Typically, the planning stage is referred to as a workflow to design the concept of virtual world research and all the visual and nonvisual stimuli that need to be included and categorize them based on their features (vehicles, roads, terrain, and buildings). Various references for the world are identified, including, but not limited to, photos, satellite images, 3D models, animations, sounds, and various reference media (including films and games). A 3D world is usually developed for simulation through various tools, including modeling software, existing data from geography information system, and digital elevation models surveys, laser or LiDAR scanning, and photogrammetry. The environment data usually include roads, buildings, terrain, and surface landscape, which will be input to a database to construct the virtual world. At the end of this phase, the team will have a development pipeline and various assets that the team needs to create to bring the real world into VR.

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Disruptive Emerging Transportation Technologies

1.4.5.2  VR Creation Stage In this stage, creating an immersive VR is usually organized into several steps. First, the team will usually create a top-down concept map that acts as a base plan. During the early phase, the team will block out the landscape, creating materials and assembling a general environment in a playable state for the driver to test if all visual elements work together. The next step is to block out the environment with simple roads and terrain. This proxy scenario helps the team to control vehicle driving and provide a testing ground to start developing the rest of the world (Figure 1-16). The goal is to ensure that the driving simulation is visually rich, and the physics is correct, so that a virtual driver can interact with the vehicle and the road to obey the laws of physics. Subsequent to the proof of concept, the team will usually model a specific environment(s) in a polished visual appeal to achieve a higher level of visual/ programming complexity. This is the stage where the modeling team creates all the unique modular assets that will be used to compose the overall scene. The team will work on roads, buildings, traffic lights, terrain, trees, and signs and consider the relevance of object scale, maintain a high video frame rate, and take programming action to reduce cybersickness. The team will usually follow a fixed production workflow to create environments, time-saving modeling, texturing, and UV mapping techniques. The team will add dynamic effects, including real-time interactions and effects, to enhance the degree of immersion and presence of sensory stimulation. These include visual elements such as the weather system, animated landscape features, and nonvisual systems such as engine noise and vibration. The final environment will be assembled and optimized in either a fully immersive system with HMD or a semi-immersive system with large screens (Figure 1-17). The team will adjust the virtual environment, including traffic, pedestrians, and the

Figure 1-16.  Exemplary scenario modeling in the unreal engine to host a driving simulation.

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Figure 1-17.  Exemplary VR driving simulation: (a) First-person view of VR content creation in unreal game engine, (b) third-person view of VR driving simulation. weather system and post effects such as fog, lens effects, and the field of view to reduce cybersickness and improve the sense of virtual and physical presence.

1.4.5.3  Data Collection and Analysis After the VR scenario creation, the effectiveness of the training simulation will be measured. Many qualitative and quantitative methods and scoring systems are available to measure the success of a product based on user feedback, for example, a presence questionnaire and a 7-point Likert format that is based on the semantic differential principle (Matthews et al. 1978). The evaluation can also target specific features in the subsystem, such as terrain, route, weather, vehicle, pedestrians, and physics simulation. In addition to a screen capture of the scene and video recording of the trainee’s action, other sensory data can be collected during the training simulation. Eye tracking (ET) is an emerging method of data collection that helps researchers record a user’s visual attention. Across different fields of application and design, ET has become a way of interpreting user experience, which may not be described quantifiably with traditional observing methods. Mostly used in the product design and retail design, ET pilot studies have begun to surface within the training fields regarding what elements trainers are specifically fixated with. Recent ET technologies and devices such as Tobii Pro glasses, X3-120, and HTC Vive Pro Eye allow ET to be integrated as a powerful analytical tool (Figure 1-18).

Figure 1-18.  Exemplary integrated analytical tool in VR driving simulation: (a) Eyetracking data collected with Tobii Pro glasses, (b) heat map overlay with a picture, (c) heat map generated by Tobii Pro Screen-based eye tracker.

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Figure 1-19.  EEG sensor used in nonimmersive driving simulation. During the data collection stage, electroencephalogram (EEG) monitoring can provide physiological measurements. The device is placed on the user/ trainee’s wrist or head to measure brain waves, skin conductance, heart rate, and blood pressure. Using analytical data, it is possible to study driving behavior and stress, in conjunction with ET. Researchers can investigate if this method could measure what one focuses on in addition to understanding what elements provoke stress in the user/trainee. The benefits of obtaining stress patterns as interpretable data open up opportunities for design vehicle, road, signage, and traffic control systems, drawing on the field of neuroscience (Figure 1-19).

1.4.6 Demonstrated Study of Urban Mobility in Driving Simulation As Goldhagen (2017) explained, “Cognition is a product of a three-way collaboration of mind, body, and environment.” Specifically, VR technologies, either through immersive HMD or through large screens, offer a promising platform to study human cognition developed through visual reactions in an interactive environment. In this section, a study of urban mobility is demonstrated to show an exemplary application of using driving simulation through fully immersive, semi-immersive, and nonimmersive VR at the University of Cincinnati. This study aims to create a new interchange that connects uptown local communities, cities, and states by consolidating public transportation and new roads. It is designed to provide a linkage between proposed light rail trains and other modes of transportation such as a hyperloop, bus rapid transit (BRT), air taxis, passenger cars, bicycles, and scooters. By contrast, most of the existing developments in transportation facilities have been carried out individually for a single mode of transportation, resulting in less efficiency and/or connectivity in terms of the overall transportation network; this “multi-mode transportation node” concentrates transportation in a one-stop solution. One way to simulate the cognitive experience is to drive a vehicle that gives the user the freedom to experience the proposed design through various input devices, including HMD, keyboard, mouse, and motion controllers. Through VR, the research team could analyze the biological reactions of human visual attention and perception. The team started to combine screen-based ET and nonimmersive VR to test their unique advantages for studying the visual attention and movement behavior of people in virtual space.

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During the study, Tobii Pro X3-120 was used to capture ET data during the driving simulation. The primary purpose of this early test was to have a better understanding of how participants visually respond to various spatial configurations. Tobii Pro captured the user’s eye movement and recorded the eye fixation duration on a specific area of a photo. By using analytical tools in Tobii studio, the researchers generated a data representation using the Area of Interest (AOI), Bee Swarm (Gaze Pattern), and heat map methods (Figure 1-20). The analytical method is built on the five visual elements: face, edge, intensity, blue-yellow contrast, and red-green contrast, which trigger subconscious viewing. These five elements are described as “building blocks of visual attention” in the visual attention software by 3M (3M 2019, Tang and Auffrey 2018). The path of the user’s eye movement and the duration of a user’s gaze are different ways in

Figure 1-20.  Examples of heat map and gaze plot: (a) Real-time eye-tracking data in a driving simulation. Purple dot represents the eye gaze. (b) Tobii-Pro screen-based eye-tracking. Source: Images provided by Josiah Ebert, UC.

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which the analytical algorithm begins to value given stimuli, captured either from a screen-based ET, from glasses, or from an immersive HMD. ET and VR in the driving simulation provide first-person experiences in the circulation design for vehicles and driving. This can provide a real-time observation of where the eye gaze may run when a user is driving through the designed space. ET provides engineers, planners, architects, and designers a range of spatial design ideas to direct vehicle movement within the space in a particular manner. This becomes crucial while designing a transport hub, in which wayfinding design elements are an essential part of a program organization. The ET for driving simulation can improve the design evaluation and take it to a new level. An additional factor to account for in analyzing the success of a design is through recording simultaneous ET and physiological data. For example, if a design draws attention to desired locations, but the driver is mentally stressed in the process of gazing, the purpose of what he/she is gazing at might be clouded, leading to possible confusion or sensory overload. The level of stress detected is typically very high for the first 5 to 10 s; subsequently, it quickly drops and stays low for the remainder of the driver’s experience. This drop reveals that, although the complexity of the route and signage is initially jarring, the simplicity of the overall layout and circulation is understood by the driver relatively quickly, and when understood, the route is easy to navigate. The impact of biophilic design, which is mixing green trees within an artificial environment, can be studied in VR to reduce the driver’s stress level. The degree of stress level reduction measured through an Empatica wristband can be applied to the wrist of the driver during the simulation. Figure 1-21 demonstrates the results of the virtual driving experiment shown in the chart. During the simulation, participants were asked to drive in VR while wearing the Empatica wristband to measure stress levels. It was observed that the stress levels had reduced as they approached the biophilic design elements within site.

Figure 1-21.  Stress level captured through Empatica wristband during VR simulation. Source: Images provided by Shreya Jasrapuria, Niloufar Kioumarsi, UC.

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The graph illustrates a reduction in stress levels as the user moves closer to the green walls and trees. This information gives the researcher a better understanding of the quantity and placement of these natural elements within the drivable space.

1.4.7  Conclusion and Challenges to Section 1.4 The global market of VR technologies for engineering applications was valued at $152.8 million in 2016 and is estimated to grow at a CAGR of 69.3% and is forecast to reach $3.6 billion by 2022. (Virtual Reality Technologies: Global Markets to 2022, BCC Research) The use of immersive technologies plays a significant role in the transformation of engineering applications. The primary use of VR in engineering includes 3D modeling tools/design, training, and visualization techniques for retail, construction planning, and management. Specifically in the field of education, VR, combined with other sensory data analysis, is a significant innovation that has been helping engineers and designers visualize and evaluate the proposed design quantifiably. The use of VR gives a sense of movement and passage of a period of time to otherwise flat, spatial projections. It allows the user to visualize how the built space would be perceived in reality when constructed. In the case of a transportation hub involving many different modes of transport, it becomes essential to understand the organization of the space, how each transit mode works in tandem with the others and how the pedestrians/vehicles navigate through each of them or independently reach them. In addition to the well-known cybersickness, sometimes called motion sickness in HMD, several technical, physiological, and cognitive constraints related to VR exist. The performance and frame rate of VR will drop dramatically if a scene has many polygonal faces. The low frame rate will result in display latency, sensorimotor discrepancy, temporal visual-motor discrepancy, and the latency of the “perception, cognition, and action” loop. Collectively, they may contribute to motion sickness. The level of detail limits requires the environment to be modeled efficiently to minimize the polygon number, which has never been a priority in standard 3D software, whereas the skills to optimize a sophisticated model for real-time rendering are essential. However, as a side effect, a low polygon model will lose details and demonstrate poor visual appeal when the camera gets closer to the object in VR. The challenges and characteristics of cognition and interaction in VR offer the research field a greater opportunity for innovation. For instance, the current low field of view in VR cannot provide a peripheral view. Even with a highresolution display and wide view angles, technically, it is not possible to display peripheral-vision images in HMD. There is a lack of proprioceptive sensations when interacting with the environment in VR; the current technologies such as a treadmill and haptic gloves do not provide the real muscular and kinesthetic experience. The VR technology will eventually overcome all these challenges in this booming, developing industry.

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1.5 APPLIED INTERNET OF THINGS TECHNOLOGIES IN TRANSPORTATION The IoTs refers to a system of interconnecting a large number of physical devices anywhere via internet to collect and share data. The IoTs technologies can be used to promote a large-scale distribution of low-power, low-cost remote devices to be connected as a machine-to-machine (M2M)-type networking to support mass data collection, systemwide monitoring, and industrial automation controls. Being recognized as one of the key technologies in the Smart City domain, the IoTs technologies are being gradually adopted in various application domains, including transportation (Anand et al. 2015). This section will address the following aspects of the IoTs technologies: overview of the IoTs technologies, and identification of transportation application scenarios where the IoTs can help.

1.5.1  Overviewing of Internet of Things Technologies The 2018 Ericson Mobility Report (5G Americas 2019) indicated the following: There were roughly 400 million the IoTs devices with cellular connections at the end of 2016, which could reach 1.5 billion in 2022, and the amount of the IoTs connections already exceeded mobile phone connections in 2018. These numbers present the picture of today’s digital world where machine-tomachine type communication with the IoTs will play increasingly important roles in various applications. The major features of the IoTs are as follows (5G Americas 2019, Ai et al. 2018, Botta et al. 2015): • Low-cost sensors; • Low-cost power/energy (with the expectation of a long battery life); • Majority of IoTs applications demands low communication bandwidth; • Commonly large/ultra-large-scale deployment, which demands extended coverage and reliable connectivity; • Scalable networking in supporting deployment flexibility; • Supporting diverse applications (e.g., from simple remote metering monitoring to living video surveillance); and • Supporting Big Data Analytics at the backend system. The common IoTs system architecture includes the following: • Things—remote IoTs devices/sensors; • IoTs Gateway—interfacing with a backed/cloud system; • IoTs Communication Network—communication media and protocols supporting the IoTs data transmission, which include the following: 1. Communication between the IoTs sensors and the IoTs gateways,

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Figure 1-22.  Concept of an Internet of Things (IoTs) architecture. 2. Communication between the IoTs gateways and the IoTs backend cloud, and 3. Communication between the IoTs devices. • IoTs Cloud System—backend cloud platform supporting mass data aggregation and Big Data Analytics. Figure 1-22 illustrates this concept.

1.5.2  IoTs Communication Technologies and Protocols Most technologies of the IoTs are featured as vendor-specific proprietary frameworks or community-based solutions. Along with the migration path of the IoTs technologies, the communication solutions vary from time to time. Despite wireline solutions, most wireless solutions exist, ranging from a short to middle coverage range to a long coverage range by using satellite communications. Therefore, the standards of the IoTs vary widely on the basis of not only the market domination of vendors, but also promotion from standardization development organizations. Figure 1-23 shows the major application domains of the IoTs. The IoTs communication technologies could be further classified as frequency spectrum usage and unlicensed and licensed radio technologies (5G Americas 2019, Postscapes 2019). The unlicensed radio technologies are • ZigBee Technology, • Z-Wave Technology, • Wi-Fi,

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Figure 1-23.  Major IoTs application domains. • Bluetooth, and • LORA and Sigfox. The licensed radio technologies are • Cellular Technologies, • GSM, • 2G/3G, • 4G LTE/LTE-M/MB-IoT, and • 5G mMTC. Depending on different application scenarios, IoTs solutions can offer different benefits in terms of coverage range, data rate, and dominated marketing areas. Table 1-4 summarizes the main features of these frameworks of the IoTs.

1.5.3 Standardization Migration of Internet of Things Technologies Currently, no universal standard across the world of the IoTs exists. A few partners/ alliance entities maintain their own proprietary standards (from connection setup to system interfacing, and data schema), as shown in Table 1-5 (Ai et al. 2018; SamSolutions 2019).

10–100 m

250 kbps

Mesh Medium ZigBee-PRO

Range

Data rate

Network Cost Standards

Bluetooth

Sources: 5G Americas (2019), SamSolutions (2019), Postscapes (2019), Hwang (2018).

35 km (GSM); 200 km (HSPA)

Smart grid Smart metering Smart city

3GPP

Cellular coverage 1–100 kbps

Smart grid Smart metering Smart city

Smart grid Smart metering Smart city

Cellular Cellular High Low 3GPP 3GPP release 13 release 15 and up 3GPP 3GPP

Cellular coverage 200 kbps– 1 Mbps

Low Cellular bands

4G LTE LTE-M/NB-IoT 5G mMTC

High Medium Cellular bands as Cellular 900/1,800/1,900/2,100 MHz bands

GSM/2G/3G

Max. 170 kbps (GPRS) 600 Mbps 384 kbps (EDGE) 2 Mbps (UMTS) 10 Mbps (HSPA) Mesh Cellular Low High IEEE 802.11 3GPP release

100 m

Medium 2.4 GHz 5 GHz

Wi-Fi

Mesh Low Bluetooth lowenergy (BLE) LoRa Bluetooth IEEE alliance SIG Smart grid Healthcare, Every Smart fitness, industrial metering beacons domain Smart city security, Smart home

0.3–50 kbps 1–3 Mbps

Mesh Mesh Medium Medium Proprietary LoRAWAN

40 kbps

Low Low Low 800– 868– 2.4 GHz 900 MHz 915 MHz 868– 902 MHz 40–300 m 2–15 km 50–150 m

Z-Wave LORA and technology Sigfox

Standard ZigBee Z-Wave organization alliance alliance Major market Smart home Smart Manufacture home Smart metering Smart grid

Low 2.4 GHz

Power usage Frequency

ZigBee technology

Table 1-4.  Summary of Main Features of the IoTs Communications and Protocol Solutions. Emerging Technologies Impacting the Future of Transportation

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Table 1-5.  Major IoTs Cloud Platforms Comparison. Amazon

Microsoft

Google

IBM

IoT platform

AWS IoT

Azure IoT Hub

IBM Cloud

IoT services

Data pipeline Machine learning

Stream analysis Machine learning Power PI

IoT protocols

HTTPS, MQTT, Web Sockets Amazon FreeRTOS AWS Greengrass

HTTPS, AMQP, MQTT, Web Sockets, CoAP Windows 10 IoT

Google Cloud IoT Core Big data Stream analysis Machine learning HTTPS, MQTT, gRPC Android things Cloud IoT Edge

62%

20%

IoT OS Edge computing for IoT Cloud service market share

Azure IoT Edge

Machine learning

HTTPS, MQTT Watson IoT

12%

For cellular-based solutions of the IoTs, standardization efforts are being carried out by 3GPP, particularly with today’s migration path toward 5G (5G Americas 2019, Hwang 2018, RF Wireless World 2019). • Two types of LTE (long-term evolution)-based technologies of the IoTs were incorporated in the 3GPP Release 13 (2016), which are being widely supported by today’s 4G LTE networking. • LTE-M (M stands for machine-type communication), in “Supporting lower device complexity, massive connection density, low device power consumption, low latency and extended coverage.” • NB-IoT (Narrow Band-Internet of Things), “With improved indoor coverage, support of massive number of low throughput devices, low delay sensitivity, ultra-low device cost, low device power consumption and optimized network architecture.” • These two solutions could be deployed as an “in-band” together with normal mobile services or as a “stand-alone” with a dedicated spectrum band. • As part of 5G migration use cases in “massive” machine-type communications (mMTC), it is expected to support extreme density of sensor deployment up to 1 million connected vehicles and devices simultaneously, and support emerging new IoT application domains, such as factory automation and UAV control.

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1.5.3.1  Internet of Things Sensors The IoTs sensors are key components of a IoTs system, which have the following features (TechBriefs 2019): • Low cost, • Low energy consumption, • Physically “small,” • Wireless and preferred higher reliable connectivity, • Hard and rugged for diverse deployment environments, • Capable of self-identification and self-validation, • Robust to minimize or eliminate maintenance, and • Capable of self-diagnostics and self-healing. The sensor types range differently depending on the application domains (Sharma 2019), for example, temperature, proximity, pressure, water quality, chemical, gas, smoke, IR, level, image, motion detection, accelerometer, gyroscope, humidity, and optical sensors. Many of these sensors could be used in various smart transportation and smart city application scenarios, which naturally involve the IoTs technologies in solving transportation issues.

1.5.3.2 Internet of Things Supporting Cloud Services and Application Layer Protocols Apart from infrastructure support of the IoTs application, the IoTs application layer protocols are also very important. The large-scale deployment of the IoTs sensors shall demand the cloud infrastructure to support a large amount to realtime data collection with a secure and privacy-protecting environment. Table 1-5 shows the typical IoTs cloud platforms currently available in the market, based on the information provided from Cano (2018) and Joao (2019). At the network and transport layer, as shown in Figure 1-25(a), most IoTs solutions are built on an IP-based TCP/UDP protocol (except ZigBee). To better execute the task of massive data collection, most of the IoTs application layer protocols are particularly designed to support light tied message-based solutions but with wide diversity. Table 1-6 shows the major IoTs application layer protocols.

1.5.3.3  Internet of Things Application Domains The IoTs technologies can be widely used in many application domains, some of which are listed as follows (Cano 2018): • Smart city, • Connected industry, • Smart building, • Smart home,

Message oriented TCP One-to-many TLS/SSL Yes

Protocol type

Transport layer Mechanism Security QoS

Pub./Subs.

Architecture

MQTT

Message oriented TCP One-to-many Proprietary Yes

Pub./Subs.

SMQTT

Table 1-6.  Major IoTs Application Layer Protocols.

Req./Resp Pub./Subs. Message oriented TCP One-to-one TLS/SSL No

Web Socket

Message oriented TCP One-to-many TLS/SSL Yes

Pub./Subs.

AMQP Req./Resp. Pub./Subs. Message oriented TCP One-to-many TLS/SSL No

XMPP

Resource oriented UDP One-to-one DTLS Yes

Req./Resp.

CoAP

Resource oriented HTTPS One-to-one HTTPS No

Req./Resp.

RESTFUL

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• Connected and automated vehicle, • Smart energy/smart grid, • Healthcare IoTs, • Smart logistics, • Smart retails, • Smart agriculture, and • Manufacture automation.

1.5.3.4  Linking Internet of Things with Other Technologies Being a bundle of applications, the IoTs cannot work alone. Instead, it makes use of the following two associated technologies to create an integrated ecosystem: 5G and Edge Computing.

1.5.3.5  Impact of 5G Migration Cellular networking is one of the dominant IoTs communication solutions. The forthcoming 5G will deeply impact the future IoTs applications with its promoted use case, referred to as the massive machine-type communication (mMTC). 5G mMTC supports low-power ware-area (LPWA) connectivity with a much higher data rate, large-scale coverage, and a higher density of the IoTs device deployment (1 million low-power devices within 1 km simultaneously) (RF Wireless World (2019). These can be elaborated as follows: • 5G mMTC supports a high density of devices. • It supports a long range. • It supports a low data rate (about 1 to 100 kbps). • It leverages the benefits of ultralow cost of M2M. • It offers 10 years of battery life. • It provides asynchronous access.

1.5.3.6  Impact of Edge Computing As discussed in the previous section, the concept of Edge Computing intends to bring more computing intelligence to the remote Edge side. Shifting computing to where sensors are located reduces the system response time and improves the data processing capability. The Edge Computing framework will play a unique role with the IoTs use cases, because a large-scale deployment of the IoTs devices will generate a huge amount of real-time data. If these data are all transmitted to the cloud for processing, it will add significant pressure to the network bandwidth usage. With the support of the Edge Computing architecture, the massive IoTs data can be processed at the edge side directly, which can improve the system response time, resulting in lower latency and improved reliability performance. Only the

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necessary postprocessing data need to be transferred to the cloud. Therefore, undoubtedly, the Edge Computing architecture will be tightly integrated with the IoTs ecosystem.

1.5.4  Transportation Scenarios of Applying Internet of Things Although the use of the IoTs technologies in the transportation domain is still in its exploratory stage, the IoTs has great potential to be embedded into almost every application scenario in transportation systems.

1.5.4.1 Transportation Infrastructure Monitoring and Asset Management by Internet of Things The IoTs capability of massive sensor deployment and data collection can assist monitoring and tracking the changes of transportation infrastructure. In addition, with the integration of Edge Computing and cloud technologies, it can increase the capability of data-driven analysis and prediction modeling in some of the following aspects (Anand et al. 2015): • Infrastructure health, • Roadway facility status, • Road safety status, • Roadway traffic status, • Asset management data collection, and • Maintenance decision-making.

1.5.4.2  Bridge Monitoring by Internet of Things The Living Bridge Project in New Hampshire is where the memorial bridge that connects Portsmouth, New Hampshire, and Kittery, Maine, is monitored on a dayto-day basis with the installation of various sensors around the bridge. The following information has been collected in real time (Ridden 2019). Figure 1-24 shows the project overview of (i) bridge structural performance, (ii) bridge traffic patterns, (iii) bridge-surrounding weather conditions, and (iv) sea-level and tidal information. Similar efforts were made in Michigan to monitor the health status of the Mackinac Bridge that connects the upper and lower peninsulas of Michigan (Goldstein 2019, Ebsworth-Goold 2016).

1.5.4.3  Smart City and ITS Applications with Internet of Things The IoTs has been recognized as one of the dominant technologies for smart city. Smart city covers various application scenarios. Figure 1-25 demonstrates such IoTs application scenarios based on a survey of 1,600 IoTs projects in February 2018 (Scully 2018). As shown in Figure 1-25, traffic management was identified as the top demand for the IoTs smart city projects. The corresponding applications might include some of the following:

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Figure 1-24.  Overview of the living bridge project—infrastructure monitoring by IoTs. Source: https://livingbridge.unh.edu/.

Figure 1-25.  Smart city and IoTs. Source: Scully (2018).

• Congestion management with arterial signal operations, • Roadway maintenance and construction management, • Intelligent public transportation, • Parking management,

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• Multimodal and nonmotorized traffic management, • Special event management, and • Disaster response and dissemination preparation. In addition, smart transportation management may also operate in conjunction with other smart city applications such as public safety, utilities management, and smart street light management, and so on. The IoTs sensor networks can help with smart transportation/ITS infrastructure migration in many matters, particularly large-scale data collection and status tracking.

1.5.4.4  Connected and Automated Vehicles and Internet of Things The development of CAV technologies can also benefit from the IoTs technologies. Like the data collection by the massive IoTs sensors, the full deployment of CAV vehicles will also generate a massive amount of data every single moment. Therefore, CAV can be integrated with a part of wider IoTs transportation systems, providing benefits when combined with many existing technologies of the IoTs. For example, because of a large-scale deployment feature, many platforms of the IoTs already include mature big data processing technologies capable of dealing with a large amount of real-time data. It is highly possible that some matured IoTs application layer protocols will be beneficial for a CAV data processing system and, therefore, designed for such a system.

1.5.5  Conclusion of Section 1.5 The IoTs technologies have been adopted by other industrial domains for many years, and they hold great promise to the transportation industry, too. With the combination of 5G wireless technologies and Edge Computing technologies, the IoTs can contribute more to the following: • Transportation infrastructure monitoring, • Smart city and ITS application, and • Connected and automated vehicle applications.

References 3M. 2019. “Visual attention software (VAS) by 3M.” Accessed September 4, 2019. https:// www.3m.com/3M/en_US/visual-attention-software-us/. 5G Americas. 2019. “5G—The Future of IoT.” White Paper. Accessed September 1, 2019. https://www.5gamericas.org/wp-content/uploads/2019/07/5G_Americas_White_ Paper_on_5G_IOT_FINAL_7.16.pdf. 5GAA (5G Automotive Association). 2019. “Timeline for deployment of C-V2X— Update.” White Paper. Accessed September 2, 2019. https://5gaa.org/wp-content/ uploads/2019/01/5GAA_White-Paper-CV2X-Roadmap.pdf. 5GAA. 2020. “A visionary roadmap for advanced driving use cases, connectivity technologies, and radio spectrum needs.” White Paper. Accessed August 28, 2021.

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CHAPTER 2

Surface Transportation Automation Heng Wei, Paul A. Avery, Hao Liu, Gaurav Kashyap, Jianming Ma

2.1 CONCEPTS OF VEHICLES IN COMPLIANCE WITH SOCIETY OF AUTOMOBILE ENGINEERS AUTOMATION LEVELS 2.1.1  Society of Automobile Engineers Automation Levels Connected and autonomous vehicle (CAV) technologies are among the most heavily researched automotive technologies. The vehicle technologies currently available are only a fraction of what is being developed for the future. The technologies for autonomous cars, connected cars, and advanced driver assistance systems overlap, and the following is an overview of the technologies, definitions, benefits, and challenges of this emerging sector (CAAT 2019). Autonomous vehicle (AV) is referred to as the fully automated or self-driving vehicle. US Department of Transportation’s National Highway Traffic Safety Administration (NHTSA) defines AVs as those in which operation of the vehicle occurs without direct driver input to control the steering, acceleration, and braking and are designed so that the driver is not expected to constantly monitor the roadway while operating in self-driving mode. For the sake of standardization in understanding the levels of automation while aiding clarity and consistency, NHTSA has adopted the definitions of the Society of Automobile Engineers (SAE) International for levels of automation (i.e., SAE Automation Levels or Levels of Driving Automation). These definitions divide vehicles into levels based on “who does what, when” (CAAT 2019, SAE International 2018). Table 2-1 provides details in defining the SAE automation levels. SAE automation levels 0 to 2 are referred to as lower technologies, whereas SAE

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Table 2-1.  SAE Automation Levels. SAE level and name SAE level 0: no automation

SAE level 1: driver assistance

SAE level 2: partial automation

SAE level 3: conditional automation

SAE level 4: high automation

SAE Level 5: Full Automation

Source: SAE International 2018.

Description The full-time performance by the human driver of all aspects of the dynamic driving task, even when vehicles automated by warning or intervention systems The driving mode—specific execution by a driver assistance system of either steering or acceleration/ deceleration using information about the driving environment and with the expectation that the human driver performs all remaining aspects of the dynamic driving task The driving mode–specific execution by one or more driver assistance systems of both steering and acceleration/deceleration using information about the driving environment and with the expectation that the human driver performs all remaining aspects of the dynamic driving task The driving mode–specific performance by an automated driving system of all aspects of the dynamic driving task with the expectation that the human driver will respond appropriately to a request to intervene The driving mode–specific performance by an automated driving system of all aspects of the dynamic driving task, even if a human driver does not respond appropriately to a request to intervene The full-time performance by an automated driving system of all aspects of the dynamic driving task under all roadway and environmental conditions that can be managed by a human driver

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automation levels 3 to 5 are referred to as technologies related to automated driving system (ADS)–equipped vehicles and equipment.

2.1.2  Connected Vehicle A connected vehicle (CV) is equipped with specific devices that allow bidirectional communications with the driver, other vehicles on the road and roadside infrastructures, as well as the “cloud” systems outside of the vehicle, as illustrated in Figure 2-1 (Elliott 2011, Leonard 2018). The CV technologies allow the vehicle to share data via internet access with other devices both inside and outside the vehicle (Meola 2016). Recently, DOT refined the definition of CVs as “a network of vehicles that communicate wirelessly with each other, infrastructure, and wireless devices” (DOT 2020a). The CV communication technologies enable cars, buses, trucks, trains, roads and other infrastructure, and our smartphones and other devices to “talk” to one another (DOT 2020). In general, there are five ways by which a vehicle can be connected to its surroundings and communicate with them (CAAT 2019; Locke 2020): • V2I (vehicle-to-infrastructure): The technology captures data generated by the vehicle and provides information about the infrastructure to the driver. The V2I technology communicates information about safety, mobility, or environment-related conditions (DOT 2019a). • V2V (vehicle-to-vehicle): The technology communicates information about the speed and position of surrounding vehicles through a wireless exchange of information. The goal is to avoid accidents, ease traffic congestions, and have a positive impact on the environment (NHTSA 2016). • V2C (vehicle-to-cloud): The technology exchanges information about, and for applications, of the vehicle with a cloud system. This allows the vehicle to use information from others through cloud-connected industries such as energy, transportation, and smart homes and make use of the IoTs (ABI Research 2015). • V2P (vehicle-to-pedestrian): The technology senses information about its environment and communicates it to other vehicles, infrastructure, and personal mobile devices. This enables the vehicle to communicate with pedestrians with the aim of improving safety and mobility on the road (DOT 2019b). • V2X (vehicle-to-everything): This technology interconnects all types of vehicles and infrastructure systems with roadside and handheld devices. This connectivity includes cars, highways, ships, trains, and airplanes. Two devices play a key role in disseminating and receiving as well as exchanging data between vehicles and infrastructures: onboard units (OBUs) and roadside units (RSUs). OBUs provide access to vehicles and drivers to participate in the V2X environment, allowing data transmission in wireless communication between vehicles and V2X equipped infrastructure. The RSUs serve as the demarcation components between vehicles, other mobile devices, and existing

Source: DOT (2017).

Figure 2-1.  Illustration of the CV concept.

CV platooning uses cooperative adaptive cruise control to improve traffic flow stability

CV can help to prevent crashes at busy intersections

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traffic equipment and convey traffic or traveler information to passing drivers or to connected management equipment (NHI 2020). To enable safety, mobility, environmental, and road weather benefits, such a CV network is envisioned on a large scale, enabling not only V2X connection but also data exchanges between roadside equipment and management centers. For safetycritical applications, the DOT CV program utilizes the FCC-licensed spectrum for dedicated short-range communications, which is the Federal Communications Commission (FCC)-granted 5.9 GHz band with very low latency. Cellular V2X (C-V2X) will also play a role sometime in the future (but not for safety applications). Currently, the cellular communication is 3G or 4G technology. Some original equipment manufacturers (OEMs) use CV technology to refer to the fact that their infotainment or vehicle tracking systems communicate with their corporate cloud. As discussed in Chapter 1 (Section 1.3.6), without any mandate policy or rule changes from the government to allow the use of the dedicated frequency spectrum by C-V2X applications, it will be difficult to make the application effective or even deploy it nationwide. It will be more consistent with a path to 5G network convergence and the broader public interest to use a combination of unlicensed spectrum and bands other than 5.9 GHz (Calabrese and Nasr 2020).

2.1.3  Autonomous Vehicle Any discussion on autonomous vehicles (AVs) has the potential for misunderstanding because of the confusing mixture of nomenclature that has developed alongside this technology. Language is pliable and adaptable as users collectively determine its proper use for communicating a specific concept. With AVs, the debate currently revolves around the use of autonomous versus automated, and then as presented in Section 2.1.1, further segmentation is provided to help steer and clarify meaning. For the sake of argument, this section will refer to autonomous and automated vehicles interchangeably because the preferred term used in the technology industry is autonomous, whereas the preferred term used in government is automated, but, in fact, these are the same technologies. Some operation of the AVs occurs without direct driver input to control the steering, acceleration, and/or braking and is designed so that the driver is not expected to constantly monitor the roadway while operating in self-driving mode (NHI 2020). AVs may combine sensor and map data, can detect and classify objects in their surroundings, and may predict how they are likely to behave. For example, these objects include other moving vehicles, pedestrians and cyclists, and stationary objects (e.g., signs, trees, traffic cones) (NHI 2020). AVs are, in essence, the vehicles that drive without inputs from a human being. In other words, this means that they perform all activities necessary to perceive their environment, make decisions based on all available data, and actuate their control mechanisms, which are essentially the throttle, brake, and steering. This description is simple to articulate, and yet its complexity can expand, seemingly without bound, with each level of detail. For example, just the word “perceive” leads to very complex and abstract concepts, and the practitioner developing software for an AV can quickly find themselves in the realm of philosophy instead of computer science or engineering. It is easy to become enamored with the physical components of an

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AV, which outwardly are largely represented by sensors; however, the sensors are not where the magic of AV perception and decision-making occur. Rather, they are just the first-level gateway for encoding the physical environment, which is necessarily limited by the physics of the sensor itself. The magic actually occurs in the software modules that decode this perception data, fuse it with other realtime, historical, and predicted data, to then produce an output that is consistent with the vehicle’s goal, directive, or intent. Although the development of AV-supported software has always had a component of artificial intelligence and machine learning, over the past decade, developers have relied increasingly on the use of neural networks (NNs) and deep learning techniques, which by their very construction, are inscrutable, even with little support from mechanisms. So, the inputs and the results of a software process based on NNs may be known, but the process by which the results were attained cannot be. This has also given rise to another branch in computer science called explainable artificial intelligence (XAI), which, as the name implies, allows the decision process to be scrutinized. The challenge for vehicle OEMs who are trying to commercialize this technology, and for regulating bodies like NHTSA, is to prove, to themselves and the public, that these vehicles are safe. A number of efforts are underway within the industry, and in partnership with standards bodies and regulatory bodies, to develop a shared taxonomy for how to even discuss this topic, as well as policies and procedures for testing and verification of these complex systems.

2.1.4  Cooperative Vehicles with Automation Cooperative vehicles are the next logical step in the evolution of these systems, with higher levels of autonomy and greater levels of connectedness to one another and to other infrastructure-based devices, but as has been discussed previously, even the terminology can cause some confusion. The term cooperative implies shared intent, which goes beyond just simply being in communication with other devices. Cooperative behaviors, then, are behaviors that enable two or more devices to coordinate their operation toward some mutual benefit. DOT released the third iteration of their AV guidance, called Autonomous Vehicles 3.0 (AV 3.0), in October of 2018 (DOT 2018) and AV 4.0 in December 2019 (DOT 2019c), and for the first time explicitly mentioned cooperative automation within the context of highly automated, connected vehicles coordinating their actions with shared intent, and listed a number of exemplary use cases such as vehicle platooning, speed harmonization, and coordinated intersection flow. As DOT and state DOTs begin work to understand the impact that CV/AV technologies will have on the transportation system, the concept of cooperative vehicle applications will become more critical for implementation. AVs will become the main subset of the future cooperative automation applications, which may include (NHI 2020) the following: • Vehicle platooning, • Speed harmonization,

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• Cooperative lane change and merge functions, and • Coordination of a signalized intersection approach and departure. These use cases are just the tip of the iceberg for what can be accomplished via cooperative vehicle behaviors. One area where this idea has taken early roots is in vehicle platooning, where the vehicles are class VIII trucks (DOT 2019d). The term platooning, like many of the terms in this area, can have different meanings to different people. DOT uses platooning to mean two or more vehicles that are in low-latency communication sharing position, speed, and even brake position data to travel very close together. For large trucks, the concept of aerodynamic efficiency operates, whereby if one vehicle can travel close enough to a lead vehicle, then the air passes over both vehicles as if they were a single, long vehicle. This reduces the drag for the following vehicle(s) and, thus, improves fuel economy, which over hundreds or thousands of miles driven can add up to significant savings. Another, more general-purpose, use for the platoon is simply any group of vehicles that are traveling together as a unit, without any specifications about their following distance. However, the benefits of cooperative vehicle movements are often understated or unrecognized. Without cooperation, a traffic system consisting of significant percentages of highly automated and/or connected vehicles might be worse than the systems we have today, in terms of congestion and overall efficiency. The technologies for AV and CV are also sufficiently siloed that often even within the same company, a vehicle OEM, for example, these two groups are not well integrated, or integrated at all. AVs will become just another agent in our transportation system, and if they are not able to communicate effectively with other vehicles and with infrastructure devices, their efficacy will be significantly diminished, and the transportation system as a whole will be negatively impacted.

2.1.5  Autonomous Shuttle With the automation in the vehicles being widely developed and the mad race is to address the human error factor in causing accidents, several methods and technologies have been put forward (Popple 2019; Lutin 2018). The increase of traffic more with the number of cars, a level of saturation has to be addressed by public transportation services. Several options such as the high-speed rail, subway, and transit system are available, which are expected to reduce the number of cars moving on roads, although the scope and efficiency of the public transport services can be taken advantage of only when there is sufficient connectivity and when there are other factors favoring these services. Because of the first-and-lastmile connectivity to transit systems, walking and biking may be a viable option as a connector for the transit routes that serve the neighborhood or a county. However, in a bigger picture, this may not be a satisfactory solution to a large-scale service for intercity connections. Many private companies have attempted to provide shared information and service apps to make car ridesharing and taxi service more reachable, but they are still serving only a small portion of the public. This is still a challenge to keep

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the service profitable (Lutin 2018). To improve efficiency either in terms of transit service or in terms of addressing the problem of first-and-last-mile connectivity, autonomous shuttle (AS) or autonomous transit (AT) can be a better solution (Dong et al. 2019, Lutin 2018). This can make the transit service more attractive and easier to use to riders, considering the financial cost and safety as well. As discussed previously, the AVs are capable of sensing the environment and moving safely with little or no human input. These vehicles have a combination of sensors to perceive their surroundings. These sensors are GPS sensor, LiDAR, RADAR, cameras, distance estimator, and other onboard equipment to communicate with the roadside infrastructure and to the other vehicles. As a pioneer and specialist in the AV market, NAVYA, a France-based corporation, which is currently in use in various sites across the world, offers mobility solutions to assist cities and private sites in improving their transport offers with AV and shared/electric technologies. Figure 2-2 shows a typical AS designed and manufactured by NAVYA. Figure 2-3 illustrates the Autonom technology that

Figure 2-2.  Exemplary AS made by NAVYA. Source: Navya Autonom Shuttle Evo. Courtesy of Navya.

Figure 2-3.  Illustration of NAVYA’s Autonom Shuttle Evo technology. Source: Courtesy of Navya.

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allows NAVYA vehicles to operate without a driver on board, while enabling the vehicles to locate, analyze, and interact with the environment in real time.

2.1.5.1  Operation Design Domain Entities are encouraged to define and document the operational design domain (ODD) for each automated driving systems (ADS) available on their vehicle(s) as tested or deployed for use on public roadways, as well as document the process and procedure for assessment, testing, and validation of ADS functionality with the prescribed ODD (NHTSA 2017). The ODD is the definition of where (roadway types and speeds) and when (light condition, weather limits, etc.) a system is designed to operate. The ODD will include the following information at a minimum to define each systems’ capability limits/boundaries: • Roadway types (interstate, local, etc.) on which the vehicle is intended to operate safely, • Geographic area (city, mountain, desert, etc.), • Speed range, • Environmental conditions in which the vehicle will operate (weather, daytime/night-time, etc.), and • Other domain constraints. A vehicle should be able to operate safely within the ODD for which it is designed, in situations where the systems are outside of its defined ODD, or in which conditions dynamically change to fall outside of the systems’ ODD.

2.1.5.2  Deployment of Autonomous Vehicles/Shuttles With the cities and the surrounding suburban regions becoming densely populated, there is a necessity to facilitate the movement of people in an efficient, safe, and environmentally friendly manner. As most of the highway sections of the cities are reaching levels of saturation, expansion of roads will not be a realistic proposition. Currently, public mass transit, which can accommodate more passengers but consume fewer resources, may end up costing less for municipal officials. Although no fully automated bus as a commercial entity is available at present, some test operations are underway with small-scale deployment. For example, a prototype was developed through projects funded by grants from the Federal Transit Administration (Pessaro 2016). Table 2-2 lists typical examples of transit bus automation demonstrations and Pilot projects, which have been funded and managed by the Federal Transit Administration (FTA), as updated on August 18, 2021 (FTA 2021). Automated vehicle technologies have been piloted in the United States, to more specifically on public transits as individuals or partnering with transportation companies. For example, an autonomous Mobility-as-a-Service (MaaS) provider, Beep that is headquartered in Orlando, Florida, offers fully managed mobility solutions to both public and private communities and communities in controlled speed, geofenced areas as proof points for the safe testing of AVs on public roads.

Operation of an on-demand service for ambulatory paratransit users and seniors aged 65 and over for Valley Metro’s RideChoice program using Waymo’s Chrysler Pacifica models. Project partners include Arizona State University and Waymo. Operation of automated vehicles through three real-world demonstration projects (a lowspeed automated shuttle service in the Roosmoor community, an on-demand, wheelchair-accessible automated shuttle service to a regional medical center, and installation of infrastructure along a two-mile segment of the I-680 corridor). Project partners include County Connection, Verizon, AAA, Nissan, and Local Motors. Operation of an automated bus in Mahoning Valley, OH, and Santa Clara Valley, CA using two purpose-built, common-specification prototype accessible automated electric vehicles. Project partners include WRTA and CALSTART. Operation of a Local Motors Olli low-speed automated shuttle on the VA Palo Alto Health Care System campus.

Valley Metro Regional Public Transportation Authority (Valley Metro)

Santa Clara Valley Transportation Authority (VTA)

Santa Clara Valley Transportation Authority (VTA)

Contra Costa Transportation Authority (CCTA)

Project description

Lead agency

Santa Clara County, CA

Santa Clara County, CA

Contra Costa County, CA

Phoenix Metropolitan Area, AZ

Location

Table 2-2.  Transit Bus Automation Demonstrations and Pilots Funded and Managed by FTA.

In Operation

In Planning

In Planning

Completed

Status

$845K CMAQ Grant

$2.3M AIM Grant (shared with WRTA)

$7.5M ADS Demo Grant

$250K MOD Grant

Grant funding

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University of Iowa

Jacksonville Transportation Authority (JTA)

Connecticut Department of Transportation (CTDOT)

Access Services of LA

Operation of an automated Dodge ProMaster van along a three-mile corridor to provide shuttle service between a VA hospital in Westwood and an LA Metro light rail station. Project partners include LA City DOT, City of Culver Transportation Department, and Santa Monica’s Big Blue Bus. Operation of bus rapid transit service with three electric New Flyer Xcelsior CHARGE 40-foot buses on the CTfastrak dedicated busway (9 miles, 11 stations, 5 intersections) with applications including bus platooning and precision docking. Project partners include CTE, New Flyer, Robotic Research, the University of Connecticut, and the Capitol Region Council of Governments. Operation of automated vehicles and other ITS systems along Bay Street in Jacksonville. The 15 vehicles to be used for the project are to be determined. Operation of an automated Starlite Transit bus (Ford Transit 350 HD Cutaway Cab chassis) on a 47-mile rural fixed-route loop from Iowa City through rural areas and small towns. Project partners include the University of Iowa, Iowa DOT, AutonomouStuff, and Mandli Communications. Johnson County, IA

Jacksonville, FL

New Britain and Hartford, CT

Los Angeles County, CA

In Planning

In Planning

In Planning

In Planning

(Continued)

$7.0M ADS Demo Grant

$25.0M BUILD Grant

$2.0M IMI Grant

$120K STAR Strategic Partner

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Lane Transit District (LTD)

Lane County, OR

Mahoning County, OH

New York Metropolitan Area (NY and NJ)

Operation of automated motor coaches in the Lincoln Tunnel Exclusive Bus Lane (XBL), with applications including lateral lane-keeping and bus platooning. Project partners include New Jersey DOT, New Jersey Turnpike, New Jersey Transit, Robotic Research, Southwest Research Institute, Coach USA, and Greyhound. Operation of an automated bus in Mahoning Valley, OH, and Santa Clara Valley, CA using two purpose-built, common-specification prototype accessible automated electric vehicles. Project partners include Santa Clara VTA and CALSTART. Operation of an automated 60-foot articulated New Flyer bus on a 1.5-mile segment of LTD’s Emerald Express Bus Rapid Transit route, with applications including lateral lane-keeping and precision docking.

Western Reserve Transit Authority (WRTA)

Las Vegas, NV

Operation of an automated circulator shuttle in the Las Vegas Medical District. The vehicles to be used for the project are to be determined.

Regional Transportation Commission (RTC) of Southern Nevada Port Authority of New York and New Jersey (PANYNJ)

Location

Project description

Lead agency

Completed

In Planning

In Planning

In Planning

Status

Table 2-2.  Transit Bus Automation Demonstrations and Pilots Funded and Managed by FTA. (Continued)

$1.9M VAA Demo

$2.3M AIM Grant (Shared with VTA)

$250K STAR Strategic Partner

$5.3M BUILD Grant

Grant funding

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Source: FTA 2021.

Pierce Transit

City of Arlington, TX

Metropolitan Transit Authority of Harris County (METRO)

Operation of an EZ ZEUS, an electric, wheelchairaccessible cutaway bus, on a fixed-route connecting Texas Southern University, University of Houston, and Houston’s Third Ward, with connections to Metro buses and light rail. Project partners include AECOM, Phoenix Motorcars, and EasyMile. Operation of four automated Lexus RX 450h SUVs and one automated wheelchairaccessible Polaris GEM e6 in an on-demand shared ride service in downtown Arlington and the University of Texas at Arlington (UTA) campus. Project partners include Via Transportation, May Mobility, and UTA. Operation of 30 New Flyer transit buses equipped with DCS Technologies’ automated emergency braking Pedestrian Avoidance Safety System (PASS). Pierce County, WA

Arlington, TX

Harris County, TX

In Operation

In Operation

In Planning

$1.6M SRD Grant

$1.7M IMI Grant

$1.5M AIM Grant

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Figure 2-4.  Illustration of multicity deployments of NAVYA’s AS in Florida. Source: Courtesy of Navya and Beep.

Beep is currently operating as the largest and longest AV mobility network at a single site. Based on the information provided from NAVYA, a manufacturer of autonomous transit vehicles, Figure 2-4 illustrates multicity deployments of NAVYA’s ASs managed and operated by Beep in Florida. These exemplary deployments include the following: • Jacksonville, Florida: Public and private partnership with the Jacksonville Transit Authority to support rigorous testing of AVs. • Orlando, Florida: The largest and longest AV network at one location in the country. • St. Petersburg, Florida: First-and-last-mile solution along a redeveloped waterfront and tourist corridor. • Tampa, Florida: First-and-last-mile solution provides increased connectivity to more than a dozen traditional bus routes and access to businesses, restaurants, and retail. • Port St. Lucie, Florida: Connects various residential areas to the goods and services across this 15 mi2 community. Although technology can support a full-size public transit, concerns persist over these vehicles’ interaction with human-driven vehicles and pedestrians, which is discussed in a later section.

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2.1.5.3  Autonomous Shuttle as Micro Transit To enhance public transit services such as buses, light rails, and subways, the connection from the neighborhood to these stations is a huge gap for the public to access, that is, the first-and-last-mile problem. A possible solution to this problem can be found in the form of an autonomous micro transit (AMT). The requirement for passenger capacity in an AMT and the average speed of the vehicle should be very low to ensure the safety of both pedestrians and passengers. The infrastructure required for such vehicles would be minimal with only the roadside equipment sending information about signal timing. Pilot programs initiated to test such vehicles have already given good results, and, therefore, they can move into the real-life environment regardless of the infrastructure facility in them. In several locations across world and in the United States, these pilot programs are fully functional, and already several agencies have released ASs fully into cities and private site regions. Agencies such as Navya, Transdev, and Easymile have a huge network of vehicles currently running as micro transits. Special mention must be made about the services currently in operation, and these are Navya Autonom Shuttle at the University of Michigan and Easymile EZ10 in Tagel, Berlin. As indicated in Figure 2-4, the first-and-last-mile solution through using NAVYA’s autonomous vehicles in Tampa, Florida, provides increased connectivity to a good number of traditional bus routes and also access to businesses, restaurants, and retail.

2.2  KEY SUPPORTIVE SYSTEMS OF CONNECTED VEHICLES Some technological systems are available that can be used to support CVs’ functionality. These systems are often utilized collectively to maximize their benefits, as illustrated in Figure 2-5. To better understand CVs’ collective functionality, the existing CV-supported systems are categorized into a CV-aided safety system, mobility system, and environment system (Liu 2016).

2.2.1  Safety Systems Among the CV-based safety systems reported by NHTSA (Carter 2005), the blind spot/lane change warning system, emergency electronic brake lights (EEBL) warning system, and forward collision warning (FCW) system are among those that might influence the behavior of freeway drivers. Particularly, the blind spot/lane change warning system sends warning messages if the subject driver is about to make a lane change, but there are vehicles in her or his blind spots. The EEBL systems enable a vehicle to broadcast a self-generated emergency brake event to surrounding vehicles. On receiving the event information, the onboard device in the receiving vehicle determines the relevance of the event. If the event poses a potential risk to the subject driver, it provides a warning to the driver to avoid a crash. The concept of the EEBL is visualized in Figure 2-6. This system is

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Figure 2-5.  Example of a collective CV-supported system application. Source: Rupert (2016).

Figure 2-6.  Concept of EEBL. Source: DOT (2016).

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Figure 2-7.  Concept of FCW. Source: DOT (2016).

particularly useful when the driver’s line of sight is obstructed by other vehicles or adverse weather conditions (Iteris 2015). Existing studies emphasize on the development of EEBL algorithms and methods for determining the relevance of the event to the subject drivers (Szczurek et al. 2012). More researchers have started field or simulator tests for identifying the behavioral impact of the system. An FCW system is designed to aid the driver in avoiding or mitigating rearend collisions with the leading vehicles through driver notification or warning messages (CAMP VSCC 2005). The concept of FCW is visualized in Figure 2-7. The existing FCW systems apply different criteria to determine the timing for releasing the warning message. The most common criterion adopts a threshold following distance. If the actual following distance is found to be shorter than the threshold distance, the system will start sending drivers the warning message until the following distance becomes larger than the threshold again (Brunson et al. 2002, Willemsen et al. 2010, McGehee et al. 2002). Alternatively, the threshold time headway (Adell et al. 2011) and threshold time-to-collision (Yan et al. 2015) are used to define warning timings. It has been found that the FCW system can reduce the driver’s reaction time and increase the deceleration rate (Adell et al. 2011, Yan et al. 2015). FCW is perhaps the most tested safety CV-supported system so far because of its great potential benefits in reducing rear-end crashes.

2.2.2  Mobility Systems McGurrin et al. (2012) classified high-priority mobility systems into seven bundles. Among them, the intelligent network flow optimization (INFLO)

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seeks to optimize network flow on freeway and arterials by informing motorists of existing and impending queues and bottlenecks; providing target speeds by location and lane; and allowing the capability to form ad hoc platoons of uniform speed. The bundle is comprised of three systems: dynamic speed harmonization (SPD-HARM), queue warning (Q-WARN), and CACC. The objective of SPD-HARM is to dynamically adjust and coordinate speed limits in response to downstream congestion, with the purpose of reducing vehicle crashes and improving mobility in terms of maximizing traffic throughput and reducing the formulation of stop-and-go waves (Chen and Rakha 2015). Such an objective is achieved through V2I communications. When the roadside infrastructure detects a traffic breakdown, it computes an advisory speed based on the detected traffic condition and sends the advisory speed to the upstream drivers. Because the advisory speed is lower than their original speeds, the upstream drivers will eventually reduce their speeds, and as an aggregated effect, the inflow from the upstream road segment to the congested freeway area will be reduced. Consequently, the traffic congestion downstream will dissipate quicker than cases without the SPD-HARM, and the number, duration, and propagation speed of shockwaves will be greatly decreased. For individual drivers, their travel time through the congested road segment will be reduced, and the fuel efficiency of their vehicles will be improved. Algorithms of SPD-HARM are often developed based on the existing variable speed limit (VSL) algorithms. Examples of VSL algorithms can be found in Hellinga and Mandelzys (2011), Hegyi and Hoogendoorn (2010), and Carlson et al. (2011). Compared with nonequipped drivers, drivers affected by SPD-HARM drive slower within the congested freeway segment with a smaller deceleration rate and more consistent speed with the surrounding traffic (Lu and Shladover 2014). The Q-WARN systems utilizes the V2I and V2V communications to enable vehicles at the end of a queue to automatically broadcast their queued status information (e.g., rapid deceleration and lane location) to nearby upstream vehicles and infrastructure-based central entities (such as the Traffic Management Center). The objective of Q-WARN is to minimize or prevent rear-end collisions that often happen at the queue end (Iteris 2015). Besides safety benefits, Q-WARN can also reduce the number of shockwaves formed and the length and duration of formed queues (DOT 2012). In practice, Q-WARN is usually used to send a warning message to upstream drivers in a 3 to 6 mi range (Pesti et al. 2013). Many drivers will move to alternate routes after receiving the message. It can, therefore, be adopted as a traffic demand management method as well. The concepts of SPDHARM and Q-WARN are illustrated in Figure 2-8. Also, CV mobility systems have been developed but not included in the INFLO bundle. For example, the freeway merging assistance system provides lane-changing advisory messages to drivers in the freeway shoulder lane if there is incoming on-ramp traffic and recommends them to change to the middle lane whenever possible. As a result, extra gaps will be created in the shoulder lane so that the on-ramp drivers can merge into the freeway mainline more easily. In the

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Figure 2-8.  Concept of SPD-HARM and Q-WARN. Source: DOT (2016).

meantime, the mainstream drivers can avoid disturbances caused by the merging traffic (Park et al. 2011, Park and Smith 2012). A similar system is used for work-zone traffic organizations. The work-zone merge system reported by Zeng et al. (2012) is designed to harmonize the workzone merging traffic flows, with the objective of minimizing traffic incidents and maximizing operational efficiencies. This V2I system monitors traffic conditions at the work-zone area and sends upstream drivers merging instructions based on the work-zone traffic conditions and the target vehicle’s operating characteristics. If the work-zone traffic is a free-flowing one, the system sends an early merge message (e.g., at 2 mi upstream from the work zone) to encourage upstream drivers to make lane changes from the closed lane(s) to the open ones. When the traffic flow is transiting from free flow to congested flow, the system recommends some vehicles (e.g., heavy-duty vehicles) to make the early merge and the rest of the vehicles to make the late merge at the place of lane drop. Once the traffic breaks down at the work zone, the system sends the late merge message to all drivers. This strategy maximizes the queue storage capacity by using all lanes. The work-zone merge system can influence drivers’ lane-changing behaviors in a way that the lane changes may cause fewer disturbances to the traffic flow, which contrasts with a scenario without this system. The CACC system is configured by combining the V2V communications to an adaptive cruise control (ACC) system, which then turns into a cooperative ACC (i.e., CACC) system. The CACC enables vehicle drivers to adjust their speed to the preceding vehicle in their lane and also responds more quickly to speed changes by the preceding vehicle and other vehicles farther ahead that are beyond the line of sight (Milanes and Shladover 2014, CPATT 2019), as illustrated in Figure 2-9. In other words, the CACC can allow vehicles to cooperate by communicating with

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Figure 2-9.  Illustrated concept of a CACC system. Source: Lu and Shladover (2018).

one another, whereas in the ACC mode, so as to lead to more closely, accurately, and safely movements following the vehicle ahead, with braking and accelerating done cooperatively and synchronously. The CACC experimental tests of four vehicles at the California Partners for Advanced Transportation Technology (CPATT 2019), sponsored by Nissan Motor, showed great improvement in cars following stability compared with the same four vehicles using their production ACC controllers without V2V. By using the CACC system, the stability of traffic flow can be improved, driver confidence can be increased, and possibly shorter vehicle-following distances can be created. As a result, a highway’s effective capacity can be ultimately better used while achieving greater fuel efficiency (FHWA 2016).

2.2.3  Environment Systems The Connected Eco-Driving system is the most important CV environment system. DOT (2016) defined the concept of the system using the eco approach and departure at signalized intersections as an example, as illustrated in Figure 2-10. Eco-driving algorithms have two branches. In the first branch, the algorithm is implemented by the equipped vehicles to optimize their engine efficiency as well as maintain a predefined average speed (Mensing et al. 2014). In the other branch, the connected eco-driving provides customized real-time driving advice to drivers so that they can adjust their drivers’ behavior to save fuel and reduce emissions (Iteris 2015). For example, the algorithms developed in Barth and Boriboonsomsin (2009) and Yang and Jin (2014) utilize the V2I communications to obtain the traffic condition downstream from the subject vehicle. Based on the traffic flow status, an advisory speed is computed and sent to the driver. The driver then adjusts the speed in the hope of encountering less acceleration and deceleration cycles and reducing fuel consumption.

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Figure 2-10.  Concept of Eco-driving. Source: DOT (2016).

2.3  KEY DESIGN ELEMENTS OF AUTONOMOUS VEHICLES The major components that make up the software of an AV are “perception,” “navigation,” “localization,” and “command and control (C&C),” which is often referred to as the drive-by-wire system. Equally important, however, and much less publicized are the “health monitoring,” “behavior architecture,” and “world model” systems, along with countless others depending on the specific design. Understanding what these systems are, what they do and do not do, and how they interact are critical for understanding “what” an AV does, and especially “why” it does it. Also, these systems are increasingly complex, even without NNs, such that their developers spend a great deal of time understanding what an AV is doing and why it is doing it. Briefly, we discuss each of these major components, and where some common pitfalls exist for developers, which will also highlight the challenge ahead for vehicle OEMs and regulators like NHTSA to provide assurances that these systems are “safe.”

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2.3.1 Perception An AV’s “perception” element is designed to “see” driving and surrounding conditions by using sensors to monitor its environment (GM 2018). The sensor data with highdefinition map data are combined by the computer to localize the vehicle. Sensorenabled perception subsystem detects and classifies objects in three dimensions, determines their location, and provides their speed and direction. Perception can also predict the objects’ next motion, with the capability of identifying varied objects such as pedestrians and trucks in different predicted movements. This subsystem takes in data on the external environment as well as internal states of the vehicle, decodes the data that have been encoded by a variety of sensors on a variety of timescales, organizes the data into patterns, and provides the processed data to other software modules involved in decision-making. The perception subsystem is also a collection of systems, and the term perception pipeline is often used to refer to a collection of perception modules that operate in sequence. The perception pipeline contains processes that segment individual objects out of the raw data, localize the objects relative to the ego vehicle, track the objects over time, and, when possible, identify or classify the objects. Object classification is important because other modules in the AV software stack can then access known behaviors of an object class and combine that with information about the current environment to make predictions about the object’s near- and longterm movement possibilities. As an example, a telephone pole and a person look very similar to certain sensors, but these two objects have very different potential future behaviors and very different implications to the vehicle itself. Data from other sensor types must be combined to distinguish one from the other and make an accurate prediction about its future state. To perform perception functions, for example, General Motors (GM) self-driving vehicles have five LiDARs, 16 cameras, and 21 radars. Figure 2-11(a) illustrates the key detection components for an AV (CRS 2021). Based on Figure 2-11(a) and Bronzi et al.’s (2016) explanations about the perception functions provided by an AV detection system, Figure 2-11(b) illustrates the perception detection technologies in support of an AV. The LiDARs, radars, and cameras all scan both long and short ranges with views of 360 degrees around the vehicle. The LiDAR provides highly precise feedback using laser measurements for both fixed and moving objects. Radar is complementary to LiDAR; it uses electromagnetic pulse measurements to see solid objects that have low light reflectivity. Both LiDAR and radar provide inputs for measuring the speed of moving objects, allowing quick, confident determinations of speed. Cameras are also complementary to LiDAR by measuring the light intensity reflected off or emitted from objects, providing rich details of the object. The LiDAR and camera data are combined to classify and track objects, making high-confidence determinations more quickly. This function helps identify pedestrians, vehicle types, and road details (e.g., lane lines, construction zones, and signage). The complementary set of long-range sensors track high-speed objects (e.g., oncoming vehicles), and the short-range sensors provide details about moving objects near the vehicle (e.g., pedestrians and bicycles) (GM 2018).

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Figure 2-11.  Illustration of the perception technologies of autonomous vehicles.

2.3.2 Navigation This subsystem provides a macro plan (route) for the vehicle to follow as it traverses through its environment, taking in both raw data from sensors and processed data perception modules, as well as any information that will change the macro plan, such as a message received via the communications system. The terms often associated with this subsystem are route planning and path planning; however, as with other terms, the term path is often used interchangeably with route, which causes confusion because the AV is doing path planning constantly, which actually refers to its very near-term path and alternate paths that are under consideration. A route can be thought of as the macro plan that the vehicle will follow and its path as a precise spatial description of its movement.

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As mentioned previously, the planning function determines the desired vehicle behavior (GM 2018) by choosing routes to optimize efficiency and safety from trip origin to destination. The primary factors influencing the process include the vehicle location, other road users’ predicted actions, traffic controls, road markings, rules of the road, and other external factors. Alternate route should be planned at the same time in case something unexpected happens. Another function, called controls function, implements the final path from planning, converting its commands for the actuators that control the steering, throttle, and brake and drive unit (GM 2018). For example, GM’s Cruise selfdriving vehicle has been designed with the controls function to give the selfdriving system full vehicle maneuverability, with stability, traction, and antilock brake systems being fully active.

2.3.3 Localization This system is concerned with the micromovements of the vehicle, to understand exactly how the vehicle is moving through space and time. This system is critical for tracking the movement of the vehicle as it moves through its environment so that movement can be compared against the target values of position and speed. It is also critical to know the precise movement of the vehicle and its real-time pitch, yaw, and roll values, as this motion, called ego motion, must be subtracted from the data being sent by the vehicle’s sensors. These positional transforms must be made relative to a common datum in the vehicle, usually the center of the rear axle; otherwise, the sensor data are useless.

2.3.4  Command and Control This system operates on the lowest timescale (0.1 to 0.001 s) and is the most critical to have certain safety checks and redundancy built in. All processing of sensor data, navigation plans, and so on culminate at the level of the C&C system and change the values for the steering angle, the throttle value, or the brake value.

2.3.5  Health Monitoring This subsystem is comprised of dozens or hundreds of simple software modules that do one thing, which is to monitor a single process in the AV software stack, and if this process shuts down or exceeds some threshold value, it is the monitor’s job to notice this change and take some specific action. This action might be to restart the process, enter data into a log, provide a warning to other modules or a user, and so on.

2.3.6  Behavior Architecture This is the most important subsystem in an AV but is often overlooked and rarely mentioned. Even if the behavior architecture of an AV is not an explicit subsystem with inputs/outputs, it still rules the behavior of the entire vehicle. This is the subsystem that determines if an AV is a robotaxi operating on city streets or is a

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resupply and extraction vehicle operating in an active combat zone. For AVs that operate on public roads, the behavior architecture will help determine how the AV drives in lanes, changes lanes, cooperates with other vehicles, yields right of way to pedestrians, navigates intersections, and so on.

2.3.7  World Model This is a catch-all subsystem and can take many forms, but it is essentially the collection of knowledge that the AV has about its environment, the other entities in the environment, and its role in this environment. Again, as a vehicle operating on city roads, this system might store information about laws that govern how a vehicle operates, rules about right-of-way at four-way stop intersections, and so on. The specific sensors that are used on an AV depend on a variety of factors, including the required field of view (FOV) surrounding the vehicle and the spatial resolution required. Because no single sensor can satisfy all the FOV and resolution requirements, a variety of sensor types are used, and just as critical as the type of sensor used is its placement on the vehicle, which will also determine with which other sensors its FOV overlaps. For close-in object detection, ultrasound sensors are often used as they provide highly reliable yes/no detection of objects at distances up to a few feet; however, they cannot provide high enough resolution for object classification. Radar, LiDAR, and cameras are the other primary sensors deployed on AVs, depending on their design, and each of which has its own list of strengths and weaknesses, but when combined, create the best combination to provide a 360-degree situational awareness for the AV.

2.3.8  Advantages of Lower Levels of Automated Driving From past accident reports (NHTSA 2016), about 94% of accidents can be attributed to human error, either from distraction, poor judgement, or impairment, which can and are being addressed by lower levels of automated driving technologies called advanced driver assistance systems (ADAS). One of the market sectors expected to benefit greatly from automated vehicle technologies is transit, including low-speed shuttles. Specifically, certain ADAS features are expected to increase the safety of human-operated transit vehicles, such as collision avoidance features like automated emergency braking (AEB), and driver assist features like lane keep assist (LKA). Other approaches for accelerating the adoption of automated vehicle technologies in the transit market sector include cooperative driving automation (CDA) features such as platooning, and even adding managed lanes for properly equipped vehicles.

2.3.8.1  Collision Avoidance and Emergency Braking From the National Transit Database, about 85,391 buses and vanpools were involved in collisions that resulted in 1,340 fatalities and 201,382 injuries between 2002 and 2014 (Lutin 2016) and costed about $5.7 billion. The average cost per year per transit bus for casualty and liability expenses is around $6,600. From a study

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conducted by the Washington State Transit Insurance Pool, Spears et al. (2017) finding pointed out that 65% of $53 million in bus claims during 2002 and 2014 could be prevented by using the collision avoidance and automatic emergency braking systems, resulting in fewer collisions and fatalities, injuries, and any incurred insurance costs.

2.3.8.2  Steering and Lane Keeping Active safety features like LKA have been successfully demonstrated to allow transit vehicles to operate safely and reliably on narrow freeway shoulders. A pilot project by Minnesota Valley Transit has tested this technology, which resulted in saving travel time, and, therefore, has been recommended as an alternative to light rail.

2.3.8.3  Bus Platooning One of the automation technologies being considered for the transit sector is cooperative driving automation (CDA) defined in the Society of Automotive Engineers (SAE) publication J3216. One of the applications of CDA is known as platooning, where two or more vehicles follow in a chain of vehicles that behaves like a single long vehicle. Depending on the implementation of the CDA, and the capabilities of the individual vehicles, the headway between vehicles can be reduced to distances that would be dangerous for human drivers to maintain. This can have the effect of increasing roadway capacity and average speed of the vehicles, which would then reduce average travel times as well. Many buses also have precision docking capabilities (Cao and Ceder 2019), which when combined with platooning, may serve as a rail-like mode at a lower cost. Thus, autonomous bus rapid transit (BRT) can be made as an alternative where light rail systems are not practical.

2.3.8.4  Managed Lanes for Automated Shuttles Low-speed automated shuttles often carry between 6 and 12 passengers and operate under 30 kmph (approximately 19 mph), which is a potential fit for the problem of first-mile / last-mile connectivity. However, interaction with human-driven vehicles at higher speeds poses a safety risk to the shuttle passengers and other roadway users. One possible solution is using managed lanes for the shuttles and other low-speed automated vehicles, at least until sufficient AV traffic is realized and more human drivers adapt to it. Additionally, a lane fully dedicated to AVs may be helpful in implementing traffic management strategies (Ma et al. 2017), even at lower market penetration levels. In this concept, one lane is dedicated to AVs to provide ease of traffic movement and to maintain high speeds with lower headways, resulting in smoother traffic flow and fewer collisions, as illustrated in Figure 2-12.

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Figure 2-12.  Conceptual illustration of dedicated AS lane. Source: Adapted from DOT (2018).

2.4  D  ISTRIBUTED LEDGER TECHNOLOGIES FOR CONNECTED AND AUTONOMOUS VEHICLE SYSTEMS Distributed ledger technology (DLT) has emerged as a potentially revolutionary approach across a variety of industries, including transportation, banking and financial (World Bank 2018), logistics, and supply-chain management. Specifically, the use of a blockchain or tangle (IOTA Foundation 2019) within a distributed ledger provides a mechanism for enabling peer-to-peer transactions and maintaining records in an immutable form that is distributed across a system of nodes and which is auditable by anyone (IBM 2018). Similar to other decentralized systems, DLT provides inherent security against attacks as well as the capability to self-heal when a part of the system is damaged, lost, or otherwise compromised, because there is no single point of failure where a successful attack might compromise the entire system. If, for example, a single node of a DLT system is removed entirely, the database it contained continues to exist across all other nodes, and if an attack attempts to modify the database at that node, the changes made will be determined to be inconsistent with the databases stored on the other nodes. The nonconforming node can then remedy this situation by replacing its database with the collectively agreed on database, or its transactions will essentially be ignored by the collective. Additional security in DLT is the use of cryptographic signatures linking transactions to one another; thus, if even a single byte is modified in any

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transaction in the ledger, a check of the integrity of the “chain” of transactions will reveal a discrepancy in the cryptographic hash function results for the modified transaction, thus invalidating the chain. Transportation systems are changing rapidly with the introduction of new technologies and modes of operation, including connected vehicles, automated vehicles, shared mobility services, and so on, and these are providing a unique opportunity for the deployment of DLT as a means for establishing transactional relationships among devices (nodes) that may or may not have an a priori trust relationship. One concept in DLT is that of the Smart Contract, which essentially is a secure multiparty workflow in which the parties need to assert the existence of transactions (Figure 2-13). For example, this concept has been applied to the short-term rental of shared vehicles in which an individual using a phone app and a crypto wallet negotiates directly with a vehicle service to secure short-term rights to use the vehicle by transferring units of virtual currency. Similarly, the use of DLT has been proposed for use in CV systems as a secure mechanism for CVs to transact certain types of data or agreements. One example might be the formation of a platoon of vehicles, in which a multiparty smart contract is established to govern the “rules” of the platoon, such as route, max/min speed, and acceptable vehicle types. In addition, ledgers of trusted devices could be kept, for example of critical infrastructure, avoiding the need to independently verify the authenticity of their messages with a central authority. In the following sections, the application of DLT for a number of use cases is explored, and in

Figure 2-13.  Smart contracts. Source: https://blockgeeks.com/guides/smart-contracts/.

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particular, how DLT is a complementary technology to AVs and CVs, and in fact, how it may be a critical enabler for these technologies to properly function.

2.4.1  An Introduction to Distributed Ledger Technology Distributed ledger technologies represent a paradigm shift in how we think about the process of conducting transactions and transferring value, and they have taken the form of transactions linked through cryptographic hash functions, as in a blockchain, or as a directed acyclic graph, as is used in the IOTA Foundation’s Tangle. DLT, in essence, is a shared and immutable ledger of transactions that is linked together in an auditable structure and can be set up as a permissioned blockchain in which the participants are known and trusted, or as a permissionless blockchain in which the participants are not necessarily known to one another prior to the transaction. This in, and of, itself may not sound too attractive, but the key concepts underlying DLT, and the ways in which it can be applied across a variety of sectors, makes DLT a game changer for many traditional industries and an enabler for new ones. First, the shared aspect means that no single copy of the ledger is the correct one, and, therefore, no single entity is the controlling entity. Each entity in the system is a node that is able to communicate peer to peer and can serve as both a publisher of information and a subscriber or consumer. Essentially, it is a decentralized system for executing and tracking transactions. Second, the way individual transactions are linked together, such as in a blockchain, uses cryptographic hash techniques, much like those used to ensure that a downloaded copy of software is identical to the original, without any concern that it has been modified somewhere between the producer and the consumer. Third, the way the ledger is distributed creates a third requirement for verifying that a transaction has occurred and is valid. This requirement is satisfied through a variety of mechanisms such as proof of work, in which nodes in the network that contain a copy of the ledger work together to solve a complex puzzle. The assumption is that the group with the most number of nodes with the same ledger will have the most computing power and, thus, will solve the puzzle first. The ledger contained within these nodes becomes the new ledger of record and is distributed to all other nodes, regardless of the information their local copy contains. One example of DLT that most people are familiar with is Bitcoin, which was created from a need to remove various barriers to financial transactions, but this is only one example of how DLT can be utilized. Today, DLT is revolutionizing traditional business networks for financial transactions, or for any transaction in which something of value is transferred among parties. Also, this value does not need to be monetary in nature; in fact, many DLT applications consider value in terms of information, or activity of devices, or groups of devices using mechanisms like smart contracts. The many data breaches we hear about, in which a central repository of information is compromised and its data stolen, is a good example for which DLT could be utilized to safeguard individuals’ personal information, and as our devices become more entwined with our lives, including devices like our vehicles, the potential for data insecurity and personal vulnerability only increases.

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2.4.2  Use of Distributed Ledger Technology in Transportation Current transportation systems produce large amounts of data through the use of sensors to detect the activity of vehicles (FHWA 2019), pedestrians, and other road users; however, much of these data are currently discarded or ignored. Also, the vehicles themselves are still largely passive actors in the system, not communicating with other vehicles or infrastructure devices. Passengers with smartphones have become the two-way communication bridge for vehicles; however, with the introduction of connected and automated vehicles into the transportation system, the requirement that vehicles play an active role will increase. As vehicle connectivity increases, the potential for exploitation of the system increases dramatically through threat vectors like over-the-air messages that provide spoofed or fraudulent information, causing the vehicle to behave erratically or dangerously. This section will examine a few examples in which DLT will not only be an asset for a more automated and CV system but may actually be a foundational requirement for secure operation. The technologies associated with CVs and AVs are converging with each other and with transportation management systems, but security and trust in the interaction among vehicles and infrastructure devices is of great concern. In addition, using the concept of smart contracts, secure and reliable transactive applications are possible, such as tolling and charging, without the need for, and vulnerability of, centralized personal data storage.

2.5 APPLICATION OF TRANSPORTATION AUTOMATION TECHNOLOGIES 2.5.1  Connected and Automated Vehicle Applications Connected vehicles are vehicles that have the ability to send and receive messages, simply put. However, what the vehicles do with the messages is of great interest as the vehicles’ behavior affects the lives of their occupants and those around them, as well as the functioning of our transportation system. If the messages coming into the vehicle have been maliciously generated, or modified in transit, the results could be catastrophic. In fact, a number of CV applications have already been envisioned, with several either under development or in some form of proof-of-concept deployment around the country (Kockelman et al. 2016a, b). It is worth discussing a couple of these in the context of DLT to see how DLT might be more than a nice-to-have feature but might actually underpin the success of the application. One application envisions CVs communicating directly with traffic signal controllers via an intermediary device for the purpose of requesting a behavior change from the controller [Figure 2-14(a)] (DOT 2015). If the request is for the controller to move to a green phase for the vehicle’s approach lane sooner than it would have otherwise, it is referred to as a priority request. If the request is for

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Figure 2-14.  (a) Connected transit signal priority; and (b) connected vehicle platooning. Sources: (a) https://www.its.dot.gov/infographs/Eco_traffic_signal.htm; and (b) Reuter (2015).

the controller to move immediately to a green phase for the vehicle’s approach lane, it is referred to as pre-emption. The former is envisioned to apply only to transit vehicles, which is communicated through the use of a flag in the message. The latter is envisioned to apply only to emergency vehicles, again communicated using a flag in the message. The current implementation of the application, however, makes no mention of how this message flag will be secured against tampering, in which just anyone can potentially trigger these behaviors. Some vehicle manufacturers are working to address this by setting this flag in hardware during manufacturing in a way that cannot be changed later; however, additional safeguards will have to be created as the message is constructed to prevent software from modifying that field of the message. This application is essentially a negotiation between devices, but whose outcome is more consequential than a more abstract transaction involving financial data or record retention. The result of this interaction can potentially change the signal phase for an intersection, which will have its own consequences, and we must ensure that the device making the request is authorized to do so. Individual intersections may also coordinate with one another, either as a peer-to-peer network or as part of a central authority, to improve vehicle movement across the system. Such adaptive signal timing can result in optimization of the flow of traffic, but if the messages flowing between the nodes are faulty or deliberately modified, the system can grind to a halt. Another application envisioned for connected and automated vehicles is that of cooperative platooning [Figure 2-14(b)], in which multiple vehicles form a team and travel as a group for some period of time. This application requires a formation step in which two or more vehicles agree to the “rules” of the platoon, which may include the target set speed, the target headway distance, allowed and disallowed

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vehicle types, and so on. Once agreed on, the vehicles can travel inline as a group, which has many benefits, especially if the vehicles are equipped with ADSs. One benefit is that the headway distance, the distance between vehicles, can be much closer than a human driver can reasonably maintain. This provides a benefit of improved aerodynamics for all but the lead vehicle and allows more vehicles to travel in a lane (increased capacity). A second benefit is that the vehicles can all travel at the same speed, controlled very tightly by the ADS control systems, which helps eliminate the “shocks” experienced with strings of human-driven vehicles. Vehicles can join and leave the platoon according to the smart contract (rules) as needed, and each of these transactions is recorded in the DL of the platoon. This may be of particular interest to freight vehicles, which can realize significant gains in fuel economy through the reduction of drag over long distances. There is some debate as to whether these types of interactions should form permissioned or permissionless blockchains, and this largely depends on how you define the nodes in this system. For example, if during manufacturing, vehicles can be imbued with immutable signatures in a way that cannot be circumvented, and these can be added to a large blockchain of “trusted” devices, then their interaction with blockchains through CV applications can be permissioned. However, if it is not possible or not technically feasible to accomplish this, then the vehicle will be an unknown node in any CV system within which it interacts, and, thus, the blockchains will need to be permissionless. Both types have their advantages and disadvantages, and it will be beneficial for researchers and practitioners in both DLT and CV to start thinking about these types of implementation issues.

2.5.2  Mobility Smart Contracts Another application that is already being explored is utilizing smart contracts to enable vehicles to conduct transactions for tolling and electric vehicle (EV) charging, as shown in Figure 2-15 (Milligan 2019). The concept is that the smart contract defines the rules by which nodes interact, like vehicles, tolling, and charging stations, and the contract need not be managed or moderated by a central

Figure 2-15.  EV Blockchain application. Source: West Monroe Partners at https://www.westmonroepartners.com/Insights/Client-SuccessStories/​EU-Large-West-Coast-IOU.

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authority. Beyond the concept of a stationary charging location is the concept of in-ground vehicle inductive charging, in which a vehicle’s battery is charged while driving. These applications allow more dynamic pricing mechanisms, and with smart contracts, can be executed with more precision, higher security, and less friction in the overall transaction. For example, a charge lane in which vehicles can charge while driving may offer smart contracts at a certain price based on dynamic demand on the system, or other factors, and vehicles can set threshold triggers such that if the price drops below a threshold, the smart contract is executed, and the vehicle is able to enter the charge lane for the price included in the smart contract. Similarly, CVs and intersections may construct smart contracts with similar pricing mechanisms for movement through a corridor. Vehicles are able to negotiate a travel time through a corridor for a price, which the intersections may set based on system demand or other factors. In fact, cities and other municipalities may set these smart contract terms for their entire network, and each vehicle that interacts with the network executes the contract based on its own threshold triggers. A vehicle, for example, that is “unwilling” to pay anything additional, may experience a significantly longer travel time through the network than a vehicle with a higher price threshold. The world of shared mobility has also been moving toward DLT and smart contracts where users’ smartphones contain an app that is linked with a digital wallet, and the negotiation for the mobility asset is conducted between the user’s phone and the asset itself. The financial transaction is then handled using DLT that includes the digital wallet, which is secure, fast, and auditable. Ride share transportation network companies may also move in this direction to build blockchains that capture “trust” scores for both drivers and passengers, with a public ledger showing all interactions from the inception of the blockchain.

2.5.3  Cooperative Driving Automation Federal Highway Administration (FHWA) under DOT has developed the CARMA Platform, an open-source software to support the research and development of the system of cooperative driving automation (CDA), which can be used to address common traffic situations and provide testing and evaluation of resulting applications (DOT 2020a). Specifically, the CARMA Platform is an ADS that allows AVs to interact and cooperate with on-road or roadside infrastructure (such as stop lights) and other vehicles via wireless communication (DOT 2020b). With the use of CARMA Cloud, CARMA Platform provides an advisory or warning message about what is ahead on the roadway in real time, including work zones, traffic incidents, and weather conditions, and enables AVs to interact and cooperate with infrastructure and other vehicles, facilitating safer and a more efficient movement of goods and services. The CDA is aimed to enable AVs to communicate among vehicles, infrastructure devices, and road users including pedestrians and cyclists, with an attempt to reduce crashes caused by human error and save lives, advance

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mobility efficiency, facilitate freight movement, increase productivity, and reduce the need for roadway facilities, thereby resulting in economic savings. Currently, CDA-related research focuses on AVs working together and with roadway infrastructure to increase safety and improve operational efficiency by reducing fuel consumption at intersections by 20%, doubling the capacity of existing lanes, and saving fuel by 10% (DOT 2020b). The initial version of the CARMA Platform was started in 2014 as an initial proof of concept. The next version was CARMA2 and was migrated to the Robot Operating System (ROS) architecture in 2018, and the third version, CARMA3, began in August 2018 using an agile software development process. The latest version of CARMA Platform is available on GitHub. This platform is open for collaboration (DOT 2020b), particularly extensible and reusable for a wide variety of research applications in terms of advancing innovation and V2V, V2I, V2P, and V2X communications. A collaboration between transportation engineers and researchers is highly encouraged by using the platform to accelerate the development, testing, and evaluation of CDA, while advancing the safety, security, data, and use of artificial intelligence in AV technologies.

2.5.4  Security Considerations Security for CVs is currently envisioned as a security credential management system (SCMS), which functions as a centralized system with a “root” authority that can issue root certificates and then with various pseudo authorities that can issue derivative pseudo certificates. Certificates must be loaded on to a CV device at the manufacturer’s end using a secure process and must be refreshed periodically at a secure location using secure processes to avoid a situation where fraudulent certificates are able to circulate in the system. This approach is beset with a number of technical and logistical challenges, which the DOT and vehicle OEMS are actively researching. DLT may be a complementary technology that could be integrated along with the SCMS to distribute the issuing and validating entity certificates, while maintaining the immutable transactional security envisioned by the SCMS. In summary, DLT provides a number of benefits for realizing secure, reliable, and immutable transactions in an arbitrarily large system of interacting nodes. The shared ledger concept, combined with cryptographic security linking transactions into an immutable chain, and shared processes for verification provide a unique framework for enabling secure transactions in a CV system. CV applications are essentially transactions among devices, which may be financial in nature, as in the case of demand-based pricing for a service or may simply be an exchange of information. Also, as the number of CVs and AVs increases in our transportation system, the potential for malicious activity and danger to the entire system increases significantly. Therefore, security for this system will need to be scalable and robust to enable the types of system-level benefits imagined.

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2.6 DRIVING AUTOMATION DEFINITION AND AUTONOMOUS VEHICLE LAWS As of January 2022, a majority of US states have enacted legislation and/or executive orders permitting AVs (Figure 2-16), and this number will be continually updated. Automated vehicles have different capabilities in terms of driving autonomy. The SAE International (2018) defines six levels of driving automation, as shown in Table 2-1. Exemplary features of different automation levels are further explained in Table 2-3. The term self-driving usually refers to Automation levels 3, 4, or 5. Vehicles with no automation belong to Level 0; it means that the human driver performs all aspects of the dynamic driving task (DDT), even when vehicles are automated by warning or intervention systems. Level 1 automation can assist drivers with either longitudinal (i.e., acceleration/deceleration) or lateral (i.e., steering) control of vehicles such as lanekeeping assistance system. Level 1 automation is also known as driver assistance. The human driver performs all remaining aspects of the DDT.

Figure 2-16.  Autonomous vehicle regulations in the United States. Source: NCSL (2020).

Operational design domain Example features

Driver

Driver

Driver

Driver

System

System

Conditional automation

Level 3

System

System

High automation

Level 4

System

System

Full automation

Level 5

Fallback-ready System System users become drivers during fallback N/A Limited Limited Limited Limited Unlimited • Automated • Adaptive • Lane • Traffic jam • Local • Same as Level emergency cruise centering chauffeur driverless taxi 4, but a braking control AND • Pedals/ feature • Blind-spot OR • Adaptive steering enabling warning • Lane cruise wheel may or everywhere in • Lane centering control may not be all conditions departure installed warning

System

Partial automation

Level 2

Driver and System

Driver assistance

No automation

Dynamic Sustained lateral Driver driving and task longitudinal (DDT) vehicle motion control Object and event Driver detection and response (OEDR) DDT fallback Driver

Level 1

Level 0

Table 2-3.  How SAE J3016 Defines Driving Autonomy.

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Level 2 vehicles can take over both longitudinal and lateral controls under certain conditions. Level 2 automation is also called partial automation, under which the driver performs the rest of the tasks such as monitoring the driving environment. Currently, most vehicles on the market can offer Level 1 or Level 2 capability. Although most ADS developers and vehicle manufacturers strive to develop Level 3 and Level 4 vehicles, most of them try to avoid offering vehicles in Automation level 3 because of liability concerns. At Level 3 automation (i.e., conditional automation), vehicles can perform all aspects of the driving task under specific conditions. However, the human driver should respond appropriately to a request to intervene. At Level 4 automation (aka, high automation), vehicles can perform all aspects of the driving task under specific conditions (e.g., a well-mapped, geofenced area) even if a human driver does not respond appropriately to a request to intervene. Level 5 is full automation, under which vehicles can perform all aspects of the driving task under all roadway and environmental conditions that can be managed by a human driver.

2.7 SUMMARY Under the categories of transportation automation levels as defined by SAE International, this chapter provides a comprehensive overview of the CV and AV technologies (or termed CAV technology in general) as well as other automationbased vehicles such as cooperative vehicle and AS. Key supportive systems for CVs are introduced in detail under the categories of safety systems, mobility systems, and environment systems. The key components of AV designs are presented in detail with multiple functions such as perception, navigation, localization, command and control, health monitoring, behavior architecture, and world model, followed by a discussion of the advantages of automated operations. In addition, the chapter presents a special technology for CAV systems, called the distributed ledger technology or DLT, which has emerged as a potentially revolutionary approach across a variety of industries, including transportation, financial, supply-chain management and logistics, and energy. The use of a cryptographic architecture such as blockchain or tangle within a distributed ledger provides a mechanism for enabling peer-to-peer transactions and maintaining records in an immutable form that is distributed across a system of nodes and which is auditable by anyone. DLT can also potentially provide inherent security against attacks as well as the capability to self-heal when a part of the system is damaged, lost, or otherwise compromised. Section 2.5 gives a comprehensive profile of the potential to effectively apply CAV technologies and associated systems to achieve the envisioned future of transportation. The applications include CAVs” operational functionality to enhanced safety, increased mobility, and improved environment; mobility smart contracts; CDA; and security considerations as well.

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Last but not least, driving automation definition and AV laws are briefly discussed in compliance with the SAE International’s defined automation levels, which has been adopted by DOT National Highway Traffic Safety Administration.

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IBM. 2018. “Blockchain for dummies.” Accessed October 2, 2019. https://www.ibm.com/ downloads/cas/36KBMBOG. IOTA Foundation. 2019. “Meet the tangle.” Accessed August 2019. https://www.iota.org/ research/meet-the-tangle. Iteris. 2015. “Emergency electronic brake light.” Accessed October 20, 2019. http://www. iteris.com/cvria/html/applications/app23.html. Kockelman, K., S. Boyles, P. Avery, C. Claudel, L. Loftus-Otway, D. Fagnant, et al. 2016a. Bringing smart transport to texans: Ensuring the benefit of a connected and autonomous transport system in Texas: Final report, 280–291. Tech. Rep. 0-6849-1. Austin, TX: University of Texas at Austin, Center for Transportation Research. Kockelman, K. M., P. Avery, P. Bansal, S. D. Boyles, P. Bujanovic, T. Choudhary, et al. 2016b. Chap. 4 in Implications of connected and automated vehicles on the safety and operations of roadway networks: A final report. Tech. Rep. No. 0-6849-1. Austin, TX: University of Texas at Austin, Center for Transportation Research. Leonard, M. 2018. “Connected vehicles pilots pave the way to autonomy.” Accessed November 22, 2019. https://gcn.com/articles/2018/06/15/connected-vehiclesinfrastructure.aspx. Liu, H. 2016. “Synthesis of quantified impact of connected vehicles on traffic mobility, safety, and emission: Methodology and simulated effect for freeway facilities.” Ph.D. thesis, University of Cincinnati, Dept. of Civil and Architectural Engineering and Construction Management. Locke, J. 2020. “What Is Connected Vehicle Technology and What Are the Use Cases?” Blog article from DIGI. Accessed October 4, 2021. https://www.digi.com/blog/post/what-isconnected-vehicle-technology-and-use-cases#:∼:text=A%20connected%20vehicle%20 is%20one%20that%20is%20capable,an%20important%20factor%20in%20the%20 advance%20of%20IoT. Lu, X.-Y., and S. E. Shladover. 2014. “Review of variable speed limits and advisories: Theory, algorithms, and practice.” Transp. Res. Rec. 2423 (1): 15–23. Lu, X.-Y., and S. E. Shladover. 2018. Truck CACC System Design and DSRC Messages. PATH Research Report for FHWA Exploratory Advanced Research Program: Task 2.1 – Design CACC Control System for the Trucks. Lutin, J. M. 2016. “The implications of automated vehicles for the public transit industry.” In Presentation to I95 Coalition. Accessed October 2, 2019. https://i95coalition.org/ wp-content/uploads/2016/03/ LUTIN-Panel-5nsofAutomatedVehiclesforTransitforI95CorridorCoalition.pdf?x70560. Lutin, J. M. 2018. “Not if, but when: Autonomous driving and the future of transit.” J. Public Transp. 21 (1): 92–103. Ma, J., S. Shaldover, and X. Y. Lu. 2017. The concept of operations: Applying bundled speed harmonization, cooperative adaptive cruise control, and cooperative merging applications to managed lane facilities: A final report. Tech. Rep. DTTFH61-12-D-00020. Washington, DC: FHWA. McGehee, D., T. Brown, J. Lee, and T. B. Wilson. 2002. “Effect of warning timing on collision avoidance behavior in a stationary lead vehicle scenario.” Transp. Res. Rec. 1803: 1–6. McGurrin, M., M. Vasudevan, and P. Tarnoff. 2012. Benefits of dynamic mobility applications preliminary estimates from the literature. Rep. No. FHWA-JPO-FHWA-13-004. Washington, DC: DOT. Mensing, F., E. Bideaux, R. Trigui, J. Ribet, and B. Jeanneret. 2014. “Eco-driving: An economic or ecologic driving style?” Transp. Res. Part C Emerging Technol. 38: 110–121.

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Meola, A. 2016. “Automotive industry trends: IoT connected smart cars & vehicles.” UK Business Insider. Accessed November 22, 2019. https://www.businessinsider.com/ internet-of-things-connected-smart-cars-2016-10?r=UK. Milanes, V., and S. Shladover. 2014. “Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data.” Transp. Res. Part C Emerging Technol. 48: 285–300. Milligan Partners. 2019. “Tolling network: Open source Blockchain system for toll interoperability.” Accessed October 2, 2019. https://milliganpartners.com/ tolling-network-open-source-blockchain-system-for-toll-interoperability/. NCSL (National Conference of State Legislatures). 2020. “States with autonomous vehicles enacted legislation and executive orders.” Accessed April 2, 2020. http://www.ncsl. org/research/transportation/autonomous-vehicles-self-driving-vehicles-enactedlegislation.aspx. NHTSA (National Highway Traffic Safety Administration). 2016. “Vehicle-tovehicle communication.” Accessed November 22, 2019. https://www.nhtsa.gov/ technology-innovation/vehicle-vehicle-communication. NHTSA. 2017. “Automated driving systems 2.0: A vision for safety.” Accessed November 22, 2019. https://www.nhtsa.gov/vehicle-manufacturers/automated-driving-systems#​ automated-driving-systems-av-20. NHTSA. 2019. “Achieving V2X interoperability & security—Results from USDOT’s Security Credential Management System (SCMS) deployment workshops.” Accessed October 20, 2019. https://www.nhtsa.gov/sites/nhtsa.gov/files/documents/kreeb_ presentation_sae_2019_v4-tag.pdf. Park, H., C. Bhamidipati, and B. L. Smith. 2011. “Development and evaluation of enhanced IntelliDrive-enabled lane changing advisory algorithm to address freeway merge conflict.” Transp. Res. Rec. 2243 (1): 146–157. Park, H., and B. L. Smith. 2012. “Investigating benefits of IntelliDrive in freeway operations: Lane changing advisory case study.” J. Transp. Eng. 138 (9): 1113–1122. Pessaro, B. 2016. Evaluation of automated vehicle technology for transit—2016 update. Project No. 2117-9060-21. Tampa, FL: NCTR. Pesti, G., C. L. Chu, H. Charara, G. L. Ullman, and K. Balke. 2013. “Simulation based evaluation of dynamic queue warning system performance.” In Proc., Transportation Research Board 92nd Annual Meeting. Washington, DC: National Academies of Sciences, Engineering, and Medicine. Popple, R. 2019. “Autonomous vehicle technology: Preparing for the next wave of innovation in public transit.” Mass Transit Magazine. Accessed December 6, 2019. https://www. masstransitmag.com/alt-mobility/autonomous-vehicles/article/21073560/autonomousvehicle-technology-preparing-for-the-next-wave-of-innovation-in-public-transit. Reuter, E. 2015. “When cars talk: The future of Colorado’s I-70 Mountain Corridor.” Summit Daily News. Accessed December 6, 2019. https://www.postindependent.com/ news/local/when-cars-talk-the-future-of-colorados-i-70-mountain-corridor/. Rupert, B. 2016. “Example CV pilot deployment concepts: H.W. Halleck Expressway.” US DOT Sample Pilot Concepts. Accessed September 12, 2019. http://its.gov/pilots/ cv_pilot_deployment.htm. SAE International. 2018. “Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles J3016_201806.” Accessed October 28, 2019. https://www.sae.org/standards/ content/j3016_201806/. Schakel, W., V. Knoop, and B. van Arem. 2012. “Integrated lane change model with relaxation and synchronization.” Transp. Res. Rec. 2316: 47–57.

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Spears, J., J. M. Lutin, Y. Wang, R. Ke, S. M. Clancy, and WSTIP (Washington State Transit Insurance Pool). 2017. Active safety-collision warning pilot in Washington state. Final Rep. Transit IDEA Project 82. Washington, DC: Transportation Research Board. Szczurek, P., B. Xu, O. Wolfson, and J. Lin. 2012. “Estimating relevance for the emergency electronic brake light application.” IEEE Trans. Intell. Transp. Syst. 13 (4): 1638–1656. Willemsen, D., M. Kievit, F. Faber, and J. Mak. 2010. Evaluation of accident impact through simulation. Safespot integrated project - IST-4-026963-IP. Accessed October 28, 2019. http://www.safespot-eu.org/documents/SF_D4.6.4_Evaluation_of_accident_​ impact_through_simulation_v1.7.pdf. World Bank. 2018. “Blockchain & distributed ledger technology (DLT).” Accessed October 2019. https://www.worldbank.org/en/topic/financialsector/brief/blockchain-dlt. Yan, X., Y. Zhang, and L. Ma. 2015. “The influence of in-vehicle speech warning timing on drivers’ collision avoidance performance at signalized intersections.” Transp. Res. Part C Emerging Technol. 51: 231–242. Yang, H., and W.-L. Jin. 2014. “A control theoretic formulation of green driving strategies based on inter-vehicle communications.” Transp. Res. Part C Emerging Technol. 41: 48–60. Zeng, X., K. N. Balke, and P. Songchitruksa. 2012. Potential connected vehicle applications to enhance mobility, safety, and environmental security. Tech. Rep. No. SWUTC/12/161103-1. College Station, TX: Southwest Region University Transportation Center, Texas Transportation Institute, Texas A&M University System.

CHAPTER 3

Autonomous Vehicle Testing Jonathan Corey, Heng Wei

3.1 INTRODUCTION Autonomous vehicles (AVs) are subject to numerous regulatory, performance, and liability constraints, each of which requires validation and verification. Testing typically begins at the design level and continues through prototyping to manufacture. Although the methodologies used to test AVs vary widely with goals and the current stage of AV development, testing is primarily aimed to prove AVs’ safety under most road conditions. This has turned out to be much more difficult than initially thought (Fogarty and Sperling 2018). In general, AVs are tested to certify (1) they can safely drive the roads, (2) will keep the people inside and outside the vehicle safe, and (3) will rapidly recognize and yield to emergency vehicles as well as execute safe maneuvers under emergency situations or in nonstandard environments like construction zones. AV technology and the regulatory environments in which AVs will operate are still advancing through various stages of development and refinement. The constant flux in technology, readiness, and surrounding environments continues to make it a major challenge to explicitly categorize AV technologies and systems testing. This situation is likely to remain the case for several years as technologies move from testing into the field and then production. Feedback from field performance will drive subsequent alterations to AV technologies and operating environs, and issues encountered with production units will catalyze another round of revisions and reassessments. To date, seven areas of testing are available that are relevant to AVs with testing areas that fall into the broad categories of simulation and real-world or field testing. Each of those areas focuses on different aspects of the AV technology and AV systems. • AV technologies testing, • Mechanical testing, • Software and cybersecurity data testing, • Combined system testing, 105

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• Complete vehicle testing, • System of systems testing, and • Version testing.

3.2  AUTONOMOUS VEHICLE TECHNOLOGY TESTING AV technologies replace a human driver’s cognitive capabilities and direction to guide an autonomous vehicle in operation and accomplish the driving tasks that a human driver undertakes automatically. As shown in Figure 3-1, AV technologies include a wide variety of electronic sensors, computers, and cameras. These include light detection and ranging (LiDAR) sensors, global positioning systems (GPS), ultrasonic sensors, radio detection and ranging (Radar) sensors, infrared sensors, inertial navigation systems, guidance computer, global positioning system (GPS) receivers, and dedicated short-range communication (DSRC) equipment. Integrated, these systems and sensors are used to (1) determine the distance between the AV and obstacles, (2) park the AV, (3) navigate the AV and execute pathfinding operations, and (iv) employ cameras that provide 360 degree views around the vehicle to prevent collisions (CRS 2021). Safe, efficient, and

Figure 3-1.  Illustration of autonomous vehicle technologies. Source: CSS (2020).

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long-term operation require that the AV technologies used to produce an AV must be tested to enable navigating roadways successfully. The following four driving tasks are necessarily executed continuously while the AV (or a human) drives: (1) detect objects in the vehicle’s path, (2) classify these objects as to their probable makeup (e.g., a plastic bag in the wind, a pedestrian, or a moving bicycle), (3) predict the probable path of the object, and (4) plan and execute an appropriate response (CRS 2021). DSRC is mostly used in AVs for short-range data communication such as V2V and V2I, as needed to monitor road conditions, congestion, crashes, and possible rerouting. DSRC equipment installation can be complemented with 5G wireless communications infrastructure. Widespread 5G installation increases available bandwidth, offers a redundant information pathway, is potentially more appropriate for certain functions, and will probably undergo more frequent security updates as 5G is deployed internationally and is exposed to more threats. Meanwhile, DSRC has evolved slowly and has not been widely deployed. DOT has called for retaining the entire 5.9 GHz band for exclusive transportation DSRC use. As the industry has continued to explore vehicle automation, cellular-based technology has recently emerged as a DSRC competitor, known as C-V2X (CRS 2021). 4G long-term evolution/5G and fiber-optic secure networks enable vehicle-tonetwork (V2N) communication. V2N communications allow traffic management centers to collect and analyze data from vehicles and infrastructure as well as offering a pathway for updating vehicle maps and communicating relevant pathfinding information. Some AV technologies, such as GPS, adaptive cruise control, and rear-facing cameras, are currently being offered in vehicles on the market, whereas manufacturers are studying how to add others to safely transport passengers without drivers (CRS 2021).

3.3  MECHANICAL TESTING Mechanical testing focuses on the physical systems of an AV. The majority of mechanical testing for AVs will be identical to conventional vehicle testing. For example, steering wheels and brake systems are mechanically identical between conventional vehicles and AVs. This will allow automobile manufacturers to apply their historical methodologies to the mechanical testing process. One area where conventional vehicle and AV mechanical testing will have the opportunity to diverge is in the consistency of a computer’s response. Where human drivers have a statistical distribution of responses, a computer is capable of applying exactly the same response every time. This can change the wear pattern experienced by mechanical parts, which may necessitate adjustments to the existing methodologies to be adaptive to AV mechanical testing. Note that changes in wear patterns will be both potentially beneficial and harmful. A quick consideration of steering systems can illustrate the issues. A human driver may oversteer and put stress on power steering systems at the extremes

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or generate higher response forces attempting to corner too fast where an AV would have software limits to prevent either condition. The corollary is that under computer control, an AV will steer to the same degree under the same conditions, making turns at the same rate, potentially leading to more wear at specific points in the steering gears. The ISO 26262 standard includes the ability to define a system as proven in use, which recognizes that some systems have been safely used in the field for years and are well understood and have already achieved high safety standards. Many of the base automotive systems drawn from conventional vehicles, such as braking and steering systems, are subject to this declaration as mechanical systems. The logic and control systems of an AV may be designed to interface with existing systems or as integrated systems with corresponding impacts on how they will be qualified for safety certification. As a general rule, mechanical systems are subject to material and mechanical standards. Any given test may involve multiple standards and a potentially complex overlap of interacting standards. Braking tests, as an example, could use ASTM F1649 Standard Test Methods for Evaluating Wet Braking Traction Performance of Passenger Car Tires on Vehicles Equipped with Anti-Lock Braking Systems (ASTM 2013) on a test surface and with tires generating a coefficient of friction between them according to ASTM E1337 (ASTM 2018), using a reference tire (ASTM E1136) (ASTM 2017), in addition to other standards. Readers should note that overlapping standards and standards in development can create a hazard where important factors are not tested because an appropriate standard has yet to be developed, is out of date, or is applied inappropriately.

3.3.1  Safety Systems Safety critical systems, including active safety systems (air bags, collision avoidance systems, brakes, etc.) and passive systems (crumple zones, seat belts, etc.), are subject to higher levels of scrutiny. Safety critical systems are critical in avoiding collisions and occupant safety during a crash. In general, this will place safety critical systems into higher severity categories for ASIL analysis. Increased ASIL ratings correspond with increased standards and more thorough testing requirements. Depending on the construction of the AV, various components of the software and hardware systems will be safety critical. Redundancy can be used to reduce the rating of individual components. When redundant systems can assume the same function, the oversight systems may become more critical than the individual redundant systems. Note that although safety critical systems are subject to intense scrutiny, the relative risks these systems are designed to mitigate can be reduced through improved performance of the autonomous driving system. One of the main arguments in favor of AVs over human drivers is that more incidents are caused by driver failure than by AV operation. Passive systems such as crumple zones and seatbelts are only activated in the event of a collision (or a hard braking event for

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seatbelts). If the AV driving system can reduce the number of relevant events, the risk will decrease, and scrutiny shifts to the driving system.

3.3.2  Engine and Drivetrain The reliability and function of the motive systems of an AV is one area where virtually no difference between conventional vehicle and AV testing is to be expected. The primary driving forces for engine and drivetrain reliability are marketing and customer preference rather than safety after minimum reliability levels have been achieved. Current manufacturing standards, in general, exceed minimum reliability needs. Whether a vehicle is a conventional vehicle or an AV will be largely irrelevant to reliability requirements in early AV production with many AVs based on conventional vehicle models and using many from the conventional vehicle production lines. If any changes are to be expected in the long term, one argument would be for a relaxation of standards from conventional vehicles. The argument for such a relaxation is that an AV can more consistently be serviced. When service is needed, an AV could simply drop its user off and then drive itself to a mechanic. There is also an argument for requiring higher reliability standards when an AV may drive considerably longer on the road or suffer a failure without a human to address it. In all likelihood, the market will segment to address both cases with simple AV shuttles operating in an urban area relaxing their requirements slightly and long-haul AV trucks driving across the country requiring higher reliability.

3.4  SOFTWARE AND CYBER SECURITY DATA TESTING AVs are reliant on significantly more software than conventional vehicles. Each driving model, sensor, and external interface requires its own programming and software that needs testing and validation. Although automobile manufacturers have a firm grasp on mechanical testing and there is an extensive ecosystem of third-party labs to do the testing, software testing and validation is a newer and more chimeric endeavor. There are multiple variables, such as programming language, hardware specifications, and device speeds that will be in continual flux as AVs develop and technology advances. Software testing methodologies will need to keep current with advances in technology, programming, and desired outcomes. AV systems are in many ways as much or more dependent on their software stability and correctness as they are on their mechanical systems. An error in the driving systems’ software could manifest in any number of undesirable behaviors such as driving toward obstacles rather than avoiding them. All design and testing regimes begin with the identification of requirements, but software testing is particularly sensitive to design requirements. A physical object can be taken outside of its design constraints with some possibility of function. Software

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that encounters unexpected conditions is much more likely to fail or generate an unexpected result. Smart software design can mitigate the issue to a degree, but failure tolerance must be designed into the software, preferably from the beginning of design. The ISO 26262 standard grew out of IEC 61508 (IEC 2010), with both standards emphasizing identification and codification of design requirements followed by code development and then validation of function requirements. The complexity of automotive applications spurred the creation of ISO 26262 with greater recognition of the difficulties and impossibilities of achieving complete software specification for AV software. These difficulties lead to multiple divergences in software design and validation, with the two extremes being many simple component programs controlling limited systems supervised by a very complex oversight program and multiple intermediate complexity peer programs each in charge of one or more functions. As an example of redundant design and overlapping systems, consider the GM Cruise AV. It mounts multiple redundant, overlapping, and complementary physical and software systems to achieve its safety goals. These include cameras, long-range radars, short-range radars, and LiDARs. The radars, cameras, and LiDARs are used concurrently to ensure that diverse objects are properly detected, with specific note made of radar’s ability to detect low-reflectivity objects that LiDAR might miss (GM 2018). Each of these sensors may detect objects at different rates, at different distances, and as different configurations, necessitating additional programming to determine which sensors are to be given the highest confidence. The Cruise AV also addresses redundancy at the physical systems level with redundant motors for steering and actuators for braking. Redundant networks, computers, and power supplies increase the likelihood that the AV systems will remain operational (GM 2018).

3.4.1  Driving Model The potentially insurmountable challenge to programming a driving model is the simple variety of conditions that may be encountered by an AV. Among weather, natural hazards, construction, and many other conditions, there are functionally infinite possible combinations for a design team to anticipate, create requirements for, program code for, and perform testing and validation for. Extreme conditions may also be rare enough that it is unreasonable to expect them to occur during any given limited term test project. Unfortunately, the converse logic virtually guarantees that some fraction of the AV fleet will encounter any given set of such conditions at some time after deployment. These countervailing logical conclusions create a built-in trap for AV certification. AV driving models and software validation in all conditions will be prohibitively expensive and time-consuming. Likewise, programming models complete enough to address all possible conditions are prohibitively resource intensive. The solution to this trap that seems to be the consensus worldwide is to engage in limited testing under controlled and supervised conditions, with use conditions being gradually relaxed to include an increasing number of variables.

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Researchers from Carnegie Mellon University and Edge Case Research have explored the statistical issues involved with AV software validation. Beginning with a hypothetical requirement of a mean time between failures of one billion hours, a number comparable to aircraft software failure rates, they build the case for the sheer infeasibility of conduction sufficient testing to achieve statistical certainty of a mean time between failures of one billion hours. One of the major shifts in design and thinking that they noted is the shift from lower ASIL requirements for assistive features like lane keeping assistance that are overseen by a human driver to an AV system that will need to address any and all conditions. This shift impacts validation and requirements at multiple levels (Koopman and Wagner 2016). The Optimus Ride project in Boston will serve as a case study for this kind of incremental observation of AV operation and adjustment of programming. The project issued eight quarterly progress reports for 2017 and 2018. The Optimus Ride project includes human drivers in a supervisory role. In the first report, nuTonomy (2017) noted that their drivers were taking over control under conditions such as the presence of emergency vehicles, wrong way drivers on the road, near heavy construction equipment obstructing the roadway, and when other vehicles were behaving erratically. They noted that the takeover of manual control was out of caution rather than because the driving systems were incapable of navigating the circumstances. What is of more relevance for this discussion is how the circumstances encountered in Boston showed the designers new conditions to program for. These conditions are as follows: correctly identifying articulated buses and irregular heavy construction equipment, navigating a particularly congested set of intersections, sharing the road with speeding vehicles, and correctly identifying birds on the road as birds are all challenges encountered in the first 230 mi of drive in Boston (nuTonomy 2017). The second quarterly report includes notes that training data and neural network programming had to be more rigorous for certain locales within the test area because GPS information was less precise in the urban canyon (Optimus Ride 2017a). The third quarterly report notes that data in construction zones were collected for later use, driving parameters were adjusted for rider comfort, and that programming was adjusted for cases such as vehicles that do not come to a complete stop at intersections, wide turns by trucks, and double-parked vehicles blocking the travel lane (Optimus Ride 2017b). The final 2017 quarterly report notes experience with rain and localized flooding as well as hardware and network improvements (Optimus Ride 2017c). The first quarter of 2018 challenged the Optimus Ride system with snowfalls, obscured signage and lane markings, the growth of snowbanks to avoid, and issues with reflective snow and ice causing misidentification of lane divisions and available turning radii (Optimus Ride 2018a). In the second quarter of 2018, Optimus Ride expanded operations to another area with wider roads, introducing new challenges with poor lane marking and driver misbehavior, requiring the AVs to navigate unmarked street parking and unsanctioned use of one wide lane as two lanes (Optimus Ride 2018b). The third quarterly report focused on the

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documentation of operational design domain and issues with addressing and validating edge cases and probabilistic behavior (Optimus Ride 2018c). In the final quarter of 2018, improvements in obstacle detection and recognition were noted (Optimus Ride 2018d). As readers will note, the Optimus Ride project encountered numerous challenges across a broad range of topics ranging from obstacle detection to lane and position detection to situations outside of road usage rules (construction zones, incorrect behavior by motorists, etc.), and weather could contribute additional challenge to any given set of circumstances. The project began with a small operational area and later expanded as the team addressed the challenges encountered in the initial area. Driving speeds increased and safety driver interventions decreased over time.

3.4.2  Sensor Interfaces One significant change from a conventional vehicle to an AV is an increase in the number of sensors, although given the number of optional features such as adaptive cruise control and blind spot detection available on conventional vehicles, this increase is not as great as one might expect. Each sensor requires power and data bandwidth, leading to trade-offs in designs based on need and purpose. Some sensors such as tire air pressure monitors can be battery powered and wirelessly connect to the AV’s systems. Others like LiDAR and radars require more power and more consistent power delivery as well as much higher data bandwidths, leading to wired connections and choices to be made in the kind of data bus used to move the information. The CAN bus has been in widespread US and European use on conventional automobiles for decades. Although the standard has been updated over time, it is still a low security network with limitations in addressing devices and data rates that may become problematic for AV development. CAN bus does have a very important benefit that readers should consider, although it is designed for very low latency to support its mission of real-time control. How sensors are connected and what systems share bus bandwidth and access with them are very important on multiple levels. On the simplest one, the bus must be able to support real-time communication between all sensors and systems that need to transmit data on it. Architectural choices may create safety and security issues where not all sensor data are available because of bandwidth bottlenecks or communications protocol choices. At a higher level, shared buses and interconnectivity can create security vulnerabilities where hacking one system grants access to other systems. With AVs’ increased reliance on data, the data bus itself becomes a safety critical component to create redundancies for and protect from cyber threats.

3.4.3 Cybersecurity The cybersecurity issues AVs face are not new threats. As conventional vehicles have increased in complexity and connectivity, the surfaces exposed to cyberattack have grown as well. Hackers are already executing attacks on Bluetooth links to

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infotainment systems (Meyer 2018). As systems and interfaces increase with AV complexity and with the proliferation of external systems interfacing with AVs, there will be more ways to attack AVs. Smart architectural designs and segregation of critical control abilities will be necessary to prevent attackers from rerouting vehicles, harvesting information, and committing many other intrusive and illegal acts. In addition to digital hacking of AV software, researchers in China have demonstrated that designers will also need to be on guard for physical hacking of algorithms. In this case, the researchers placed special reflective stickers on the road to fool lane identification algorithms and trick a Tesla into driving into oncoming traffic (Goodin 2019). The precise placement of the stickers created a situation that tricked the lane identification algorithms into believing that the lane was merging into the adjacent lane. On a two-lane road with opposing traffic, this would be potentially catastrophic. Tesla noted in their response to the publication that this vulnerability had been patched in software updates released before the research efforts completed the publication process.

3.4.4  Cyber Data Testing Cyber data testing ensures the security of data collected by computers embedded within AVs and the protection of on-board systems against intrusion and corruption. Replacing human drivers with computers necessitates the generation of instantaneous data such as vehicle location, velocity, fuel status, and so on. In the same vein, significant longer-term data may also be generated, such as predicted brake wear, oil condition, tire wear, and so on. Significant AV internal data, such as sensor and device firmware, network configuration, and computer resource allocation must also be maintained. Finally, security critical computer data such as encryption keys, remote ID codes for DSRC communications, and electronic key codes must be retained and secured by the AV. To function, AVs must communicate with other vehicles and roadside infrastructure securely sending and receiving data. Secure communication requirements are not limited to AVs themselves but include the rest of the V2X infrastructure as well, in which corrupted input data can affect system performance. Consider the impact of false vehicle data on V2X-enabled traffic signal control. Similarly, AVs depend on secure data inputs as well. Critical functionalities like pathfinding depend on map and traffic data received by the AV. To maintain security, AV software and firmware will need to be kept up to date. Computer manufacturers have dealt with operating system updates for decades, which AV manufacturers would be wise to learn from. Updating vehicle software is likely to be more difficult in many ways, given the larger number of devices involved in an AV compared with a laptop or phone. Even more important, AV updates will expose critical safety functionality every time, which will necessitate a secure update download, curation, and installation process. In addition to the AV-level considerations of updates, there are larger system-level concerns. Threats requiring patches must be swiftly identified,

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Figure 3-2.  Potential entry points for autonomous vehicle hacking. Source: CRS (2021).

patches created, patches security tested, and disseminated to user vehicles. With AVs passing data from one vehicle to another, each AV naturally depends on other AVs to be updated and secured. This will necessitate swift population wide AV updates to ensure that all vehicles are properly updated. Unattended and automatic updates will be critical to AV system operation. It should be noted that various legal, regulatory, and cultural issues that will not be covered here may be involved with updates and update requirements. With all these considerations, it should come as little surprise that protecting autonomous vehicles from hackers is a big concern for federal and state governments, manufacturers, and service providers (CRS 2021). Figure 3-2 illustrates potential entry points for autonomous vehicle hacking to vehicle’s electronic systems using a dozen portals, including seemingly innocuous entry points such as the airbag, the lighting system, and the tire pressure monitoring system.

3.4.5  System of Software Systems Testing An anticipated challenge that will need to be addressed as AV technologies and implementations advance is the testing and validation of systems of the software systems. For example, AV-aware traffic signal control systems will need to function correctly with and without AVs, across a broad range of traffic patterns, and correctly process a variety of AV inputs. System design errors can easily introduce instability, inefficiency, and circular dependencies. AV routing, platooning, and parking systems all have similar potential problems that will need to be tested for.

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Another anticipated challenge for AV operations is how software upgrades, operating system changes, and communications standards will be tested. Associated questions include how version compatibility will be handled for systems and AV hardware compatibility. These challenges closely match those currently experienced by the smart device (smart phones, tablets, etc.) industry, from which the AV industry can borrow methodologies. Interoperability will require significant effort in standards development and adoption to allow vehicles from different manufacturers, with different hardware revisions, and some variation in update status to operate across varying V2X deployments, traffic conditions, weather conditions, and operational jurisdictions.

3.5  COMBINED SYSTEM TESTING Combined mechanical and software systems will behave differently from individual components. Combined systems require their own testing and validation systems. For example, the brake system can be mechanically reliable and the software to control the braking system can respond perfectly, but the combined system may not behave as expected. If the software actuates the brakes too quickly or slowly, within the wrong range, or fails to account for brake wear, the combined system may not perform within expected parameters. Combined system testing can borrow heavily from mechanical and software testing methodologies as the source of any given problem will be traced back to either a mechanical or a software source. As NASA discovered to their chagrin, it is possible to have properly functioning mechanical systems and properly functioning flight models while still having significant failures at the system level. In NASA’s case, the Mars Climate Orbiter had working altimeters, thrusters, and valid flight operations code. However, the combination of a thruster’s performance being rated in imperial units and the flight computer operating in metric units resulted in the retrorockets firing too late for the craft to land safely (MCOMIB 1999). These kinds of interface problems may avoid detection during the testing of individual systems because the individual systems are performing correctly given their input data. However, once the systems are combined, the incorrect behavior may manifest. The proliferation of sensors on AVs offers many opportunities for different units to be used and different operational assumptions to be made. These miscommunications can span a wide variety of nonideal behaviors. A simple issue like a sensor beginning a report every second versus a sensor reporting one second after the end of the previous report can easily result in the timing of the report drifting over time. Alternatively, a sensor to determine wheel angle during turns can cause problems if the report is in percent of turn instead of angle in degrees of turn. These basic problems seem silly, but as NASA already demonstrated, they can manifest even in highly tested and evaluated systems.

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In general, AVs are a much more forgiving test environment than space, although the environment in which they operate is much more complex. AVs are also much more likely to be recovered and analyzed to correct faults discovered in the field. Initially, the presence of human safety drivers will also help in the identification of such errors. However, readers should note that it will be inevitable that errors of this type occur over time.

3.6  COMPLETE VEHICLE TESTING With certification of road legality typically being a matter of complete vehicle certification, methodologies for complete vehicle certification are an important consideration. Confidence in AV performance must be high for authorities to sign off on AVs driving with live traffic on public roads. This introduces significant methodological challenges in terms of completeness of testing and statistical certainty. Addressing this challenge is of significant importance to the development and deployment of AVs. Once the systems and software in a vehicle have completed testing of individual systems, the complete vehicle needs to be tested. Numerous potential issues are there to resolve for a complete vehicle, which may be difficult to address at a lower level. For example, parameters related to passenger comfort are hard to address before passengers are experiencing the ride. Similarly, the behavior of a vehicle with relation to human drivers and pedestrians is difficult to fully validate before there is a complete vehicle with which to share the road. Simulations can be run, but the human factor and feedback from human actions in response to AV actions is notoriously difficult to simulate. The Optimus Ride project 2017 Q3 report discussed passenger comfort parameters that they adjusted to improve ride quality. These included lateral and longitudinal jerk (acceleration) being too high (Optimus Ride 2017b). These adjustments are unsurprising when one considers that the physical limits of the hardware and mathematical limits for the software are designed at a systems level and subject to capability limits of the systems prior to integration into a complete vehicle. Optimus Ride is not the only project testing complete vehicles. GM’s Cruise AV is undergoing testing and validation in San Francisco, however with some difficulty (Welch 2019). Currently, reports indicate that trips on the GM Cruise AV are taking much longer than expected with numerous issues being blamed for the delays. These causes include random shutdowns, an inability to distinguish stationary and moving objects, and misidentification of spray from the tires of other vehicles proving to be an obstacle (McEachorn 2019). After some high-profile fatalities while testing AVs and driver assistance systems, many manufacturers have revised their testing strategies to prefer testing at test tracks and other specialized facilities. Following a March 18, 2018, pedestrian collision in Tempe, Arizona, Uber suspended AV testing on public

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streets (Said 2018). Tesla, with its AV-adjacent Autopilot driver assistance system, has seen more than a few fatality collisions associated with its systems (IIHS 2018, NTSB 2019).

3.7  SYSTEM OF SYSTEMS TESTING AVs will not operate alone in a void. They will be a part of, and interact with, larger systems like traffic signal control. Systems like FHWA’s Signal Phasing and Timing (SPAT AKA Etexas) (Harmonia 2015) are designed to enable CVs and AVs to make better traffic signal control–related driving choices. As previously discussed with relation to Combined System Testing, just because two systems function correctly in isolation does not mean that they will function well together without adjustment. Consider the interaction of AV-aware traffic signal control, an AV, and AV routing systems. If the systems are too sensitive or update too frequently, it will be possible to create situations where oscillations are introduced. When an AV selects the optimum route based on projected travel time and then that information is used to determine traffic signal control parameters, it can change the outcome of the traffic signal control optimization. When the new traffic signal information is then fed into routing software, a new route may become optimum, changing the AV’s decision, and vice versa. If designers are not careful, it will be possible to create indecisive conditions where each AV may change the outcome. With so few AVs on the road, system of system testing is largely simulation based. Test facilities will be the natural places to begin physical testing. As the test deployments proceed and mature, limited field testing of AV-aware traffic signals and routing systems will follow naturally as projects increase in scope.

3.8  VERSION TESTING As software publishers have learned over the last several decades, supporting and operating complex software over time requires version control and updating mechanisms. Hardware also goes through revision processes, although usually at a slower pace than software. As complex systems like AVs are integrated over time, the interaction of varying versions of hardware and software will need to be validated to ensure that changes across versions do not create undesirable outcomes. With the relatively short time span that AVs have been operational, versioning has occasionally been an issue, but it has not reached levels anywhere near what can reasonably be expected. Assuming an AV will be in the field for at least 10 years, there will be significant changes and updates to the vehicle’s driving model, potential updates to sensor firmware, and probable version changes in

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components related to parts exchange during repairs. Each change will need to be validated with the other changes made to the AV. With versions proliferating over time, there will potentially be infinite possible combinations to validate. Because infinite versions are impossible to test and validate, manufacturers and programmers will be forced to limit support over time.

3.9  SIMULATED VERSUS REAL-WORLD TESTING AV testing methodologies can be broadly divided into simulation-based and realworld strategies. Simulation-based strategies offer low-cost methods of testing but are limited by the assumptions and design of the simulation environment. Real-world testing methodologies tend to be more labor-intensive, expensive, and limited by safety considerations. Each strategy has its own strengths and weaknesses. Simulation has long been used for testing self-driving cars, but only recently it has become an integral part of training neural networks. With simulation software, engineers can test the AVs in the virtual world using sample data that are collected in the real world (Angadala 2020). Basically, simulation strategies are based on software-in-the-loop (SIL) and hardware-in-the-loop (HIL) methodologies. SIL allows testing of the software components of an AV system. SIL can also be used to simulate a software model of a physical component, which can be useful in prototype development. HIL requires a physical component to interact with the simulation. This improves the physical representation of the simulation over purely software simulations. HIL tends to be a narrowly applicable technique than SIL because a physical component must be involved. Real-world testing requires a physical component, system, or complete AV to be tested. This ensures that real-world testing will consistently occur later in the design and development process than software-based methods. Real-world testing is also the only way to be certain of performance factors like wear patterns and mechanical life expectancy. Real-world testing is also consistently more expensive on a per test basis, particularly for destructive testing. A majority of simulation-based testing occurs with software testing, HIL simulation of combined systems, system of systems testing, and version testing. Conversely, real-world testing primarily occurs with mechanical and complete vehicle testing. All safety critical systems go through real-world testing at some point. All simulated testing and highly structured physical testing is subject to experimental design problems. It is relatively easy to set the conditions of the tests in too restrictive of a manner to identify problems. Likewise, conditions that could manifest issues may simply not be tested for ease and convenience, such as failing to test a system under freezing or wet conditions. This can occur during any kind of testing. The failure is one of human nature, timeliness, and resource management.

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Wherever possible, researchers, analysts, and engineers need to ask themselves whether the design and testing conditions are broad enough. Some conditions are obvious to test, such as rain, snow, and blazing hot sun. Others can be more difficult to think of, such as a faulty sensor intermittently reporting an erroneous value or a system failing to reset to its default state as expected. One of the greatest weaknesses of any testing regime is the completeness of the testing conditions.

3.10  ANALYSIS FRAMEWORKS Testing of individual components and systems for AVs falls under a multitude of standards and testing practices ranging from manufacturer internally developed practices to national standards to international standards. Numerous standards bodies such as ASTM (mechanical testing), SAE (definitions), IEC (communications), and ISO (process), among many others have standards related to AVs. As an exemplar of AV-related standards, the ISO 26262 adaptation of IEC 61508 will serve as a discussion aid for the remainder of this document. The ISO 26262 standard identifies numerous testing related concepts, processes, and practices related to AV testing (ISO 2018). One key feature of ISO 26262 is the definition of automotive safety integrity level (ASIL), defined in Part 2 and detailed in Part 9. The ASIL analysis method is commonly seen in AV literature. The assessment of safety risks, creation of safety requirements, and the management processes to tie requirements to validation testing comprise the following: software and simulation testing, mechanical testing, and combined system testing. This information is commonly summarized as a V diagram showing design and the derived requirements on one branch of the V and integration and validation testing on the other. Supporting processes and guidelines to applying ISO 26262 are detailed in sections of complete vehicle testing, version testing, and testing facilities. The ASIL is a coarse evaluation of the impact of a failure of a specific system on a vehicle’s occupants and other road users. An ASIL designation is a product of the severity of a risk (nondamaging disruption to fatality collision) multiplied by the exposure to that risk (how often per unit of operation) and modified by the controllability of the occurrence. ASIL is expressed in terms of letters from A at the lowest level of risk to D at the most severe.

3.11  SOFTWARE SIMULATION Simulated testing occurs at many different stages of prototyping and design. Software can simulate low-level design elements such as circuit designs. The next step-up in complexity is HIL and SIL simulation in which hardware and software

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systems are tested with software-generated inputs. Driving simulators represent a special case of “hardware” in the loop simulation with the human driver receiving virtual input. Driving simulators are often used to test vehicles’ characteristics on the road and interactions with human drivers. Finally, AVs need to be tested in controlled environments prior to on road testing, particularly when testing a complete AV’s response to unusual circumstances.

3.11.1  Design Simulation The design process can begin with simulation. Software like MathWorks’ Simulink can be used to simulate basic components and algorithms (MathWorks 2019). Simulating hardware designs in software allows the designer to adjust parameters prior to production of the hardware. Larger and more complex simulations allow designers to virtually test complex systems of systems. Software testing of hardware designs ranges from basic circuit analysis to complete hardware design and simulation (Figure 3-3). A similar strategy applies to algorithm development. MATLAB, also by MathWorks, is a software package frequently used for algorithm development. MATLAB allows researchers and designers to directly program algorithms as well as incorporate external libraries and code (MathWorks 2021). For example, MATLAB can be used to test and refine algorithms for video image processing. This allows researchers and developers to refine their algorithms while working on high power computers and determine the trade-offs between algorithm accuracy, computing power requirements, speed, and number of objects to track.

3.11.2  Software in the Loop Simulation SIL simulation exists on a continuum with software design simulation. Many of the same tools can be used to perform SIL, such as MATLAB and Simulink. Proprietary code may also be written to test software. Differentiating software

Figure 3-3.  Simulation and testing regimes.

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development from SIL is usually a function of the state of the software being tested and the rigor of the process used. Where software development can proceed on an ad hoc basis, SIL simulation testing requires a more rigorous and formal approach than software development and design. SIL testing needs to identify a range of input parameters to be tested with acceptable output values to compare the software output against. When the software outputs are within acceptable values, the software passes the test. Readers may also encounter model in the loop (MIL) simulation and testing. MIL focuses on what actions the model program is supposed to enact during operation but does not require the model to be coded in the final design language or compiled for the final hardware. For example, MIL testing can be enacted with a model written in Python running on a desktop in which the final code for the vehicle will be written in C++ running on a microcontroller. SIL testing will use the compiled C++ code running on a simulated microcontroller and HIL testing will test the system using the code running on the microcontroller. Many authors and publications do not differentiate between SIL and MIL simulation, typically using SIL for both.

3.11.3  Hardware in the Loop Simulation HIL requires a physical system to test with the appropriate digital connections to transfer inputs to the physical system and sensors to encode the physical system’s response for use in the simulation. Typically, this involves an input and output (I/O) system to handle the transformation of digital outputs from the computer running the simulation software into analog inputs for the hardware system. The I/O hardware may also include additional circuitry to convert signal voltages and frequencies as necessary. Sensors reading the hardware system also feed back into the I/O system with additional conversion and control circuitry as needed. National Instruments’ LabVIEW software is one software package that can be used for HIL simulation (National Instruments 2020). The LabVIEW software allows users to apply MATLAB algorithms to their hardware. In addition, the LabVIEW software enables an analysis of simulation data and fitting of results. Where LabVIEW descends from software designed to run lab equipment and perform data acquisition, OPAL-RT’s RT-LAB Orchestra descends from software designed to simulate complex electronic components, such as FPGAs, and power systems at high frequency and fidelity (Opal-RT Technologies 2018). Although it may require significant effort to accomplish, it is possible to create a network of HIL simulations. In principle, an entire AV can be recreated in HIL and SIL simulation. Together, HIL and SIL simulation can approach complete vehicle testing.

3.11.4  Driving Simulator Although driving simulators have less relevance to completely automated vehicles than to conventional vehicles or partially automated vehicles, they have relevance to testing the passenger experience and human reactions to AV actions.

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A number of software packages are available for driving simulation. Many of the currently available driver simulation software packages include HIL, MIL, and SIL components in addition to human driver input. Driving simulators revolve around ego vehicles, which the humans drive. The rest of the simulation environment is created in a manner similar to a video game, with digital vehicles, pedestrians, and cyclists following scripted, rule-based, or AI-created paths through the digital network. Physical rules emulating real-world physics are implemented “in game” to control behaviors like traction in simulated weather conditions. Simulator hardware varies from simple video game controllers at the low end to complete vehicles with motion control systems and surround displays. Although the driving simulator hardware has an impact on the human driver’s experience, it changes little in regard to simulated vehicles. Simulation size and the number of ego vehicles a simulation can support are functions of the computer hardware and software used to run the simulation. Packages like CARLA allow the simulation of multiple ego vehicles, weather, and maps, among other factors (Dosovitskiy et al. 2017). CARLA includes the ability to simulate multiple sensor types such as video, LiDAR, and radar. One advantage of CARLA is the open assets and editable environments. Other simulation packages have been developed out of earlier simulation models like CarSim, which was used to simulate vehicle kinematics prior to the development of AVs (Mechanical Simulation Corporation 2019). CarSim includes MIL, SIL, and HIL capabilities. CarSim was developed out of a test sequence– based simulation for conventional vehicles. Unlike some of the more mundane packages, rFpro was developed from a more glamorous racing simulation history (rFpro 2019). rFpro was developed to test Formula 1 vehicle dynamics across the conditions at various circuit racetracks. Over time, the package has been developed to include roads outside the racing circuit. Software like PTV’s VISSIM is typically used for traffic modeling through microsimulation of vehicles. VISSIM can also be combined with driving simulators and/or used as a foundation for SIL testing of AV algorithms (PTV Group 2019). VISSIM includes a COM interface to enable external code to interact with the simulation, allowing users to model traffic behavior across a wide range of customized conditions.

3.11.5  Environment Simulation All the simulation methodologies discussed so far are as applicable to conventional vehicles as to AVs. The sensors and driving software of an AV have introduced a new class of driving simulation specific to AVs called “environment simulation.” Environmental simulation is the equivalent of a driving simulator for a human being. It renders a simulated environment for the AV to operate in and evaluate the results. NVIDIA has developed an environment simulation system named NVIDIA DRIVE Constellation to feed computer-generated imagery to a simulated AV

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(NVIDIA 2019). The DRIVE Constellation system uses two servers, one to generate the environment and the other to simulate the AV’s systems. Currently, the system is entirely software. In addition to their capabilities as driving simulators, many driving simulator packages can be used for environment simulation in addition to their driving simulator functions. CARLA, rFpro, and CarSim each include environmental simulation capabilities for simulated AVs. Depending on the package, different methodologies will be required to connect the systems to the simulation environment. In the long term, as graphical processing power and projection fidelity increase over time, it will become easier to generate virtual environments. With increasing numbers of AVs entering the complete vehicle stage, environmental testing will increase in frequency. Likewise, as more field data are collected by AVs and more edge cases in operation are identified, the fidelity and complexity of the simulated environments and events will increase.

3.11.6  Virtual Reality–Based Simulation As an effective simulation tool, VR technology has drawn more and more attention and brought significant impact in the fields of architecture, engineering, and construction because of the rapid advancement of computational technologies. VR offers users the ability to explore a 3D-rendered environment. In engineering applications, VR environments are frequently designed to enable users to see through surfaces to the structures and equipment within, such as a civil engineering environment allowing an engineer to see rebar reinforcements within concrete walls. Applied to AVs and AV technologies, VR environments can be used to see in different spectra or view the visualizations of different sensors such as LiDAR. These simulations will assist engineers in placing sensors and determining what parameters affect an AV’s environmental sensing. VR can also be used in driving simulators to address issues such as the design and placement of mirrors, roof supports, and other view limiting design elements. The commoditization of VR hardware for video games has made VR more accessible over the past couple of years. More details about VR are presented in Chapter 1.

3.12  DOT-APPROVED AV PROVING GROUNDS DOT announced on January 19, 2017, that 10 proving ground pilot sites were designated to encourage testing and information sharing around AV technologies (DOT 2017). The primary goal of these DOT-approved AV proving grounds is to foster innovations that can safely transform personal and commercial mobility, expand capacity, and open new doors to disadvantaged people and communities, as a logical next step in the DOT’s effort to advance the safe deployment of AV technology. Meanwhile, the proving grounds will also provide critical insights

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into optimal big data usage through automated vehicle testing and will serve as a foundation for building a community of practice around automated vehicle research. Those proving ground designees include the following: • City of Pittsburgh and the Thomas D. Larson Pennsylvania Transportation Institute, • Texas AV Proving Grounds Partnership, • US Army Aberdeen Test Center, • American Center for Mobility at Willow Run, • Contra Costa Transportation Authority and GoMentum Station, • San Diego Association of Governments, • Iowa City Area Development Group, • University of Wisconsin-Madison, • Central Florida Automated Vehicle Partners, and • North Carolina Turnpike Authority. Because of the rapid increase in AV testing activities in many locations, DOT has realized that there is no need to favor particular locations or to pick winners and losers for designating the proving grounds. Since then, they have decided to no longer recognize the designations of 10 AV Proving Grounds that were announced on January 19, 2017 (US DOT 2018). Correspondingly, DOT intends to apply neutral, objective criteria and to consider all locations in all states where relevant research and testing activities are actually underway. This puts state and local agencies in charge of establishing consistent cross-jurisdictional approaches and working with first responders to develop traffic law enforcement practices and emergency response plans for automated vehicle testing. Several production vehicles on the market currently rank at Levels 1 and 2 of SAE’s automation rating system. Some experts forecast that SAE level 3 AVs will be ready for marketing and deployment in a few years. SAE level 5 deployments are anticipated to be several years out unless the AV operating environment is simplified either through infrastructure improvements or changes in operational environments. Combining the variety in testing sites, need for diverse testing regimes, and variety of technologies, it follows that AV testing and development vehicles is continuing in many states and cities in the United States (Marshall 2019). In addition to the test of AVs themselves, AV technology testing also needs to be proved effective in cooperative and interoperative on-road and roadside intelligent infrastructures. On-road testing and early deployments are important to improving AV performance and allowing them to reach their full performance potential (DOT 2018). Careful real-world testing allows developers to identify and rapidly fix system shortcomings, not just on individual vehicles but across fleets. Reasonable risks must be addressed through the application of testing protocols. As a way to build public trust, an in-vehicle driver engagement monitoring system, a second test driver, or other methods should be involved during testing on public roadways.

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3.13  TESTING FACILITIES Arguably, the first AV testing facility was the high-occupancy vehicle lanes of Interstate Freeway 15 (I-15) in San Diego, California, that were used as a test site for the National Automated Highway System Consortium Demonstration ‘97 in 1997 that had a platoon of driverless vehicles drive along the freeway using magnetic pucks in the HOV lanes for guidance (Novak 2013). Since that time, a number of ad hoc, manufacturer-specific, and fixed facilities have been used to develop AV systems and complete AVs. Some of the current AV test facilities in North America are listed and described subsequently.

3.13.1  MCity (Michigan) As shown in Figure 3-4, Mcity is a test facility located at the University of Michigan (UM) as part of a collaborative effort spanning the UM, government agencies, and industrial partners. The industrial leadership circle includes automobile manufacturers (Ford, Honda, and Toyota), a technology company (Denso), a telecom company (Verizon), and an insurance company (State Farm) (UM 2021). Mcity is a 32 acre test facility with multiple roadway types and a

Figure 3-4.  Mcity testing facility.

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variety of intersection types to enable the testing of AVs across a diverse array of conditions. Mcity also includes a variety of pavement markings, roadway geometrics, pedestrian crossings, and road surface types to enhance testing Mcity, which opened on July 20, 2015.

3.13.2  Transportation Research Center (Ohio) The Transportation Research Center (TRC) facility in East Liberty, Ohio (Figure  3-5), is a 4,500 acre private test facility that has been involved in automotive testing since 1972 when it began operation in conjunction with the Ohio State University (TRC 2017). The facility was privatized in 1988 and has continued evolving over time. Now, a new SMART center is being constructed to add a signalized testing corridor for AV evaluation (Bishoff 2019). Two factors separating TRC from many other AV testing facilities are the ability to test heavy trucks and to execute testing at freeway speeds. In addition to the various courses, TRC includes emission testing and impact testing facilities that may be used to test the non-AV components of AV design.

3.13.3  Area X.O (Ottawa, Canada) Area X.O AV testing and technology facility is located in Ottawa, Canada, and is shown in Figure 3-6. The facility began as an agricultural vehicle testing ground but was refit for AV, agriculture, drone, and military testing. The site includes features such as a speed bump, streetlights, and one-way streets (Pilieci 2019). Potentially as notable, Ottawa experiences significant snowfall over the course of the year, making the L5 facility a potential winter readiness test site.

3.13.4  GoMentum Station (California) GoMentum Station is a testing facility owned and operated by AAA located near San Francisco that has been in use since 2015 (Figure 3-7). GoMentum Station includes a diverse array of driving conditions with multiple merging and diverging road test areas as well as overpasses and tunnels to challenge AVs. One of the unique features of GoMentum Station is Bunker City, a complex of rectangular

Figure 3-5.  Transportation research center.

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Figure 3-6.  Area X.O facility.

Figure 3-7.  GoMentum station Map. Source: Google Maps (Accessed August 26, 2021).

bunkers and access roads (GoMentum Station 2021). Bunker City also hosts a range of local wildlife to add an additional wild card factor to testing.

3.13.5  Automated Driving Systems for Rural America (Iowa) The National Advanced Driving Simulator (NADS) is a self-sustained transportation safety research center affiliated with the Safety Research Using Simulation (SAFER-SIM), a Tier I University Transportation Center with the University of Iowa. Funded by government and industry, NADS utilizes its suite of world-class driving simulators and instrumented on-road vehicles to conduct research studies for the private and public sectors, including AV testing in rural roadway environments, as illustrated in Figure 3-8. NADS offers driving simulators with a range of fidelities to best address the requirements for projects,

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Figure 3-8.  Illustration of NADS and ADS for Rural America. Source: University of Iowa (2021).

including the recently awarded project, entitled “Automated Driving Systems (ADS) for Rural America,” a $7 million grant officially awarded by the DOT in spring 2020 (SAFER-SIM 2020). The DOT had set aside $60 million in 2019 in federal grant funding to test the safe integration of ADS on national roadways. The project ADS for Rural America is among eight DOT awarded projects across the country. This project will use a custom vehicle equipped with a LiDAR scanning system, computer vision systems, radars, and high-definition maps. A specially trained driver will be driving the vehicle at all times, and the vehicle will follow a 47 mi route through parts of Iowa City, Hills, Riverside, and Kalona—experiencing different types of roads along the route. The project plans to do more testing on rural roads to address the unique challenges that are facing Iowa drivers, such as sharp curves, gravel, weather, and farm equipment on the roads (SAFER-SIM 2020, Gravelle 2020). The work on the project is staying in the Midwest. The vehicle is being

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outfitted by the Illinois-based company AutonomouStuff, and the high-definition road mapping is being done by the Wisconsin-based Mandli Communications. The vehicle is also being purchased as part of the Buy America Act.

3.14  UPCOMING TESTING FACILITIES Multiple facilities figure in the planning and construction stages beyond those already listed. As project deployments increase, it will be reasonable to expect a blurring of the line between AV test facility and AV test deployment. For the purposes of this publication, a future test facility is a facility designed to test AVs separated from conventional traffic.

3.14.1  SunTrax (Florida) The SunTrax facility being built in Polk County, Florida (Figure 3-9), lies between Orlando and Tampa and is slated to be fully operational in 2022 (SunTrax 2021). The facility will include an urban area, an irregular grade area, a complex curvature area, a multimodal pick up and drop off area, and an augmented reality pad for simulating environments. In addition to the test areas, the facility will have a high-speed track and on-site workshops. A plan to add a weather simulation facility in the second phase of construction is on the pipeline.

3.14.2  Curiosity Lab (Georgia) The Curiosity Lab is located in the City of Peachtree Corners, Georgia (Figure 3-10). It is a 1.5 mile-long corridor located within a technology park that has been equipped with wireless communications and the IOT. The corridor is

Figure 3-9.  SunTrax facility. Source: Google Maps (Accessed August 26, 2021).

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Figure 3-10.  Curiosity Lab at peachtree corners. Source: City of Peachtree Corners (2019).

designed to run conventional traffic on two inner lanes and AV test traffic on two outer AV-only lanes (City of Peachtree Corners 2019).

3.15  CURRENT DEPLOYMENTS As of August 2019, Bloomberg Philanthropies and the Aspen Institute were tracking 91 cities running AV pilot projects and a further 41 in the planning process worldwide (Bloomberg Philanthropies 2019). Of those 132 projects, 31 active projects are located in the United States with a further 23 planned. Canada adds another 5 current projects and 3 planned to the North American total. Over the course of the preceding pages, a number of current deployments have been discussed. Many of the testing facilities discussed in the previous section are also associated with current deployments. For example, the Ann Arbor, Michigan AV test is occurring on the MCity test track. Similarly, the Columbus, Ohio deployment includes work at the Transportation Research Center, although the project focuses on deploying AVs on the streets of Columbus. The Optimus Ride project in Boston is occurring on Boston’s streets at Marine Park and Fort Point. In general, current deployments can be grouped into several semidistinct groups. One group is technology demonstrators focused on public exposure and data collection. Another group is autonomous shuttles for first/last mile trips. AV-based transit forms a third group of projects. Autonomous freight forms a smaller group of projects. The final group of autonomy projects is local delivery. Numerically, the first three groups comprise a distinct majority of the current deployments, with the last two groups being much smaller than the first three. A typical technology demonstrator and data collection project uses a standard format AV driving around at a test facility or on designated public access roads. One example is the first stage of the Ann Arbor, Michigan project, which advanced from being test facility bound to delivering pizza (Snyder 2017).

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Autonomous shuttles comprise the largest group of AV projects currently deployed or in planning in North America. AV shuttle applications exist in Columbus, Ohio (Smart Columbus 2019); Grand Rapids, Michigan (City of Grand Rapids 2018); and Las Vegas, Nevada (LVCVA 2019), to name a few cities. The Las Vegas deployment is unusual in that it involves an underground tunnel system. AV transit applications are frequently envisioned with shuttles or conventional format AVs. Applications involving transit buses are somewhat rarer. Rare, however, does not mean nonexistent. San Antonio is heading an effort with the Texas Department of Transportation to test an AV-based bus rapid transit service (TxDOT 2017). Autonomous truck research is a focus of considerable energy and excitement. The excitement has yet to translate into many public projects; however, many states are getting ready for autonomous truck testing (Transport Topics 2019). Truck manufacturers like Volvo are proceeding with internal research and development into autonomous truck technologies (Transport Topics 2018). Local delivery is another area of AV application being explored. A company named Starship has been testing and deploying small AV drones for delivery of food, including on the Purdue University campus (Murley 2019). The drones feature a small locking compartment and GPS guidance, allowing them to meet up with app users at designated coordinates.

3.16  IMPACT OF POLICIES ON AV TESTING Although a major part of this chapter has focused on how testing is conducted and the capabilities of test facilities, a brief discussion of national and state policy impacts is warranted. The United States Congress has been working on AV testing and deployment policy with two stand-alone AV bills debated in the 115th Congress (2017 to 2019) and integration into the America’s Transportation Infrastructure Act by the 116th Congress (2019 to 2021) (Canis 2019). Currently, federal input on AVs is focused on research coordination, data sharing, and model standards and regulation development. States are driving policy development and implementation, leading to different legal restrictions on AV testing and deployment on a state-by-state basis. In many states, local regulations can also impact testing and deployment. At the policy level, several issues will come into play as AVs continue to move away from test tracks and into on-the-road testing. The Congressional Research Service identified a number of policy concerns in which national policies will drive deployment decisions. These include the following: • Cybersecurity—Rapid response to security flaws in numerous AV and associated systems will be required to maintain AV safety. • Data ownership—Numerous groups want data regarding AV usage that will conflict with user privacy concerns.

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• Safety regulations—Current regulation has vehicle safety regulated nationally and driver licensing handled at the state level. • Communications—The volume and range requirements for AV deployment will require continued adjustment of radio spectrum allocation and determination of allowed uses. • Federal-state coordination—A number of states have enacted legislation and/ or issued executive orders regarding AV testing and deployment. In addition to those issues already identified, there are many associated policy challenges to work through. For example, every major AV deployment is likely to require upgrades to the local digital infrastructure to carry AV V2X data. These needs are likely to run into the same troubles that Google ran into when it attempted to install Google Fiber internet service (Fung 2016). Ownership of utility poles and regulations controlling the process of putting new lines on the poles is complicated, time-consuming, and expensive to navigate. It is also an arena that incumbents can use to dramatically increase costs and slow down competing deployments. Rearranging or adding lines on a pole can require each user to send out a crew to adjust their lines before the new line can be added. Understandably, the process can take months or years to complete on any kind of a regional basis, with each company waiting on the previous company’s crews to finish their part before scheduling a crew to do the next step of the work. The counter is to have a one-touch make-ready (OTMR) policy such as the one the Federal Communications Commission has been considering (FCC 2018). Under OTMR, a qualified crew would be able to do the relocation work for each line currently on the pole at one time. This would dramatically reduce the number of times each utility pole would need to be adjusted to put up new lines and reduce the timeframe for deployment. Readers should note that additional cellular bandwidth for cellular AV deployments would be likely to run into similar problems as carriers increasingly use small cells with a wired backhaul for local coverage.

3.17  CRITICAL AV TESTING ISSUES FOR FUTURE DEPLOYMENT Congressional Research Service (CRS 2021) addressed several key issues related to AV testing that have received disagreements within the 115th Congress, which are quoted as follows: • Number of AVs that NHTSA should permit to be tested on highways by granting exemptions to federal safety standards, and which specific safety standards, such as those requiring steering wheels and brake pedals, can be relaxed to permit thorough testing. • How much detail legislation should contain related to addressing cybersecurity threats, including whether federal standards should require AV technology

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that could report and stop hacking of critical vehicle software and how cyber much information car buyers should be given about these issues. • Extent to which vehicle owners, operators, manufacturers, insurers, and other parties have access to data that is generated by AVs, and the rights of various parties to sell vehicle-related data to others. • Fully autonomous vehicles may not have standard features of today’s cars, such as steering wheels and brake pedals, as there will not be a driver. By law or regulation, motor vehicles built today are required to have many of these features. Some governments are taking a lead by modifying vehicle requirements for purposes of pilot programs and tests. Permanent changes in standards will most likely be necessary if AV technologies are to be commercialized. • AVs will need new types of infrastructure support and maintenance, including advanced telecommunications links and near-perfect pavement and signage markings. Planning and implementing these highway improvements may enable AVs to be fully functional sooner. In addition, many test vehicles are currently powered by electricity, so the availability of refueling stations could be a factor in their acceptance. • Technical challenges of AV testing over a veritable mountain of problems are numerous and keep cropping up. The challenges also include how will the AV’s sensors handle snow, rain, sand, and hail, and how to deal with unrelenting sun glare and unusual pavement conditions at construction zones? Can the vehicles be trained to prepare for hard-to-imagine road situations—a truck that suddenly unloads its cargo, or a roadster that makes an unusual (or irrational) maneuver on the road? How will they communicate with humans, who have their own, subtle road culture? In addition, Fogarty and Sperling’s (2018) study suggested other key points about AV testing that should be considered in future testing activities. • Testing needs to be consistent throughout the life cycle of a component, which means that test vendors need to commit resources to maintaining test equipment for a specific product for a specified period. This is a big investment for technology that is still in the early stages of development. • Chips in AVs will need to last for a decade or more across a wide variety of sometimes harsh environmental conditions. So far, however, there are no SAE level 5 AVs on the road, so how they perform over time is entirely based on simulations. Testing schemes are still being developed to include realworld testing plus simulations. • As faster communications standards are rolled out, testing will move from physical testing of components to a combination of physical and over-theair testing, which will be required to ensure that communication speeds are sufficient to guarantee safety.

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MCity’s testing experiences (CSS 2020) and Davies’ (2016) study discovered some problems facing future testing activities as follows: • Several limitations and barriers exist that could impede the adoption of AVs, including the following: the need for sufficient consumer demand, assurance of data security, protection against cyberattacks, regulations compatible with driverless operation, resolved liability laws, societal attitude and behavior change regarding distrust and subsequent resistance to AV use, and the development of economically viable AV technologies (CSS 2020). • Weather can adversely affect sensor performance on AVs, potentially impeding adoption. Ford recognized this barrier and started conducting AV testing in the snow in 2016 at the University of Michigan’s Mcity testing facility, utilizing technologies suited for poor weather conditions (CSS 2020). • Age of self-driving cars is on its way, but a few tricky problems remain to be solved before we can all let go of the wheel. One of these is getting the robots to handle bad weather (Davies 2016). Self-driving developers have responded to these challenges by limiting when and where their cars go, restricting them to well-mapped, carefully selected terrain (CRS 2021). In general, testing areas can be divided into tricky and calm areas. In general, tricky areas have complex networks, bad weather, and traffic conditions wherein congestion and complex routing problems often exist. In contrast, calm areas have simple routes, well-maintained roads, simple traffic patterns, slower street speeds, and reliably good weather. Testing areas can be divided into different regions with geofences to alert AVs when they are entering tricky and calm areas (Marshall and Davies 2016).

3.18 SUMMARY Testing and validation of AV technologies, systems, and vehicles is a highly complex field of endeavor. This complication stems from the difficulty in fully specifying the requirements that need to be satisfied by the design and the massive variability in conditions that an AV may encounter in the field. Another challenge inherent to AV testing is the statistical issues associated with proving reliability against events and conditions that occur rarely. One common feature to challenges identified across multiple deployments is adapting to conditions that are outside of the expected rules or that outright contradict expectations. Construction zones changing lane usage, vehicles double-parked in travel lanes, human drivers failing to stop, wrong way drivers, and many, many more events are noted in multiple testing reports as causing developers to adjust their code. “Expect the unexpected” is a valuable motto and reminder for anyone developing AV technologies. Current deployments are cautiously testing their AVs, usually with supervisory drivers, and operating at low speeds. Public pressure and scrutiny

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have proven to be very dependent on publicity because of fatality reports in the news. This dynamic has resulted in slowdowns in testing and cooling of public support, justifying and reinforcing the caution many projects are exercising in testing. Looking to future developments, AV driving models probably require decades to refine far enough to enable true autonomous operation, given the current pace of development and practices. Innovative approaches and disruptive methodologies will be required to circumvent the challenges currently being experienced in refining driving model code. As AV technologies mature, standards and versioning changes over time will threaten the AV fleet with fragmentation. Fragmentation in the AV fleet may lead to cybersecurity vulnerabilities, divergences in vehicle behavior, and reliability issues.

References Angadala, V. 2020. “Why simulation is the key to test, train & build self-driving cars?” IGadgets World. Accessed October 2, 2020. https://www.igadgetsworld.com/ autonomous-vehicle-simulation/. Area X O. 2021. “Our facility.” Accessed August 26, 2021. https://areaxo.com/facility/. ASTM International. 2013. Standard test methods for evaluating wet braking traction performance of passenger car tires on vehicles equipped with anti-lock braking systems. ASTM F1649-13. West Conshohocken, PA: ASTM. ASTM. 2017. Standard specification for P195/75R14 radial standard reference test tire. ASTM E1136-17. West Conshohocken, PA: ASTM. ASTM. 2018. Standard test method for determining longitudinal peak braking coefficient of paved surfaces using standard reference test tire. ASTM E1337-90. West Conshohocken, PA: ASTM. Bishoff, L. A. 2019. “$45M autonomous vehicle testing center opens.” Dayton Daily News. Accessed October 28, 2019. https://www.daytondailynews.com/ news/45m-autonomous-vehicle-testing-center-opens/FjBmjHZGx0LFR8E6Fzp65N/#. Bloomberg Philanthropies. 2019. “Initiative on cities and autonomous vehicles.” Accessed October 28, 2019. https://avsincities.bloomberg.org/. Canis, B. 2019. Issues in autonomous vehicle testing and deployment. Rep. No. R45985. Washington, DC: CRS. CSS (Center for Sustainable Systems). 2020. “Autonomous vehicles fact sheet.” Accessed October 2, 2020. http://css.umich.edu/factsheets/autonomous-vehicles-factsheet. City of Grand Rapids. 2018. “City joins public-private coalition to bring autonomous vehicles to GR.” Accessed October 28, 2019. https://www.grandrapidsmi.gov/ShortcutContent/News-Media/City-joins-public-private-coalition-to-bring-autonomous​ -vehicles-to-GR. City of Peachtree Corners. 2019. “Curiosity Lab at Peachtree Corners.” Accessed October 28, 2019. https://www.peachtreecornersga.gov/businesses/ prototype-prime-startup-incubator/autonomous-vehicle-project. CRS (Congressional Research Service). 2021. Issues in autonomous vehicle testing and deployment. Rep. No. R45985. Washington, DC: CRS. Davies, A. 2016. “The clever way Ford’s self-driving cars navigate in snow.” Accessed October 2, 2020. https://www.wired.com/2016/01/the-clever-way-fords-self-drivingcars-navigate-in-snow/.

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PTV Group. 2019. “PTV VISSIM.” Accessed October 28, 2019. https://www.ptvgroup.com/ en/solutions/products/ptv-vissim/areas-of-application/autonomous-vehicles-and-new​ -mobility/. rFpro. 2019. “About rFpro.” Accessed October 28, 2019. http://www.rfpro.com/. SAFER-SIM. 2020. “University of Iowa ramping up automated vehicle testing on local rural roads.” Overview of Project “ADS for Rural America.” Accessed October 2, 2020. https://adsforruralamerica. uiowa.edu/news/2020/08/university-iowa-ramping​ -automated-vehicle-testing-local-rural-roads. Said, C. 2018. “Uber puts the brakes on testing robot cars in California after Arizona fatality.” San Francisco Chronicle. Accessed October 28, 2019. https://www.sfchronicle. com/business/article/Uber-pulls-out-of-all-self-driving-car-testing-in-12785490. php?psid=m7tgG. Smart Columbus. 2019. “Self-driving shuttles.” Accessed October 28, 2019. https://smart. columbus.gov/projects/self-driving-shuttles. Snyder, J. B. 2017. “Ford, Domino’s Pizza team up for autonomous delivery in Ann Arbor.” Auto Blog. Accessed October 28, 2019. https://www.autoblog.com/2017/08/29/ ford-dominos-pizza-autonomous-delivery-ann-arbor/#slide-7070696. SunTrax. 2021. “SunTrax accelerating… The future of transportation.” Accessed August 26, 2021. http://www.suntraxfl.com/. Transport Topics. 2018. “Volvo trucks develops autonomous vehicle called Vera.” Accessed October 28, 2019. https://www.ttnews.com/articles/volvo-trucks-develops​ -autonomous-vehicle-called-vera. Transport Topics. 2019. “Louisiana’s autonomous truck rules will take effect Aug. 1.” Accessed October 28, 2019. https://www.ttnews.com/articles/louisianas-autonomous​ -truck-rules-will-take-effect-aug-1. TRC (Transportation Research Center). 2017. “Transportation Research Center: About Us.” Accessed October 28, 2019. http://www.trcpg.com/about-us/. TxDOT (Texas Department of Transportation). 2017. “Texas chosen as testing grounds for automated vehicles.” Accessed October 28, 2019. https://www.txgov/inside-txdot/ media-center/statewide-news/01-2017.html. UM (University of Michigan). 2021. “Mcity test facility.” Accessed August 26, 2021. https:// mcity.umich.edu/our-work/mcity-test-facility/. Welch, D. 2019. “Don’t expect self-driving taxis anytime soon—GM delays Cruise robotaxi launch.” Los Angeles Times. Accessed October 28, 2019. https://www.latimes. com/business/story/2019-07-24/gm-cruise-self-driving-taxi.

CHAPTER 4

Emerging Delivery and Mobility Services Deogratias Eustace, Kakan Dey, Md Tawhidur Rahman, Baraah Qawasmeh

4.1  AUTOMATED DELIVERY AND LOGISTICS 4.1.1 Background Logistics is the planning and delivery process of goods transportation in a supply chain (i.e., transportation of goods from the point of production to the point of delivery). Logistics includes all activities in the supply chain, such as warehouse management, inventory management, material handling, purchasing, packaging, order processing, transportation, and customer service. The history of the evolution of logistics converges with the history of automation from the steam engine to robotic pickers and packers (Dekhne et al. 2019). Mass automation in the delivery and logistics sector has been immensely influenced by the growing shortage of labor, technology advancement, and customer expectations. Advancements in the global positioning system (GPS) and wireless and internet technologies have transformed the growth of the on-demand economy. Today’s customers expect different emerging delivery services such as parcel rerouting, post-shipment flexibility, quick, guaranteed, on-time, and even same-day delivery. By 2023, transparency market research predicts a massive 90% revenue growth of the global logistics market (PR Newswire 2016). Unarguably most of the revenue growth will be influenced by automated delivery and logistics. Trends in automation are moving in the direction of the use of unmanned delivery systems (e.g., air delivery drones, delivery robots, and self-driving trucks) in the near future.

4.1.2  Benefits of Automation of Delivery and Logistics As manual involvement in every step of logistics creates inefficiency and increases cost, automation has been incorporated in the goods supply chain for many years. Besides, automation provides real-time system analytics to operators 139

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and customers and improves the efficiency of the supply chain. Some of the major benefits in the automation of logistics and delivery in the transportation management system are listed as follows (Robinson 2019a). a. Increased accuracy: Manual management of delivery and logistics increases errors such as multiple payments for product shipping and inaccurate freight classification. Automation in delivery and logistics minimizes errors and reduces goods shipping and processing costs. Increased accuracy improves the reliability of the delivery and logistics systems. Accuracy in product handling is important in meeting high demand. To fulfill customer orders, an automated product picking system at the warehouse can maintain the picking accuracy above 99%, in addition to an 800% increase in the picking rate compared with manual labor (Tarr 2014). b. Real-time information: Real-time information (e.g., live carrier rates, transit time, and insurance status) reduces transportation costs and assists in decision-making with reduced uncertainty. Real-time information helps diverse stakeholders (e.g., shippers, freight carriers, and administrators) to pinpoint operational inefficiencies and develop solutions to improve efficiency in delivery and logistics operations. For example, real-time changes in customer orders can be adapted quickly in the management system to provide better customer service. Real-time information reduces the negative effect of freight transportation on transportation systems and the environment (Scott 2018). Using real-time traffic information, freight trucks can be rerouted through less congested routes or operated in off-peak hours. Optimized routing considering the environmental impacts decreases CO2, NOx, and other pollutants’ emissions (Taniguchi 2014). c. Improved customer service: Real-time accurate information on goods transportation improves the satisfaction level of customers. Automation in delivery and logistics helps move orders in the supply chain network faster. Internet of things (IoT)-enabled sensors and the product verification system reduce shipment processing–related delays. Automated notification of any unexpected delay reduces the volume of shippers’ and customers’ complaints and improves transparency in product delivery (Robinson 2019b, Scott 2018). d. Increased organizational control: Optimized management of goods transportation (e.g., operation cost and time) minimizes risk (e.g., late delivery and damaged product) in delivery and logistics operations. Delivery and logistics automation help businesses to grow (e.g., handling more freights, adding new users). Automated product handling can reduce 65% to 85% floor space requirements by effectively utilizing overhead space from floor to ceiling (Tarr 2014). Maintaining increased inventory in a limited space helps a supplier to handle high demand. The introduction of blockchain technology in the logistics market can enable organizations to improve product traceability, increase efficiency, and reduce occurrences

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of fraud. These outcomes of automated delivery and logistics operations help logistic managers to exercise more control over the activities of their organizations.

4.1.3  Automated Delivery and Logistic Applications In the following subsections, different applications of automation in logistics are presented.

4.1.3.1  Last-Mile Transportation Last-mile delivery involves the movement of goods from the nearest distribution hub to the final delivery destination (e.g., personal residence). Last-mile delivery usually works like the hub-and-spoke model. Increased demand for goods delivery in a densely populated urban area creates bottlenecks in the final stage of the supply chain, known as a last-mile delivery problem. Moreover, high demand during the holiday season exacerbates the last-mile delivery issue. To improve last-mile delivery services, companies continuously explore new delivery methods and strategies such as automated last-mile delivery. The development of drones, robots, and self-driving vehicles for automated last-mile delivery will lead to a reduction in the last-mile delivery workforce, unnecessary delivery delays, and associated costs. Boxbot, an automated delivery start-up, provides lastmile delivery solutions for e-commerce packages and other shipments through autonomous electric vehicles (Robo Business 2019). This system places delivery hubs close to residential neighborhoods to provide same-day or next-day delivery. Automated loading, sorting, and identification of packages save drivers’ time in package delivery.

4.1.3.2  Automated Freight Ports Automated port operation improves the efficiency of container handling. The automated operation utilizes the full potential of limited spaces at ports and improves operational efficiency and safety significantly. Automated container handling at ports helps in keeping pace with the increasing shipment volume with minimum additional infrastructure (Dillow and Rainwater 2018). The operation of gantry cranes that are used to lift containers from ships, transport vehicles, and transfer containers to the designated delivery locations and stack cranes used to pile up containers for truck pickup can be automated to improve operational efficiency. The automated container handling system reduces variable costs and improves port productivity (i.e., ships spend lesser time at ports) compared with the manual handling system. Automated operations at ports allow round-theclock operation. The port at Rotterdam, the Netherlands, is one of the world’s most advanced container shipment terminals and functions at a consistent and steady pace in terms of container handling with an increased efficiency of 80% through complete autonomous operations (Peterson 2015, MSU 2019). Two automated terminals in North America are LBCT’s Middle Harbor Facility in Long Beach,

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California, and the TraPac Terminal in Los Angeles, California. However, only yard operations are automated in these two US terminals (Mongelluzzo 2019).

4.1.3.3  Automated Warehouse Management The core of logistics and delivery processes is warehouse management (Scott 2018). A warehouse is usually used to combine and deliver shipments to delivery destinations such as retail establishments. In the era of the e-commerce revolution, warehouses need to handle large volumes of small online orders (Williams et  al. 2017). Through automated warehouse management, many problems related to manual management–related concerns such as operational delays, delivery bottlenecks, and product sorting can be eliminated. Automated warehouse management transforms physical assets into digital/automated tools and enables highly efficient asset handling mechanisms for logistics managers. However, automated warehouse management is not suitable for certain conditions or tasks. For example, the human workforce can act as an effective cover for each of its personnel in case of any emergencies, whereas automated technologies in the warehouse may not be able to perform such critical functions. Moreover, automated technologies may not be very effective for nonrepetitive tasks (Benevides 2019).

4.1.3.4  Automated Fleet Management Automation in fleet management minimizes the downtime of available freight transportation assets. Logistics managers need to consider various aspects of freight transportation, such as maintenance and delivery schedules, service routes, and vehicle usage, while managing vehicle fleets. Automation reduces inefficiencies in fleet management by developing and implementing optimized schedules, routes, and vehicle usage. The connected fleet operation enables logistic managers to capture, manage, use, and share fleet operation data for real-time decision-making. Enhanced connectivity ensures continuous communication with drivers and technicians anytime and anywhere for real-time updates (e.g., automated update of traffic and weather conditions, rescheduling, rerouting), which helps improve management in emergency situations.

4.1.3.5  Automated Reverse Logistics Reverse logistics represents the process of planning, implementing, and controlling the flow of raw materials, in-process inventory, and finished products from the point of consumption to the point of origin (Zarei et al. 2010). The main purpose of reverse logistics is to recapture values or initiate proper disposal of the returned products. An increase in consumer demand for a product subsequently increases the demand for reverse logistics. Data flow automation in associated processes in reverse logistics increases efficiency. The use of blockchain technology in reverse logistics reduces the chance of fraud and improves security in the product return process (Smith 2019). Automated grading of returned products (e.g., refurbish, liquidate, or scrap) results in efficient handling and minimizes the number of

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products dumped at landfills (Smart Returns 2018). Optoro, a return optimization platform, provides solutions to retailers and brands in reverse logistics process optimization. The company predicts that inefficient reverse logistics generates 5 billion pounds of waste and 15 million metric tons of carbon emissions every year in the United States (Optoro 2019).

4.1.4  Technology in Automated Delivery and Logistics Technological innovation and application are the core of automated delivery and logistics, which increases efficiency, safety, sustainability, and productivity of the freight transportation system. The following subsections explain several technologies used in automated delivery and logistic operations.

4.1.4.1  Technologies Used in Freight Delivery Freight delivery is a part of the supply chain of products from the point of origin to the point of destination. Freight trucks use different cyber-physical systems to automate different aspects of freight transportation. Cyber-physical systems use interconnected networks of physical and computational components to monitor and make operational decisions for automated freight transportation operations. The following categories of cyber-physical systems are used for freight transportation (Baroud et al. 2018). 4.1.4.1.1  Tracking of Assets Automated location tracking of assets such as tractors, trailers, chassis, containers, and rail cars provides real-time information about their current locations, operating and physical conditions, and so on. Lower-orbit earth satellites are used to collect real-time tracking information with high accuracy. The architecture of an automated asset tracking system consists of three parts: (a) a GPS tracking unit that collects asset location as well as other information, for example, fuel level, tire pressure, battery status, engine condition, and odometer reading; (b) a GPS tracking server that receives the data from the GPS tracking unit; and (c) an user interface that analyzes and summarizes the vehicle tracking data that are used in decision-making. Asset tracking is performed actively (the online tracking disseminates the collected information in real time) and passively (the offline tracking data can be collected after the completion of a ride). In most modern freight truck models, active and passive tracking are used simultaneously depending on the availability of a real-time communication link. A passive tracking system collects and stores information in locations with no internet connection and disseminates the stored information in the presence of a communication link. Geofencing is another asset tracking strategy that uses geographical coordinates to develop virtual boundaries or fences (i.e., geofence) around an asset to track the asset location by using GPS or radio-frequency identification (RFID) technologies (Baroud et al. 2018). If an asset crosses any of the virtual boundaries, an alert is generated and shared with the relevant users. In addition to tracking, a geofencing feature is used for enforcement purposes such as regulating

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the movement of freight trucks carrying certain types of heavy goods traveling through specific areas, routes, or tunnels (Reclus and Drouard 2009). 4.1.4.1.2  On-Board Status Monitoring and Control On-board sensors are used to monitor freight conditions, any tampering attempts, and vehicle operating parameters, and real-time adjustments are executed using algorithms. On-board diagnostics ensures proper handling of freights, reduces transportation costs, enhances driver safety, and improves customer satisfaction. Electronic speed checkers and collision avoidance systems in freight vehicles are used to monitor the real-time operations of a freight truck. Temperature sensors are used to control the temperature for perishable shipments. Gas sensors are used to measure stress on crops and pressure and toxicity sensors are used for the safe shipping of hazardous materials (Jedermann et al. 2006). The tamper prevention method is used for valuable freight transportation such as biohazards or weapons and includes an electronic seal. Drivers and management center operators receive notifications in the case of any unauthorized and unusual circumstances (Tuttle 1997). On-board status monitoring technology used in UPS delivery trucks captures information from 200 sensors, providing a rich stream of data for realtime monitoring and decision-making (Ernst 2010). 4.1.4.1.3  Gateway Facilitation Freight vehicles are often stopped at gateways such as terminal gates, inspection stations, or border crossings for security compliance checking. The slow manual checking process at these gateways creates bottlenecks. The American Transportation Research Institute estimated that bottlenecks cost $64.3 billion of lost productivity to the trucking industry, where most of this productivity loss occurred at gateways (Baroud et al. 2018). Technologies have been introduced over the years to increase the efficiency of freight truck movement through gateways. The identification of truck drivers can be verified through biometric identification tools such as fingerprints and iris recognition. Electronically stored databases of commercial drivers’ credentials in the Commercial Vehicle Identification Systems and Networks system can be used to reduce inspection frequency and time. X-rays and gamma rays are used to reduce freight truck inspection time. For vehicle identification at the gateways, radio-frequency transponders such as EZPass are used for the preclearance of freight trucks. Similar technologies are used at weighin-motion stations to reduce delays. 4.1.4.1.4  Traffic Status Information Traffic condition information assists in identifying the source of delay and making efficient and real-time operational decisions. Dynamic route optimization algorithms can be used to generate optimized routes using real-time traffic data and reroute freight trucks. INRIX provides real-time traffic flow and incident data to the GeoDecisions to guide nationwide US military logistics planning and homeland security operations (INRIX 2019). INRIX real-time data are incorporated into the IRRIS portal (an enterprise web application to support logistics operations for state and federal agencies and enterprise applications

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in private sectors such as state transportation agencies) to provide high-quality technology solutions in logistics operations.

4.1.4.2  Technology Used in Warehouse Management Up-to-date information about freights at warehouses is important for goods transportation. Automated logistics operations at warehouses reduce slowdowns, minimize human errors, and increase operational efficiency. Self-guided forklifts and pallet carts follow predefined paths through the warehouse for locating and picking up pallets or boxes at a steady speed (Marder 2016). The RFID technology is used to search specific packages and retrieve relevant information about a shipment (e.g., origin, destination, freight classification, special handling instructions). Two-dimensional barcodes such as QR codes can contain information up to 2,000 characters and increase the efficiency of the automated handling of freights. The advanced barcoding system uses powerful scanners that can decode barcodes from 50 ft away and enables managing and tracking inventory on higher shelves, reduces space requirement, reduces the time for climbing ladders, and improves safety. Automation of picking and sorting of products using robots benefits logistics operations in a warehouse significantly.

4.1.4.3  Future Technologies in Automated Delivery and Logistics The development pace of new innovative technology in automated delivery and logistics is unceasing. The following subsections present several emerging automated delivery and logistics technology under the development/testing phase. 4.1.4.3.1  Air Delivery Drones Air delivery drones can provide postal delivery, first responder service, medical supply delivery, and e-commerce delivery services. Delivery of goods using drones has the potential to solve the last-mile delivery problem. A drone can be faster and cheaper than the existing truck-based package delivery method. Two types of electric drones (e.g., multirotor drones and hybrid drones) are being tested for package delivery (Stanford Business School 2016). Hybrid drones equipped with propellers and wings have increased delivery range and allow lesser control in operation. An operational mechanism of drone-based automated package delivery is illustrated in Figure 4-1. Drone-based automated delivery can reduce last-mile transportation costs to $1 per shipment (Desjardins 2018). Customers can trace and schedule drone delivery with the help of mobile phone applications. Drones can be used to deliver goods in remote areas that are not accessible via the traditional delivery network. Many organizations such as Amazon, Google, Drone Delivery Canada, Walmart, UPS, DHL, and Domino’s Pizza have been developing and testing drone-based product delivery. Drone-based Amazon Prime Air service is committed to deliver products at the users’ doorstep within 30 min. UPS is estimated to save $50 million by reducing one mile in each delivery trip using drones (Desjardins 2018). However, a limited drone delivery range will require more warehouses and dispatch centers. Amazon has recently filed a new patent on an unmanned

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Machine readable QR code of the ordered package is provided to customer

Customer displays QR code at the nearby delivery location

Aerial delivery device (e.g., drone) receives relevant delivery information (delivery location, QR code) from package delivery system computing device

Package loaded in the aerial delivery device

Aerial delivery device transports package to the address provided as the delivery location

Aerial delivery device locates the QR code displayed at the delivery location Aerial delivery devices deposits the package on the QR code display

Figure 4-1.  Operational mechanism of drone-based automated package delivery. Source: Shucker and Trew (2016).

delivery vehicle’s operation in densely populated areas (Figure 4-2). This patent proposed a multilevel (i.e., stories, floors) warehouse/fulfillment center concept. Drone Delivery Canada (DDC), a Toronto, Ontario-based technology firm, focuses on designing, developing, and implementing a commercially viable drone delivery system in Canada. This company recently established a partnership with Air Canada and the Edmonton International Airport to implement drone delivery services in controlled airspace. The DDC’s largest and longest range drone can carry up to 400 lbs and travel up to 124 mi on a full tank of gas (Reagan 2019). However, the operation of drones must be regulated with appropriate laws to avoid unexpected circumstances such as compromising the safety of air transportation. The Federal Aviation Administration (FAA) in the United States has developed rules and regulations for four groups of drone users, namely (1) recreational flyers and modelers and community-based organizations, (2) certificated remote pilots including commercial operators, (3) public safety and government users, and (4) educational users (FAA 2019a). All drone pilots must register with FAA. Drone pilots can use B4UFLY mobile application developed by FAA and Kittyhawk to check whether it is allowed and safe to fly drones at different locations (Figure 4-3). This app provides information about controlled airspace, special use airspace, nearby critical infrastructures (e.g., airports, national parks, military training routes), and temporary flight restrictions to develop situational awareness to drone operators/pilots (FAA 2019b) (Figure 4-4).

Emerging Delivery and Mobility Services

Figure 4-2.  Multilevel fulfillment center for an unmanned delivery system. Source: Curlander et al. (2017).

Figure 4-3.  Flying restrictions check using B4UFLY mobile application. Source: FAA (2019b).

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The Australian Unmanned Aerial Vehicle (AUAV) company identifies major maintenance costs for repairing camera, radio modem, autopilot, servo, autopilot overheating, as well as battery and wings replacement. According to AUAV, the total maintenance cost may constitute about 25% of the purchase cost per year. Besides, a longer repair time can result in downtime costs (e.g., delayed shipment cost) that can be larger than the drone purchase cost. AUAV estimates a 2-year service life of a drone on average, which means a $1,000 per month depreciation cost for a $25,000 purchase cost per drone. Other drone delivery system costs include training and staff turnover, aviation insurance, and safety management (AUAV 2018). 4.1.4.3.2  Delivery Robots Land-based delivery robots can also automate the last-mile delivery system and have recently been used for the food delivery system. Owing to the presence of significant interruptions in land-based delivery, the operational speed of delivery robots is lower than that of delivery drones. However, the chances of unexpected harmful events occurring (e.g., low-severity collision with any moving and static roadway objects) are low because of the lower operational speed. Privacy concerns associated with delivery robots are also lower than those associated with flying drones. A food and grocery delivery company, Postmates, claims that their electricpowered robots can carry up to 50 lbs and travel up to 30 mi on a single battery charge (Peters 2019). Using GPS, cameras, and ultrasonic sensors, robots decide the next maneuver on the sidewalks by avoiding vehicles, pedestrians, or other obstacles. However, delivery robots can create new types of obstacles for pedestrians on sidewalks, especially senior citizens, children, and people in wheelchairs (Constine 2018). Human operator(s) track the operation of robots from the control center and deploy(s) appropriate measures in situations of emergency (e.g., accidents or thefts). Customers can access only the deliveries that belong to them by using an access code or facial recognition system (Stanford Business School 2016). 4.1.4.3.3  Automated Freight Trucks Several autonomous features are available in current truck models, such as adaptive cruise control and lane-keeping assistance. Fully autonomous trucks can possibly be commercialized and deployed by shippers by 2030 (Dougherty et al. 2017). Commercial freight operators are expected to be the early adopters of autonomous trucks, as the large size of their fleets will ensure a quick return on investments. Because self-driving trucks will reduce the need for drivers and decrease freight transportation costs significantly, the problem of the constant shortage of truck drivers in the United States will be subsequently resolved. Advanced technologies (e.g., sensors, cameras, radars, and automatic emergency braking systems) in self-driving trucks may improve safety, as trucks represent approximately 9.5% of traffic fatalities in the United States (Sputnik International 2016). Currently, self-driving features in vehicles are extremely helpful to warn distracted drivers in avoiding crashes. Convoying or platooning features of selfdriving trucks will add further benefits such as improved fuel efficiency and increased roadway capacity (Figure 4-4).

Source: Designed by Dey based on the discussion of the computing role in automated assistance to truck platooning by Tita and Ramsey (2015).

Figure 4-4.  Illustrated concept of platooning of trucks.

Close spaced platoon reduces aerodynamic drag and saves fuel

Radar based braking system see as much as 800’ ahead

Adaptive cruise control system maintains precise close spacing between trucks (as close as 30’)

Front truck’s camera allows rear truck drivers to see the road ahead in real-time

Front/lead truck controls the truck platoon operation

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4.1.5  Policy Considerations Automation in freight delivery and logistics has great potential in terms of improving the efficiency of complex freight transportation systems. Although most of the high-impact technologies are still in the early stages of development (e.g., drones, robots, self-driving trucks), it is important to develop policies to maximize the benefits and minimize the negative consequences. For example, approximately 1.7 million truckers are there in the United States, and their source of income will be influenced by automation. Automated handling of goods delivery and logistics will reduce human workforces significantly. Initiatives such as intensive skill development programs for high-demand occupations can help train the displaced workforce to enter new career paths (Lee 2018). Noise, privacy, and safety are some of the potential concerns regarding automated drone delivery. Active noise canceling and the bladeless system can be used to avoid noise (Aurambout et al. 2019). To improve safety and privacy, FAA restricts the flying of drones over people and requires monitoring from the ground all the time. Moreover, restricted access may increase delivery time and operational costs because of detouring. Concerns over operational conflicts with other traffic, especially with pedestrians, can lead to the authorization of the operation of delivery robots mainly in low-density areas. Automated delivery of goods warrants changes in the transportation system. The Metropolitan Area Planning Council identifies the following policy considerations for the automated delivery of goods (MAPC 2017): • Introduction of necessary safeguards (e.g., in terms of traffic control at intersections) before allowing automated delivery in a particular region; • Operation of automated goods delivery should not interfere with the operations of other traffic (e.g., autonomous and nonautonomous vehicles, public transits, bicyclists, or pedestrians); • Implementation of automated delivery–friendly street design concepts (e.g., curb ramps); and • Implementation of residential building designs suitable for automated delivery (e.g., allocating space for ground floor deliveries and installing package lockers). It is also important to evaluate feasibility of the automated delivery and logistics systems by performing a life cycle cost analysis (i.e., estimate the corresponding manufacturing, implementation, and maintenance costs and monetary value of the effect transportation system and environment) and evaluating the differences with the life cycle costs of traditional delivery systems before implementation (Aurambout et al. 2019).

4.1.6  Future Research Directions With the introduction of advanced technologies in automated delivery and logistics, a major research challenge is to develop and maintain a secure, costeffective, and safe system in an IoT environment. Future research should focus on

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the challenges of the implementation of complex automated delivery and logistics systems. Research on the public acceptance of automated delivery and logistics operations can provide significant insights into the human-centered operational improvement of automated delivery and logistics technologies. The evolving role of different stakeholders (e.g., labor unions, truck drivers, fleet operators) involved in delivery and logistics platforms requires a careful consideration of the workforce consequences. A systematic transition of the roles of the diverse stakeholders can ensure the progress of future automated delivery and logistics systems.

4.2  MOBILITY AS A SERVICE The emergence of Mobility as a Service (MaaS), also known as Transportation as a Service, or sometimes referred to as smart mobility, has led to a shift in the desire of individuals to own their personal vehicles as the shared mode of transportation and replaced them with mobility and transportation solutions, which are available as a service. This is usually provided through the integration of different public transportation modes and mobility options’ providers and through combining them into a single, virtual multimedia application that manages the trips users pay for from their accounts; this payment can be a per trip one or a restricted monthly fee (Hensher 2017, Wong and Hensher 2021). Using mobility as a service application enables users to plan for multimodal trips using one user interface. For example, when planning to go to the beach, you can book a bike share to get to the bus station and then be transported by a public bus to another location where you can use a car share to reach your desired final destination. All these bookings can be made through one application app using your smartphone for seamless trips (Utriainen and Pöllänen 2018). MaaS has been designed to provide convenient and practical solutions to transport people from one place to another to their choice locations through monthly subscriptions or other payment arrangements. The common mobility modes that can be used through these applications are taxis, public buses, commuter trains, and other modes of shared mobility (MaaS Global 2016). MaaS is a concept that provides a wide range of integrated transportation and mobility options, including cars, bikes, scooters, public transportation, taxis, and so on. In addition, MaaS includes trip planning and payment options that enhance and improve the users’ experience of purchasing transportation services. It is a real revolution that serves individuals in organizing and planning their trips (Intelematics 2018). Utriainen and Pöllänen (2018) comprehensively reviewed mobility as a service in scientific publications. Their review recognized the strong focus of prior MaaS research on private car users and ways of changing travel patterns of this user group through providing a higher service level. The review concluded that successful MaaS implementation is envisioned to enable the shift of more private cars toward sustainable transportation modes.

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Figure 4-5.  Illustration of a MaaS system. Instead of relying on owning private cars or using multiple uncoordinated transportation modes to reach your destination, the concept of MaaS is to combine all available multimodal mobility options such as taxis or ride-sharing options through a single user interface that can be accessed via the user’s smartphone to pay once and use all these modes in one trip, as illustrated in Figure 4-5. This service enables you to plan trips by using transportation options provided by a variety of service providers (MaaS Alliance 2019). ERTICO, which is a network of ITS and services stakeholders in Europe, defines MaaS as a smart system that provides special mobility solutions to support individual mobility desires by placing system elements (the traveler, the product, and associated information) at the heart of mobility services (NCMM 2018). MaaS Alliance (2019) also defines MaaS as the integration of various forms of transport services into a single mobility service accessible on demand. This ensures fast and flexible access to various travel and transportation services in a variety of options for the end users directly, which is the main aim behind MaaS programs. MaaS, also known as integrated mobility or combined mobility, can be defined in simple terms as a system that provides all transportation and mobility requirements for users and travelers through a single virtual service layer. This depends on

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the collection of all transportation possibilities and travelers’ desired options in one application. This means that instead of focusing on purchasing and owning cars, individuals can directly buy the mobility services they need (Utriainen and Pöllänen 2018). Currently, urban mobility applications specialized in mobility and transportation services, such as Uber, Lyft, and so on, are making efforts to increase and enable MaaS on a global scale (Hensher 2017).

4.2.1  Role of Mobility as a Service in the Context of Smart Cities Techopedia (2019) defines a smart city as a designation given to a city that incorporates information and communication technologies (ICT) to enhance the quality and performance of urban services such as energy, transportation and utilities in order to reduce resource consumption, wastage and overall costs. The Smart Cities Council (2016) says that a smart city uses ICT to enhance its livability, workability and sustainability. It collects information about itself using sensors, devices or other systems, and sends the data to an analytics system to understand what’s happening now and what’s likely to happen next. The future of smart cities will depend on networked systems sharing rich data about their roads, traffic, utilities, and so on, and they will integrate smart mobility as one of the smart solutions available at their disposal to reduce traffic congestion and improve transportation efficiency and improve citizens’ lives (Kelly 2018). The smart city concept has been billed as a solution to look into the needs and challenges in urban areas by eying the potential of ICT. Cities around the world have taken on a variety of strategies to improve their economic competitiveness, sustainability, social and capital attractiveness, and most important, quality of life for everyone (Alizadeh et al. 2018). The concept of smart cities is expected to rely and work in coordination with other systems or concepts such as MaaS and IoT to link multiple different aspects of a city’s infrastructure to react to real-time events such as traffic signals reacting to traffic patterns and adjusting accordingly. The concept of smart cities is an extremely promising concept that can greatly enhance the lifestyles of citizens because all aspects of life will begin to work synchronously with one another (BMAAS 2019). Thus, MaaS, with all the benefits it provides, will play a major role in the development and implementation of smart cities (Barreto et al. 2018, BMAAS 2019). Sensor-based IoT technology is behind much of the smart city revolution as smart city programs continue to roll out worldwide and also behind consumer behaviors toward sharing economy and preference of using smartphone apps. All these increase the potential of MaaS to become one of the most important components of smart city innovation that will improve the quality of life for those living and working in urbanized areas (Smit 2018). Both smart city and MaaS concepts are fueled by the same technological advancements (i.e., ICT, IoT). By providing customers with personalized, end-to-end journey travel options,

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MaaS inspires travelers to pursue modes of transportation other than driving a private car and, as a result, contributes to smart cities’ objectives by potentially curbing congestion in cities by reducing the reliance on private vehicles as a primary means of transportation (Smit 2018). The continued rollout of connected and automated vehicles (CAVs) and electric vehicles will come with many attendant benefits to societies in the form of environmental aspects and road safety, thus contributing to the smart cities agenda. As CAV and electric vehicle market penetration grows, IoT technology will ensure that these cars will become more connected than ever before and further contribute to the continued innovation in the smart city transportation agenda, which is mainly driven by MaaS solutions (Smit 2018). Thus, MaaS is perceived as an enabler of smart cities by integrating different transportation service options to create inclusive, efficient, and dynamic smart cities by taking advantage of the technology-driven transportation services to improve access to, and the efficiency of, the existing transportation network (Zhuwaki 2018). Flourishing smart cities require advanced, connected, and well-performing public transportation systems to attract travelers away from driving personal cars. Thus, MaaS becomes vital in contributing to the concept of smart cities. Traffic congestion and its impacts (e.g., environmental emissions, delays) are some of the major challenges most cities are always battling with, and they are on top of the agenda of smart cities initiatives. Introducing financial and behavioral incentives to new mobility options (e.g., ride-sharing) can reduce the negative impacts of traffic congestion and, thus, can accelerate the integration of MaaS into smart city implementation plans (Zhuwaki 2018). Integrating MaaS into a city involves an integration of a wide range of transportation modes. This denotes that smart cities require functional public transportation systems offering several modes such as mass transit (e.g., trains, trams, buses), nonmotorized transportation facilities (e.g., pedestrian walkways, bicycle lanes), carsharing, and technology-enabled services (e.g., ride-sharing, ride-hailing) (Zhuwaki 2018). It is only when all these various transportation modes and facilities are well coordinated and working in a multimodal manner under a MaaS platform can the smart cities’ concept be easily apprehended.

4.2.2  Implementation Features of Mobility as a Service The advantage of MaaS is the ability to bring together different types of transportation options in one easy-to-use application; that is, it can easily integrate various transportation modes from different service providers and make them accessible to users directly in a single mobility service on-demand, such as the unified mobile app, multimodal journey planning, service bundles, fixed monthly subscriptions, and pay-as-you-go billing (Dalton 2018). All these payment options are available to users depending on their needs and desires, whether the user wants to buy the service on-demand or wants to buy an inexpensive monthly package. The preceding discussion shows that MaaS manages your transportation

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options in the best possible, smartest, and easiest ways (Dalton 2018, Utriainen and Pöllänen 2018). Currently, various applications are available that provide options for multiple users to plan their trips, such as Google Maps, Whim, TripGo, and so on, with which more users are becoming more familiar. MaaS has made progress in this aspect, as it provides many public and private transportation solutions and several payment and planning trip options. Although most payments can be made through mobile apps, other forms such as transit cards, tickets, and so on are available to users, who can use them conveniently through single applications (Dalton 2018, MaaS Alliance 2019). MaaS can provide users with door-to-door mobility services from various service providers by using a single payment application as opposed to having users search for every transportation option or booking a ticket and paying for each option separately. MaaS will always provide the most suitable modes of transportation that should be used by monitoring the conditions of the transportation networks during specific time periods and knowing the user preferences about the period, comfort, value, and so on (Transdev 2019). In this subsection, we introduce some features that complement the definition of MaaS, namely, the core characteristics of a MaaS-based transportation system, a topology of MaaS integration, and key elements of a MaaS ecosystem.

4.2.2.1  Core Characteristics of Mobility as a Service We have already highlighted that MaaS aims at providing a wide range of transportation services to the customer and attempts to make choices and decisions easier through a single application app. Durand et al. (2018) put forth a list of nine core characteristics that a successful MaaS-based transportation system should have, which is reproduced as follows: 1. Integration of transport modes, in which multiple modes are combined in one single platform, thereby allowing users to take trips using multiple modes. These modes can be both traditional modes (e.g., public transport, private cars, and bicycles) and shared mobility modes. 2. Tariff option, that is, the fact that MaaS platforms offer a choice between payas-you-go and mobility packages (containing certain amounts of kilometers (or miles)-minutes-points that can be used for traveling in exchange for a monthly subscription fee). 3. Single platform where users can plan, book, pay for, and get tickets for their trips, as well as find real-time information. 4. Multiple actors, from customers and providers to platform owners, data management companies, and authorities, among others, because MaaS is built on the interaction among various such parties. 5. Use of technologies, because MaaS relies on smartphones, internet networks, ICT, and data systems.

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6. Demand orientation, as MaaS is a user-centric paradigm seeking to offer tailored solutions to users. 7. Registration requirement, which both facilitates the use of the service and allows for customization. 8. Personalization that ensures that the needs of users are met more efficiently. Travel history and expressed preferences serve to provide tailored recommendations. 9. Customization, enabling users to modify the offered option based on their preferences.

4.2.2.2  Mobility as a Service Integration MaaS is often described in terms of integration (Durand et  al. 2018), and it comprises five types of integration, namely (1) payment, (2) ticketing, (3) bundles, (4) information, and (5) service. According to Durand et al. (2018), a new integration feature of MaaS, which makes it different from the traditional concept of mobility integration, is bundle integration. According to Durand et al. (2018) and Wong and Hensher (2021), bundle integration happens when a user buys a mobility package or bundle in the context of MaaS and a user prepurchases predefined sets of credits on a fixed basis for a combination of included modes. These credits could be in time, distance, or money units (percent discount), with predetermined service-level agreements (Durand et al. 2018, Wong and Hensher 2021). According to Durand et al. (2018), “packages would have a fixed price and they could also include extra services such as grocery delivery, the guarantee of a stable Internet connection and silent spaces in public transport, free snacks, etc.” Sochor et al. (2018) proposed MaaS topology with five levels (Levels 0 to 4), defining different levels of integration as follows: (1) Level 0: no integration; (2) Level 1: integration of information; (3) Level 2: integration of booking and payment; (4) Level 3: integration of the service offered, including contracts and responsibilities; and (5) Level 4: integration of societal goals. These levels are summarized in Table 4-1 and briefly explained thereafter (Sochor et al. 2018). 4.2.2.2.1  MaaS Level 1 This level is characterized by the integration of information, which can be categorized as (1) only centralized information, (2) multimodal travel planner, and (3) assistant. The added value of Level 1 is decision support provided to the traveler for finding the best trip, and it focuses on a single trip and has users rather than customers. Transportation service providers are mainly public transportation agencies that provide open, standardized data for free. 4.2.2.2.2  MaaS Level 2 This level is characterized by the integration of booking and payment. A Level 2 service focuses on single trips and can provide the user with a travel planner, public transportation ticketing, taxi, or other transportation services if available. The added value of Level 2 is providing users with easier access to services, that is,

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Table 4-1.  MaaS Topology Levels. Topology level

Description

4

Integration of societal goals: Policies, incentives, and so on. Integration of the service offered: bundling/subscription, contacts, and so on. Integration of booking and payment: single trip-find, book and pay Integration of information: multimodal travel planner, price information No integration: single, separate service

3 2 1 0

Examples

UbGo, Whim Hannovermobil, Smile, enfach mobil Moovit, Qixxil, Google Hertz, Lyft, Sunfleet

Source: Developed based on Sochor et al.’s (2018) discussion of their proposed concept for the topology of MaaS at Levels 0 to 4 versus examples.

a one-stop-shop where they can search services, book, and pay with a single app. The required platform for Level 2 service is the business. 4.2.2.2.3  MaaS Level 3 This level is characterized by the integration of the service offered, including contracts and responsibilities. The added value of Level 3 is the provision of a comprehensive alternative to car ownership, with a focus on taking care of all mobility needs of the customer and the transportation service providers’ increased attractiveness to customers they cannot reach as single services. The Level 3 service is bundled, for example, it is subscription-based. The Level 3 service requires an ICT platform to run the business. It is noteworthy that the complexity of the technical integration can be lower for a Level 3 service than for a Level 2 service owing to fewer suppliers and lesser interaction. 4.2.2.2.4  MaaS Level 4 This level is characterized by the integration of societal goals. This is the highest level of MaaS integration. The added values are to reduce private car ownership and use, reduce congestion, create a more accessible and livable community, and so on. Intentional policies and incentives by public authorities at all levels of government can influence users’ behaviors.

4.2.2.3  Key Elements of Mobility as a Service Ecosystem A city or an urban area can start developing a MaaS solution by utilizing existing transportation infrastructures and private-sector partners already available. Goodall et al. (2017) share the main features or components required to implement a MaaS solution, which are discussed as follows:

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4.2.2.3.1 Infrastructure The infrastructure component for MaaS involves two types: technology- and connectivity-based. The technology-based infrastructure component for a successful MaaS requires the following conditions: (1) widespread availability of smartphones on 3G/4G/5G networks; (2) high levels of connectivity; (3) secure, dynamic, up-to-date information on travel options, schedules, and updates; and (4) cashless payment systems. The connectivity-based infrastructure component requires the integration of physical infrastructure that enables multimodal transfers between transportation services, for example, local bus and commuter train stations, bike- and carsharing spaces at bus and train stations, and so on. Transportation planners should think through how the various modes link up. To enable these conditions, there are many players who are expected to cooperate and contribute to this end of MaaS. These key players include mobility management firms, cellphone providers, payment processors, public and private transportation providers, and local authorities. 4.2.2.3.2  Data Providers The data provider acts as a bridge between a transportation operator and an end user. As previously discussed, users access the MaaS multimodal trip planning system through a digital platform, which can be either an app in a smartphone or a webpage account link through a computer or tablet. More advanced apps can provide a range of transportation options and offer realtime traffic conditions along the routes. Some of the well-known apps include CityMapper, Moovit, Ally, and so on. The data provider manages data from multiple service providers, provides the application programming interface gateways and analytics on usage, demand, planning, and reporting, and makes them available to the user. 4.2.2.3.3  Transportation Operators Transportation operators are the most essential players of any MaaS program. They consist of public and private transportation operators. To provide more transportation options, new innovative modes of travel have been added to the lineup, such as bike sharing, carsharing, and so on, which can be accessed through a single multimodal access app. However, Goodall et al. (2017) assert that due to gaps in public transportation services have fueled a growing army of small-scale private providers, each offering a specific service: parking, carpooling, peer-to-peer car clubs, ride-hailing, or on-demand bus rides. Typically, each operator requires its own app, with a separate interface and payment mechanism, and each service maintains its own customer relationships. These are kinds of services that a successful MaaS will combine and make accessible through a single mobile app, and a customer can pay once and use a combination of some of the transportation services available for a single trip (see the last component).

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4.2.2.3.4  Trusted Mobility Advisor The newest and most essential component that makes MaaS unique and competitive as a transportation option is the use of third-party advisors. The trusted mobility advisors link the services of the various private and public operators, arranging bookings and facilitating payments through a single app gateway. Currently known third-party app platforms include Whim, Moovel, UbiGo, Qixxit, SkedGo, and so on. According to Goodall et al. (2017), “an important factor in making MaaS a success will be getting all the players to work together.”

4.2.3  Review of Mobility as a Service Initiatives around the World The concept of MaaS, which is widely recognized as a disruptive innovation, aims to change the entire transportation industry in the digital era by offering customers mobility solutions based on their travel needs and desires. The MaaS concept was originally initiated in Finland, where currently it is already playing a key role in the national transportation policy (Hietanen 2019). Sampo Hietanen, the former CEO of ITS Finland, is the founder of the MaaS concept and has been leading the way in spreading the concept and delivering the service globally (Hietanen 2019). The name Mobility as a Service was used publicly for the first time in 2012. The term was chosen by the Director General of the Ministry of Transport and Communications of Finland. In 2013, Finland introduced a New Transport Policy in which mobility and logistics were perceived as a service and billed to be a source of growth, competitiveness, and well-being (Hietanen 2019). In 2012, Stuttgarter Straßenbahnen (SSB), a major public transportation operator in the city of Stuttgart, Germany, and Moovel (now known as REACH NOW) initiated a collaborative project aimed to simplify customers’ access to mobility. In 2015, Moovel availed new tools that allowed individuals to book and pay for public transportation tickets, in addition to accessing Car2Go (carsharing), mytaxi (ride-hailing), and Deutsche Bahn (trains) via one app (Wray 2019). In early 2018, SSB introduced BestPreis powered by Moovel. This new app can calculate the best price for users based on their travel history. Later in June 2018, SSB Flex, the newest app, was launched, in which users could book trips for themselves or for up to four other travelers, and if a booking was made for several people at once, each person in the group would receive a discount (Moovel 2019). In June 2016, MaaS Global, a start-up company founded by Heitanen (who resigned from ITS Finland in summer 2015 to establish MaaS Global), introduced the Whim application to the public, the first-ever MaaS application. The Whim app went on trial applications for two months, and in October, the first commercial ride was made using the Whim app in Helsinki, Finland (Hietanen 2019). The Whim app success story started on July 11, 2018, when they recorded a milestone of 1 million trips, and just three months later, on October 26, 2018, the trip numbers doubled to 2 million, which was an early indication that MaaS was on an upswing (Hietanen 2019). Helsinki’s MaaS success story was quickly noticed around the world, as many major cities worldwide started to strategize several motives to initiate

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similar programs. In December 2016, MaaS Global and Nextbike, the world’s leader as a bikeshare provider, introduced the mobility app Whim in the UK. This app endows individuals in Birmingham, West Midlands, and then the rest of UK with the capability of assisting customers get all sorts of transportation needs (e.g., trip planning, booking, ticketing, and payment) through a single app (MaaS Global 2017a). In May 2017, the Karlsruhe Transport Authority (KVV) launched a free KVV.mobil app in which users could easily access a network of public and private mobility services in Karlsruhe, Germany (Wray 2019). The app shows real-time connections and departure times of buses, trains, and trams at all stops in the KVV operating region, along with accessing rental bicycles from Fächerrad, the regional bike-sharing provider, and StadtMobil, the carsharing provider (Wray 2019). All tickets and bikes can be booked and paid for directly in the KVV.mobil app. In September 2017, Maas Global launched another Whim mobility service for Antwerp, Belgium, which started with a pilot project of test users in 2017 with a planned commercial launch to follow in early 2018 (MaaS Global 2017b). Currently, Antwerp is considered one of the premier cities that offers MaaS services in Europe. Now Whim is the only provider in Antwerp offering a MaaS subscription; via Whim, a customer can access De Lijn’s buses and trams (which provides services to the entire Flanders region), the Velo city bikes, taxis, and Sixt rental cars (Smart Ways to Antwerp 2019). In October 2017, Transport for Greater Manchester (TfGM), working with Atkins/SNC-Lavalin, launched a MaaS trial to get initial insights into the user behavior and perception of MaaS. They brought to the table a wide range of stakeholders with a specific aim of developing a MaaS solution tailored to the needs of the Greater Manchester region (Wray 2019). TfGM MaaS study is still ongoing to date, categorizing roles for different stakeholders in the MaaS commercial ecosystem. In addition, TfGM is developing and analyzing business platforms and operating frameworks and financial models for MaaS in collaboration with the European Commission’s Horizon 2020 program–funded projects, namely, iMOVE and MaaS4EU, which are being implemented/tested in the same region (Wray 2019). Sochor and Smith (2019) highlight several MaaS projects going on around Europe. iMOVE, a European Union (EU)–sponsored project, was launched in 2017 and is expected to run up to 2020. The main purpose of the project is to accelerate deployment and unlock large-scale access to integrated mobility (MaaS) schemes throughout Europe, paving the way for a roaming service to all MaaS users across the continent. The IMOVE project has pilot living labs in operation in Gothenburg (Sweden), Berlin (Germany), Turin (Italy), Manchester (UK), and Madrid (Spain), which are investigating and validating advanced MaaS solutions at the European level (Sochor and Smith 2019, iMOVE 2019). IRIS is another EU-sponsored project that started in 2017 and is expected to run up to 2022 (Sochor and Smith 2019, IRIS 2019). The project’s aim is to improve urban life through more sustainable integrated solutions, that is, co-creating smart and sustainable cities with mobility, energy, and ICT initiatives as their

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main components. This project is being developed in three so-called lighthouse cities in Utrecht (the Netherlands), Nice (France), and Gothenburg (Sweden), which are supposed to work as collaborators, and the test-bed cities are Vaasa (Finland), Alexandroupolis (Greece), Santa Cruz de Tenerife (Spain), and Focsani (Romania) (IRIS 2019). Lindholmen Integrated Mobility Arena is a MaaS project that started in 2018 ant it was supposed to be a 1-year pilot project that was initially scheduled to run from 2019 to 2020 involving 1,000 employees at the Lindholmen Science Park in Gothenburg, Sweden, but due to Covid-19 slowdown, the pilot was extended to the end of September 2021. The pilot test project combined various modes of transportation and parking in a smart app whose purpose is to simplify local and regional trips, decrease congestion on Lindholmen, and contribute to a more sustainable city (Drive Sweden 2022). Through one service app, pilot participants were able to use public transportation, bike sharing, e-scooter sharing, and taxis, and access a fleet of shared cars via shared private or company cars and a public carpool. In October 2018, FASTLinkDTLA, Downtown Los Angeles’ transportation management public–private partnership (PPP), launched a new ride-sharing pilot project as a counterpart to the city’s public transit network, and this service is offered in the evenings when there is a low demand for public transit services (Wray 2019). They use a white-label mobile app powered by Moovel, which lets individuals plan and pay for their on-demand trips, and the program can provide door-todoor services, and customers who are heading in the same direction share the ride (Wray 2019). Another notable project is the UbiGo relaunching in Stockholm, Sweden, in 2019 following a successful pilot study done in 2013 to 2014 with 70 paying households in Gothenburg (Sochor and Smith 2019; Fluidtime 2019). The mobility App UbiGo is an intermodal on-demand mobility service that provides an individual with access to public transportation, carsharing, rental car services, and taxis through a single platform. Through a flexible monthly subscription account, family members can share the same account (Fluidtime 2019). In May 2019, the Transit Authority of River City (TARC) of Louisville, Kentucky, announced the launch of a MaaS platform that includes a multimodal trip planning mobility app and a Dynamic Trip Planner on their website (TARC 2019). TARC’s new integrated mobility platform enables customers to seamlessly plan trips across multiple modes of travel, including TARC, Uber, Lyft, Bird Scooters, and LouVelo Bike Share. Instead of locating and booking each travel mode separately, this MaaS platform allows customers to plan and book their entire trips (door-to-door), all in one place (TARC 2019, Mobility on Demand Alliance 2019). This platform provides real-time data and analytics on system usage, trip origins and terminations, mode of travel, and rider cost savings from app usage and integrates payment services (Mobility on Demand Alliance 2019). In July 2019, Toyota Motor Corporation announced an agreement with Didi Chuxing (DiDi) to expand collaboration in mobility as a service (MaaS) project in China (Toyota 2019). Through this joint venture, the two companies will establish vehicle-related services for ride-hailing drivers. Toyota and DiDi promised to

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contribute to create a mobility service that will bring comfort and benefits to the people in China by leveraging the expertise, services, and technologies of each company (Toyota 2019). Many more pilot projects at different stages are ongoing throughout the world. Some notable ones are being pursued in Amsterdam in the Netherlands, Vienna in Austria, Copenhagen in Denmark, Toronto in Canada, and Singapore (MaaS Global 2017b).

4.2.4  Application of Technologies in Mobility as a Service The provision and successful launch of mobility as a service deployment highly depend on internet-based and wireless technologies. The core function of MaaS is to move people to get them to their destinations in a smart way, which hinges on the digitization of acquiring transportation data, performing analytics, and providing the required information to the end user in a simple way. The conception of a seamless on-demand service that MaaS strives for requires a combination of various forms of transportation, technology, and user adoption (Barreto et al. 2018, Singer 2018). The three core technologies, that is, mobile, big data, and the IoT, are behind the successful deployment of MaaS. The smartphone, which is a mobile device, allows individuals to plan, book, and pay for a trip through a single app in which the three technologies combine to provide a visual result to the user. MaaS is taking advantage of the recent rapid advances in wireless communications technology, which tremendously improved smartphones connectivity. Singer (2018) points out that because the wireless network has become common and available almost everywhere, it has given birth to the IoT, the interconnection via the internet of computing devices embedded in everyday objects, enabling them to send and receive data and interact with people in real time. The IoT has now become an integral part of our daily life. Numerous sensors that are connected to physical objects in this mobility era control how transportation systems, vehicles, traffic controls, and smart parking lots function (Singer 2018). As pointed out previously, among other things, technology is an essential component for MaaS to be able to provide individuals with on-demand access to public and private real-time mobility services (König et  al. 2017). It also enables further increased transparency in terms of mobility costs. Technology integration into mobility services is the one that constitutes the system we call MaaS; it enables functions such as route planning, booking, and pricing/ticketing/ payment possible on-demand and in real time in a seamless manner through a simple single application. In summary, in terms of technology, a successful MaaS requires internet and communication technology. According to König et  al. (2017), the following are the elements of MaaS service in terms of technology requirements: • Data requirements (e.g., common data formats, applied standards/ specifications), • Service requirements [e.g., common interfaces, open APIs],

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• Physical requirements (wireless networks) [e.g., wireless local area (WLAN) availability, 5G technology], • Legal requirements (e.g., roaming regulations, security, privacy), and • Usability requirements (e.g., accessibility, interface design). Interaction between travelers (end users) and transportation operators or service providers enabled through the app in the travelers’ mobile devices is a key for consuming final MaaS services. In addition, the technology required to accomplish the physical requirements is provided through the presence of realtime wireless networks (König et al. 2017). According to König et al. (2017), the evolvement of 5G and 4G/3G mobile network technologies together with the integration and expansion of local wireless communication networks (e.g., WLAN and Bluetooth) pave the way towards a seamless access environment to MaaS systems. Some notable mobile apps currently in operation are listed as follows: 1. Find My Ride PA: This is an application based in Pennsylvania. It provides an ideal example for serving individuals who identify and evaluate the services they require to suit their needs. The app has now spread to seven counties in Pennsylvania (York, Lebanon, Dauphin, Cumberland, Franklin, Adams, and Cambria), and it can be expanded to additional counties in the near future. The transportation services provided by this application are restricted by the public transportation network options such as buses that follow a fixed route or business transportation services such as nonpublic bus carriers, trains or taxis, and nonprofit alternative transportation options (NCMM 2018). 2. Transit App: This application combines various travel modes (except the individual driving option) into one useful user interface that allows individuals to visualize the services that are offered at any location they want to go and the mobility options they would need there. In Washington, DC, the application has introduced the integration of bike-sharing services in different regions. In addition to this, the program provides information about the length of the entire trip according to the modes of mobility used, including the walking time to the stations or time of riding the bicycle. This helps travelers transfer and open the applications required to complete their trips (NCMM 2018). 3. Citymapper App: This program serves trips that rely on more than one mode of transportation to get to the same destination, such as combining the bus and train to reach the required destination. This application provides users with the easiest and least cost-effective route to meet their mobility needs. For larger mobility networks such as train stations, the Citymapper offers step-by-step instructions to help users quickly access stations and provides the expected total travel time (NCMM 2018).

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4. TransitScreen App: This is a Washington, DC-based company that focuses on multimodality that intends to directly change riders’ behaviors in the direction of using transit modes. It provides information about the arrival times and nearby options (NCMM 2018). 5. Whim App: The app was developed in Finland and, currently, it can provide more services in which users can prepare and purchase all public or private modes of transportation they wish to use, such as trains, taxis, or buses, through monthly fee plans or direct payment on arrival. They are currently used in multiple cities in Europe and around the world (NCMM 2018, Kelly 2018). 6. Mobilleo App: This provides MaaS for users with business options such as hotel services. It can manage all business trips and business travel needs in a single, easy-to-use app. It can compare all available modes of transportation for any trip. Flights, hotels, car hire, trains, lounges, parking, chauffeur, and taxis are all bookable on this app (Kelly 2018). 7. Moovel App: This app was developed in Germany, but now it is used in multiple countries around the world. Similar to Whim, it has multimodal ticketing capability; for instance, the user can book and pay for a train ride on Deutsche Bahn (the Germany’s national train service) and then complete the last part of their trip with Car2Go (carshare) or NextBike (bikeshare) services (NCMM 2018). 8. WienMobil App: This is an Austrian-based app that provides users with the possibility of planning multimodal trips and selecting the easiest and the lowest costing route. The application integrates all modes of mobility, including walking (Kelly 2018).

4.2.5  Potential Research Areas The concept of MaaS is relatively new, and many public and private agencies, academic institutions, and organizations have made efforts to develop concepts, procedures, and tools to implement MaaS systems. Because this concept is relatively new, a lot of opportunities exist to develop more tools and programs so that the concept grows. Hence, research is still needed in this area of study. Shared mobility modes and public transportation options have great expectations because MaaS’ providers seek to achieve the necessary flexibility by attempting individuals to switch from an ownership-based system to an access-based system (Durand et al. 2018). Arguably, the integration between shared and private mobility modes and public transportation and mobility modes is relevant in MaaS, but research is still lacking in this area (Durand et al. 2018). However, fears continue about the reliability and the availability of shared mobility modes and public transportation options, and their impact on traffic congestion, in addition to the urban demandresponsive transportation that we do not know much about yet. More research on these topics is required (Durand et al. 2018).

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Tampère et  al. (2018) identified the three main research areas related to MaaS as (1) understanding the customers, (2) business models, and (3) policy implications. They provided an extensive list of potentially specific areas that could be studied further to get more insights into the aforementioned three main areas. According to Tampère et al. (2018), the following are some of the specific areas of MaaS that need to be highlighted to make it a potential subject for research:

4.2.5.1  Research Needs for Understanding Customers Examples of studies to understand customers include mobility patterns (e.g., trip chains, round-tours); heterogeneous user groups (e.g., private versus corporate, income groups); constraints and needs for mobility (e.g., handicaps, elderlies, baggage); and user as a service provider (e.g., P2P ride-sharing, private car sharing).

4.2.5.2  Research Needs for Business Models Studies about business models may include business plans for participating service providers and operators; revenue allocation for participating service providers and operators; a legal framework that will make MaaS service run smoothly and efficiently.

4.2.5.3  Research Needs for Policy Implications Studies on policy implications may include network design issues (e.g., service-level requirements, optimization of policy instruments) and regulation requirements (e.g., level playing field, open data policy, standards, harmonization, licenses).

4.3  MOBILITY ON DEMAND In recent decades, social and cultural trends have been rapidly and constantly changing, and technological advancements such as smartphones, large-scale electronic devices, and IoT have also experienced more rapid and accelerated growth. These rapid changes have also brought up some new innovative ideas on how to provide efficient and safe transportation services that can leverage emerging technologies. These opportunities can make transportation affordable and equitable, with improved mobility options available to all types of travelers (Dalton 2018). New mobility concepts and available solutions such as bike- and ride-sharing alternatives, including on-demand bus services, provide convenient and flexible ideas and options for an individual’s mobility needs (FTA 2019). Shaheen et al. (2017) define mobility on demand (MOD) as an innovative transportation concept where consumers can access mobility, goods, and services on demand by dispatching or using shared mobility, courier services, unmanned aerial vehicles (UAVs), and public transportation solutions.

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MOD for passenger modes can be enabled through shared modes, public transportation, and other emerging transportation solutions (e.g., aerial taxis). MOD for goods delivery can be provided through app-based and aerial delivery services (e.g., drones) (Shaheen et al. 2017). MOD is a new concept that has been developed to help communities around the world integrate their latest transportation options and alternatives, with the aim of bringing more practical and effective community improvements. All community categories (both urban and rural) can benefit from innovative and improved mobility services such as first-and last-mile services, paratransit improvements, and other public and private mobility solutions. According to Shaheen et al. (2017), “MOD refers to a network of safe, affordable, and reliable transportation options when, where, and how travelers want it.” New technologies and widespread use of smartphones are behind disruptive MOD systems such as Uber and Lyft, which have made safe and convenient individualized travel needs affordable (Basu et al. 2018). FTA developed the MOD Sandbox Demonstration Program, an initiative whose main objective is to provide an individual with personalized mobility that leverages automated, multimodal/integrated, and accessible transportation options (FTA 2019). According to FTA (2019), this program provides a venue through which integrated MOD concepts and solutions—supported through local partnerships – are demonstrated in real-world settings. FTA seeks to fund project teams to innovate, explore partnerships, develop new business models, integrate transit and MOD solutions, and investigate new, enabling technical capabilities such as integrated payment systems, decision support, and incentives for traveler choices. It is noteworthy that MOD and MaaS are related, as both emerging concepts are models developed to improve intermodal access to public and private mobility services (Cohen 2018); MaaS is gaining more attention in Europe, whereas MOD is gaining popularity in the United States. According to Shaheen et al. (2017) MaaS differs considerably from existing definitions of MOD in that MaaS emphasizes mobility aggregation, smartphone and app-based subscription access, and multimodal integration (infrastructure, information, and fare integration). But MOD encompasses a strong emphasis on both personal travel and goods delivery as it relates to commodified transportation services, as well as system management (i.e., supply and demand). Therefore, the concept of MOD is an innovative concept that focuses on the needs and requirements of users where the quality of goods and services provided to customers depends on their demands and desires. This can be achieved by using shared-transportation solutions or public transportation options through an integrated multimodal network.

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4.3.1  Importance of Mobility on Demand Services This subsection discusses the importance of MOD services based on mobility needs, travel behaviors, and existing transportation services.

4.3.1.1  Mobility Needs The US population is steadily growing but also becoming more aged and urbanized (DOT 2019). The constant migration of the rural population has caused this continual urban growth. At the same time, the proportion of the elderly population in the United States is growing rapidly, and statistical projections estimate that by 2045, there will be 84 million Americans over 65 years of age, which is almost twice the number of older Americans today (Mendez et al. 2017). A majority of the older people, like many other Americans, still drive their own cars and rely entirely on themselves to meet their needs, including their mobility needs, from one place to another. However, most often, these senior citizens may not be able to support themselves and may need constant help from others. For instance, about one-third of people aged over 65 have a disability that limits their mobility (Mendez et  al. 2017). The emergence of MOD has provided individuals with the opportunity to get rid of private car ownership by providing flexible and attractive transportation and mobility options that include multimodal integration of many personal and public transportation alternatives (Mobility on Demand Alliance 2019). Thus, the concept of MOD is a novel idea centered on offering transportation modes to individuals as a service available on-demand, but as a commodity in a society where the value of services is competitive in terms of cost, flexibility, travel time, delay and waiting time, number of connections, and other aspects. Recently, transportation on demand has enabled fast access to goods, services, and commercial centers through a multimodal network, using public transportation utilities, delivery options, or shared mobility solutions provided by some private companies (Mobility on Demand Alliance 2019). MOD depends on linking the transportation system with new mobility options through a multimodal network that provides individuals with the possibility of choosing different transportation alternatives in an easy, safe, reliable, and reasonable way. This, in turn, leads to an improvement in the quality and effectiveness of services provided to individuals, and, therefore, it facilitates the possibility of selecting the best MOD service that can combine different travel modes and services to support integrated mobility options (DOT 2019). The most advanced options offered to the user of MOD service are the possibility of booking and planning trips, in addition to selecting the time, duration, and cost of the desired trip, all these completed through a single-user interface (Shaheen 2018, Shaheen et al. 2018). MOD service suppliers have facilitated many transportation modes, including carsharing, scooter sharing, bike sharing, ride-sharing, transportation network company provider’s options, public transportation alternatives, and other different private and public transportation solutions (Shaheen 2018).

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In addition to what we have explained previously, the MOD service includes not only transportation options for users’ mobility but also goods and products, as it recognizes the importance of delivery services for goods. Cargo Network Services is an example of MOD applications facilitating the transportation of goods (Shaheen 2018). The objective of the MOD program can be summarized by saying that it provides the facilitation and development of a dynamic supply of transportation and mobility service options by integrating various advanced intelligent transportation systems technologies with the business innovative models that focus on providing special services smoothly and objectively, to cater to the mobility of individuals and goods (DOT 2019). MOD emphasizes that the movement or transportation of individuals is a commodity, which may have an economic value in terms of cost, duration of the trip, time of waiting and comfort level of passengers, and so on. An MOD service enables users to quickly access mobility options by using integrated mobility solutions and services through an integrated multimodal connected network (Shaheen et al. 2017, DOT 2018). In recent years, several applications that provide passenger transportation and courier delivery services have emerged to provide MOD services that have significant impacts on the improvement and development of a community’s transportation environment, economic, and social welfare owing to advancements in technology. Such applications include Uber, Lyft, ride-sharing app VIA, and so on (Shaheen et al. 2017). It is noteworthy that because MOD is built on the supply and demand concept, its emphasis is twofold: first, passenger mobility and freight/goods delivery; second, optimizing operations of the transportation network by applying the transportation systems management techniques (Shaheen and Cohen 2019). MOD has the potential to increase mobility options through flexible and convenient planning and execution of trips through multimodal networks to both passenger travel and freight delivery for areas previously inaccessible by a single mode. This is mainly possible because of its ability to provide first-and-last-mile connections to existing public and private transportation systems that are linked together through the MOD application app. Thus, unlike MaaS, MOD equally emphasizes demandresponsive freight and goods delivery.

4.3.1.2  Travel Behaviors Constant changes in social and cultural trends, as well as rapid technological advancements in wireless networks, cloud technologies, smartphones, and information technologies, have collectively contributed to developing a phenomenon called sharing economy (Shaheen et al. 2017, 2018). Sharing economy involves sharing, renting, or borrowing goods and services as opposed to the traditional way of owning them. According to Shaheen et al. (2017), this new phenomenon has already influenced several major economic sectors such as finance, goods, food, services, and transportation.

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The vision of MOD can also be explained to be developed around the concept of connected travelers, where consumers can access mobility and goods delivery services on-demand by dispatching or using public transportation, shared mobility, courier services, and other innovative and emerging technologies. According to Shaheen et al. (2017), this vision of MOD is to merge mobility and transportation systems management and operations, agencies, and private vendors, as well as all the users of the system contributing to demand. Connected travelers are an important piece of the MOD progression because the improved and advanced data network has enabled connectivity among travelers and has made it possible for people, vehicles, and transportation infrastructure to exchange data among one another. The millennials who have grown up in the computer and the internet age (in general, accepted as individuals born between 1981 and 1996) and commonly referred to as digital natives are becoming more actively involved in society and constitute a major chunk of the current workforce (Shaheen et al. 2017). According to Mendez et al. (2017), many of these millennials (young Americans aged between 15 and 34 and make up 83 million Americans) are choosing not to own cars and they are more likely to take transit. They are at the forefront of the sharing economy as they lead the changing social trends and attitudes about travel. Because they are technology and internet savvy, they prefer using technology to find new ways to travel, such as Uber and Zipcar, and they prefer to access their phones and use apps over access to a car (Mendez et al. 2017). As a result, these new social/cultural, mobility, and technological trends, led by young adult Americans, are the impetus behind the current changes in the way people travel, consume resources, and deliver goods. Consequently, MOD has evolved as an outcome or product of a sharing economy lifestyle. The modern mobility options and modes of public and private transportation systems, or bikesharing and ride-sharing alternatives, provide convenient and flexible ideas and solutions for individuals’ mobility in the sharing economy environment (Shaheen et al. 2017). The MOD as a transportation concept is based on the needs and requirements of individual users where the quality of goods and services provided to customers depend on their demands and needs. As a result, this can be efficiently achieved by using shared-transportation solutions or public transportation options through an integrated multimodal network. Therefore, in summary, we can say that rapid changes in technology, mobility, and social trends are also rapidly changing the way people travel and consume resources, as a result disrupting both the demand and the supply sides of transportation services, hence potentially supporting MOD growth. Consequently, on the supply side, this provides more choices for both passengers and goods delivery (Shaheen et al. 2017).

4.3.1.3  Existing Transportation Services The growth in MOD and its accompanying changes in travel behavior are impacting the transportation network. The DOT vision of the MOD ecosystem is

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in the form of a supply and demand framework (Shaheen et al. 2017). The DOT vision of the MOD ecosystem is shown in Figure 4-7, in which an integrated multimodal transportation operations management will play a major role under the MOD concept as it interacts and impacts the supply and demand sides and the key enablers of MOD (Shaheen et al. 2017). As noted previously, the population’s demographic makeups (e.g., the proportion of special groups such as older citizens, millennials, or people with disabilities) affect the demand as they may make different mobility choices that are different from those of the rest of the population. Furthermore, residential areas where an individual lives may influence the level and usage of MOD systems. For instance, densely populated urban neighborhoods, which traditionally favor public transportation, are likely to be more welcoming to MOD concepts because they make available more mobility and delivery choices to travelers than in suburban and rural areas. The components of the supply and demand sides, which greatly contribute to the emergence of MOD, as shown in Figure 4-6, are discussed in detail as follows (Shaheen et al. 2017). 4.3.1.3.1  Supply Side of the Mobility on Demand Ecosystem The supply side of the MOD ecosystem involves all players, operators, and devices that provide transportation services for passengers or goods delivery, including the following: • Public transportation services: These include public transit (e.g., trains, buses, ferries, paratransit). • Nonpublic transportation services: These include taxis, car rentals, microtransit (e.g., Chariot, Via); ride-sourcing (e.g., Lyft, Uber, Curb), personal vehicles, volunteer drivers, other shared services (e.g., e-Hail, carsharing, ride-sharing, bike sharing, scooter sharing). • Goods delivery services: These include freights, logistics, first-and-lastmile goods delivery, courier network services (CNS), UAVs, and robotic delivery. • Transportation facilities: These include parking lots/garages, toll plazas, roadways, and highways. • Vehicles: All vehicle types that are involved, including transit vehicles, private vehicles, and goods delivery vehicles. • Transportation management and information systems: This supply category includes payment systems for parking, toll and public transit, signal systems, mobile apps (for trip planning and payment), fleet management systems, navigation systems (e.g., GPS), and so on. • Transportation information services: These include trip schedule information, 511, and dynamic message signs; information services provided by private vendors such as Waze and Google Maps.

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Federal Government

State & Local Authorities

Public Transit Agencies

Transportation Operations & Logistics Providers

Apps and Mobile Service Providers

Transportation Managers

Public Transportation Services Transportation Mgm/ Information Systems

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Travelers Non-Public Transportation Services

User Needs & Preferences

MOD Marketplace Supply Side

Transportation Facilities & Vehicles

Operational Objectives Operational Response/ Feedback Control

Business Models & Partnerships Strategic Partnerships Financing Incentives

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Time of Ride or Delivery Request OriginDestination Request

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Equity Considerations Safety Considerations Mobility Issues Standardization

Wireless Network GPS/Sensors Big Data and Predictive Analytics

Figure 4-6.  MOD ecosystem. Source: Shaheen et al. (2017).

4.3.1.3.2  Demand Side of the Mobility on Demand Ecosystem The demand side of the MOD ecosystem is made up of all MOD system users (i.e., travelers and couriers), including their choices and preferences, which, in turn, affect the supply side as well. Examples include the following: • Travelers: These include all people who desire to travel, including pedestrians, riders, drivers, cyclists, and so on. • Goods: Parcels and cargo that require to be physically delivered and those goods that require digital delivery.

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• Time of ride and/or delivery request: This depends on the requested particular service and its availability, and, thus, it also affects the choice of the mode of travel to use. • Origin–destination request: This determines the location of the demand and the location of the desired destination and, thus, influences the mode choice and route selection. • Modal demand: This depends on occupancy, size, or type of vehicle requested. • User needs and preferences: These include the choice of the mode to take and how to make the trip (e.g., choosing whether to drive alone, carpool, use public transportation, or some other form of shared-use modes).

4.3.2 Implementation Features of Different Mobility on Demand Business Models for Passenger and Goods Movement The premise of an MOD service is to access real-time data and trip information and perform predictive analyses to provide individual travelers with a range of mobility options that can serve their needs and desires and, hence, can provide better mobility alternative solutions for all (FTA 2019). MOD programs integrate and develop many different transportation modes and make them available to all members of the community. This process of integration creates a central authority that coordinates and plans various transportation and mobility solutions for individuals. The need for developing business models for MOD systems is constantly growing, as it has a significant role in achieving various needs of users, service providers, and partners. Therefore, it may generate more benefits to partner organizations (both public and private) by reducing the demand for parking spaces and minimizing the environmental impacts by improving air quality. In addition, it has a greater role in shifting traditional travel behavior in a way that helps in reducing traffic congestion (Shaheen et al. 2017). According to Shaheen et al. (2017), MOD business models can be grouped into four categories based on the MOD service consumer and provider types. These categories include the following: business-to-consumer (B2C), business-to-business (B2B), peer-to-peer (P2P), and business to government (P2G). These business models are further explained and defined with some examples, based on Shaheen et al. (2017), as follows.

4.3.2.1 Business-to-Consumer This business model provides individual consumers with access to businessowned operated transportation services such as a fleet of vehicles, bicycles, scooters, or other modes through memberships, subscriptions, user fees, or a combination of pricing models. Examples of B2C business models include the following: Zipcar car sharing, Motivate bike sharing, FedEx, and UPS delivery (Shaheen et al. 2017).

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4.3.2.2 Business-to-Government This business model provides transportation services to a public agency where certain payment arrangements can be agreed upon, for example, fee-for-service agreement and per-transaction basis contract. Examples of the B2G business model include Government Services Administration carsharing pilot program with Enterprise CarShare and Mobility, Zipcar (Shaheen et al. 2017; GSA 2019). According to GSA (2019), carsharing is a program that assists federal agencies in reducing costs, improving efficiencies, and optimizing vehicle uses. Additional benefits of carsharing provide agencies with flexibility and access to vehicles on a short-term basis such as hourly, up to a maximum of 1 day vehicle usage and consequently saving money by eliminating the need to purchase new vehicles, which provides a more sustainable approach to government fleet management (GSA 2019).

4.3.2.3  Business to Business The business-to-business (B2B) model provides business customers with access to transportation services either through fee-for-service or usage fee agreements. Usually, these services are offered to business company employees on work-related trips. Examples of this model include corporate and business travel accounts for carsharing and ride-sourcing (e.g., Enterprise CarShare, Zipcar, Lyft, Uber), bike sharing for corporate campuses, FedEx, and UPS delivery (Shaheen et al. 2017).

4.3.2.4  Peer-to-Peer Mobility Marketplace This business model strives to maintain a marketplace, typically involving an online platform that connects individual consumers (buyers) and mobility service providers (sellers) in exchange for a transaction fee. Examples of this business model include Bitlock and Spinlister (bike sharing), Getaround and Turo (carsharing), and Scoop (ride-sharing) (Shaheen et al. 2017).

4.3.2.5  Peer-to-Peer Delivery Marketplace This business model involves peer-to-peer goods delivery services such as CNS, apps that provide for-hire delivery services using an online application or platform (e.g., a website or smartphone app) to connect couriers using their personal vehicles, bicycles, or scooters with goods (e.g., packages, food). These apps are of two types: (1) P2P delivery services are apps that enable private drivers to collect a fee for delivering cargo using their private automobiles (e.g., Roadie); and (2) paired on-demand courier services are apps that allow for-hire ride services to also deliver packages (e.g., UberEATS) (Shaheen et al. 2017). It is suggested that the government’s support for cooperation and opportunity of partnerships between public and private sectors should improve transportation options provided. As a result, this PPP may support a multimodal transportation network by developing various intelligent MOD business models, which can enhance accessibility, livability, and improve the quality of life (Shaheen et al. 2017).

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Within this scope, DOT encourages public and private agencies to form local and international partnerships to make their efforts side by side to upgrade/ improve transportation infrastructure networks that currently exist to make them suitable for modern mobility solutions and options and raise the quality of services they provide based on the desires and needs of consumers (FTA 2019). MOD service suppliers always ensure quality and an effective level of services and try to improve factors of safety and satisfaction for their users/customers or meet any necessary needs that may occur as part of competitive business.

4.3.3  Technologies Enabling Mobility on Demand Services Several recently emerged technological advancements play a major part in enabling MOD services that are used to solve many transportation problems in innovative ways. Advanced technologies that contribute to the growth of MOD include internet-based platforms, ICT, location-based services, big data, CAVs, and smart infrastructure. These technologies contribute to providing new opportunities for individual and public mobility. A brief list of some of the technology examples that advance MOD is provided as follows (Shaheen et al. 2017): • Information and communications technology (ICT). ICTs play an essential role in the mobility-on-demand system by creating a more connected transportation network. Correspondingly, by using a comprehensive ICT concept, we can ensure a transportation vision that can provide transportation service options for all, that is, can offer mobility opportunities for all individuals, including people with disabilities. • Smart infrastructure. Modern smart technologies that provide MOD services depend mainly on the effectiveness and efficiency of the infrastructure, as the speed of the system depends on a well-designed and smart infrastructure. This will become handier, especially as the penetration market level of CAVs increases. • Connected vehicles (CVs). The principle of CVs depends on receiving data from surrounding objects. As a result, a lot of data will be produced, which can be used to contribute to the efficiency and effectiveness of MOD services. The concept of CVs, which is primarily a traveler-centric approach, provides a more advanced view of the MOD system by providing better, smoother, more efficient, and connected mobility options. • Location-based technologies. These are essential for the transportation networks sector in general and for MOD services. To meet the multiple mobility needs of individuals, researchers are constantly developing more accurate positioning location techniques with lower costs. • Mobile devices and apps. The evolution of ITS and the emergence of global positioning systems (GPS) coupled with rapid growth in wireless technologies and widespread availability of personal mobile smartphones in addition to a large amount of data-sharing between individuals, are the driving forces behind smart mobility solutions. All these have led individuals to use

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transportation applications on their smartphones to meet the needs of their mobility and take advantage of MOD options that keep emerging from time to time.

4.3.4 Contribution of Mobility on Demand in Shared Mobility Ecosystem Shared mobility, that is, shared use of a vehicle, bicycle, and so on, is one of the major categories of MOD. Shared mobility provides users with short-term access to transportation services. Types of shared mobility for passenger mode include carsharing, bike sharing, ride-sharing (e.g., carpooling and vanpooling), and other on-demand ride services. Alternative transit services, for example, paratransit, shuttles, and microtransit, are also forms of shared mobility. In addition, shared mobility provides users with goods delivery services that help connect couriers with goods, for example, CNS. Shared mobility is having a transformative impact on many major cities around the world today by providing MOD services for peoples’ mobility and goods delivery options (Shaheen et al. 2017). Figure 4-7 shows the different modes that form the shared mobility ecosystem. MOD as an innovative transportation concept is making it easier for customers to access various shared mobility and goods delivery services. MOD provides services on demand by using shared mobility, delivery services, and public transportation solutions through an integrated and connected multimodal network (Shaheen 2018). MOD for passenger services combines trip planning, booking, and trip ticket payment through a single-user interface with the ability to access the entire shared mobility ecosystem (refer to Figure 4-3), including carsharing, bike sharing, ride-sharing, ride-sourcing/TNCs, scooter sharing, microtransit, shuttle services, public transportation, and other emerging

Figure 4-7.  Shared mobility ecosystem. Source: Shaheen et al. (2017).

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transportation solutions. For delivery goods, MOD facilitates courier services by offering advanced and innovative solutions such as robotic delivery, appbased courier network services (CNS), and aerial delivery services (e.g., drones). According to Shaheen (2018), “fundamentally, MOD is about how people make mobility decisions, how they move, how they consume goods and services, and the stakeholders that make it possible.” Thus, in recent years, MOD has immensely contributed in an innovative way in improving services provided by shared mobility providers across its spectrum of available options. Because MOD is about providing travelers with more unified travel options through the integration of on-demand services, public transportation, and easier payment methods, it is revolutionizing shared mobility services. MOD encourages public transit use with first-mile and last-mile connections with the ability to provide travelers with door-to-door services by combining several shared mobility modes across its ecosystem in a seamless manner. In other words, MOD has given customers a greater level of transportation accessibility and customization of the utilization of shared mobility services than ever before.

4.3.5  Future Research Direction Figure 4-8 shows the proposed research focus areas suggested by DOT expected to transform the way the society moves and benefits individuals, transportation operators, and mobility providers as well. MOD concept provides new and

Figure 4-8.  Future research focus areas. Source: DOT (2019).

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integrated payment systems and solutions for mobility options that make trip arrangements and traveling for individuals from one place to another easier and quicker. The concept seeks an operational integration of transportation products and services with existing mobility services. MOD will continue to evolve because its key pillars or enablers, such as technological advances, demographic and social trends, shared mobility ecosystem, and so on, are continuously changing as well. As new technologies come on board, they create more opportunities of incorporating them into existing MOD applications. The recent and ongoing advent of automated and connected vehicle technologies brings further opportunities by leveraging these new technologies in newer mobility services. DOT (2019) identified six interrelated focus areas for future MOD research activities that will generate the knowledge expected to make mobility within the multimodal ecosystem smarter, more efficient, and safer.

4.4 SUMMARY This chapter gives an introduction of methods and technologies used in automated delivery and logistics (e.g., warehouse management, fleet management, reverse logistics), as well as associated policy considerations. The trends of automation are also leading to the use of unmanned delivery systems such as air delivery drones, robots, and self-driving trucks in the near future. Because mass automation in the delivery and logistics sector has been immensely influenced by the growing shortage of labor, increasing demand, technology advancement, and customer expectations, emerging automation of delivery and logistics holds promise to some or a great extent. This chapter comprehensively introduces a new constitution of the transportation options—MaaS that builds on the concept of providing a wide range of integrated transportation and mobility service that is accessible on demand. MaaS intends to shift the mindsets of individuals from using privately owned vehicles as their main mode of transportation toward other on-demand types of mobility services. This means that instead of focusing on purchasing and owning cars, individuals can directly buy a mobility service as they need. The MaaS-based integrated mobility service is effective and efficient for users and travelers through a single virtual service layer or user interface that can be accessible via the user’s smartphone to pay once and use all these modes in one trip. Accordingly, the MaaS is supposed to ensure fast and flexible access to various travel and transportation services in a variety of options for the end users directly. MaaS is closely tied with the concept of mobility on demand, or MOD, which is an innovative emerging transportation concept wherein individuals can access mobility and goods services on demand by dispatching or using shared mobility, courier services, and public transportation solutions to make their journeys

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more efficient. MOD for passenger modes can be enabled through shared modes, public transportation, and other emerging transportation solutions. MOD for goods delivery can be provided through app-based and aerial delivery services (e.g., drones). All community categories (both urban and rural) can benefit from innovative and improved mobility services such as first-and-last-mile services, para-transit improvements, and other public and private mobility solutions. New technologies and widespread use of smartphones are behind the emergency of MOD systems such as Uber and Lyft, which have made safe and convenient individualized travels affordable. This chapter delivers a comprehensive overview of the MOD-related implementation features (including B2C, B2G, B2B, P2P mobility and delivery marketplace), technologies, and factors contributing to MOD in the shared mobility ecosystem. Last but not least, future research needs for all three sections are discussed at the end of each section, respectively.

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CHAPTER 5

Shared Sustainable Mobility Kakan Dey, Deogratias Eustace, Na Chen, Ting Zuo, Heng Wei, Md Tawhidur Rahman

5.1  SHARED VEHICLE SERVICES 5.1.1 Background The shared vehicle service models follow the concept of a sharing economy. The sharing economy is based on the idea of renting and borrowing goods and services instead of owning them. Shared vehicle services satisfy the mobility needs of people by sharing vehicles owned by individuals or businesses. The benefits of shared vehicle services include travel cost savings, increased efficiency of available resources, and social and environmental benefits (Shaheen et al. 2015). The Great Recession of 2007 to 2009 forced people to rethink the need for owning vehicles to meet mobility demand. Moreover, with a steady increase in traffic demand, transportation planners and operators continuously explore innovative and effective strategies for transportation demand and supply management by encouraging shared vehicle services. Shared vehicle services can reduce traffic demand by increasing vehicle occupancy rates. Mobility options have increased greatly in the last few years, as different shared vehicle services were introduced in many cities around the world. Innovation in technologies such as location-based services, social networking sites, and the internet and mobile technologies has provided the required communication and computing infrastructure for shared vehicle services. These technological innovations enable real-time on-demand shared vehicle services to mass populations, which are especially attractive to tech-savvy generations such as Millennials (Waszkowski 2019). Currently, China and the United States are the two largest shared vehicle service markets in the world, where ride-hailing services constitute 80% of the market shares. In contrast, carsharing is the most dominant form of shared vehicle services in Europe (Grosse-Ophoff et al. 2017).

5.1.2  Shared Vehicle Services and Transformed Mobility Patterns Carsharing, carpooling, ride-sharing, and ride-hailing are the few examples of the shared vehicle services currently available in many areas. Although many 185

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common features among different shared vehicle services can be found, it is important to understand the distinctions among different shared vehicle services business models to develop comprehensive transportation policies (Shaheen et al. 2018). In the following two subsections, two well-established shared vehicle services (i.e., ride-sharing and carsharing) are discussed.

5.1.2.1  Ride-Sharing Service Models Ride-sharing is the sharing of a vehicle with others with common origin and destination (O–D) or with little deviation in the origin and destination. Ridesharing is also known as carpool or vanpool, depending on the vehicle type. Figure 5-1 illustrates the classification of different types of ride-sharing services. A massive growth in ride-sharing services is expected with new operators, technological innovations, and advanced policy incentives in the future. Future ride-sharing services can include package or food delivery services to create new opportunities for ride-sharing operators to generate a large customer base and increase profit (Aron 2018). Three classes of ride-sharing service models have been discussed by Chan and Shaheen (2012): (1) Acquaintance-based ride-sharing, known as fampools or coworker carpools, are arranged among family members, friends, or coworkers; (2) Organization-based ride-sharing is arranged by an organization. Ride-sharing participants need to be members of this ride-sharing organization, or visit the organization’s website, or use smartphone applications of that organization for sharing rides; (3) Ad hoc ride-sharing is known as casual carpooling or slugging or anonymous ride-sharing in which acquaintance among participants or an organizational link is not required. People usually commute with strangers by choosing pickup and drop-off at convenient locations. Motorists driving alone can choose this form of ride-sharing to qualify for using HOV lanes. Acquaintance and ad-hoc ride-sharing are self-organized, and organization-based ride-sharing is arranged through a notice board or a computerized ride-matching system.

5.1.2.2  Ride-Sharing Policy Considerations The ride-sharing potential of a community can be promoted with proper policy incentives. Amey et  al. (2011) modeled the ride-share potential of the MIT community and discovered a potential of 50% to 77% among the commuting population, compared with only 8% of the commuting population using ridesharing because of the absence of adequate policy incentives. Several policy considerations for ride-sharing are discussed in the following subsections. 5.1.2.2.1 Pricing Service pricing policy plays a major role in encouraging travelers in ride-sharing. A simple way to distribute travel costs is to evenly split it among the riding passengers (Ma et al. 2013, Wang et al. 2018), which is most suitable with the same pickup and drop-off points for all passengers. However, the same pickup and drop-off points sometimes reduce flexibility in ride-sharing. Riders have to walk or drive personal vehicles or use other forms of transportation for first-mile

Source: Chan and Shaheen (2012).

Figure 5-1.  Ride-sharing service models.

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and last-mile transportation. The availability of parking infrastructure at pickup and drop-off locations will increase the implementation cost. A distance or timebased ride-sharing pricing scheme is suitable for door-to-door ride-sharing. Current ride-sharing pricing methods often ignore the costs associated with the delay incurred during passenger pickups and drop-offs Gopalakrishnan et al. 2016) and congestion delay. Also, detour distances or additional times for new passenger pickups and drop-offs are not often reflected in the per-passenger fare. Shared vehicle service providers can implement an appropriate travel costsharing scheme that will be attractive for all participants considering all direct or indirect costs incurred by different users. Besides, it is important to attract a sufficient pool of drivers for ride-sharing. Ma et al. (2013) recommended that the trip fare per mile should be higher for multiple passengers’ trips than for single passenger trips to attract drivers to compensate for a higher number of pickups and drop-offs in multiple passengers’ trips. Drivers need to pay huge percentages (approximately 20%) of income from each ride to service providers in most ride-sharing services (e.g., Uber and Lyft) (Ridester 2019). To challenge this business model, “Pull Up n Go,” a new Nevada-based ride-sharing service company, charges drivers a low weekly flat fee instead of a certain percentage per ride, which has encouraged drivers to take to the ride-sharing business (Pull Up n Go Technologies 2017). This pricing model can also lead to a lower cost for riders, as comparatively low fees are charged by the service providers. Faruhata et al. (2013) identified three types of pricing rules in the ride-sharing industry. In the catalog pricing scheme, drivers and passengers mention their preferred prices and service providers determine a particular trip price considering the preferred prices. In rule-based pricing, the trip price is determined by a cost calculation formula adopted by the service providers. In negotiation-based pricing, drivers and passengers bargain the trip cost considering the trip distance. Although catalog pricing and negotiation-based pricing allow ride-sharing users to participate in trip cost determination, rule-based pricing has the potential to support dynamic matching efficiently. Because of the consideration of several requirements from ride-sharing participants, catalog and negotiation-based pricings are more complicated and time-consuming (Gopalakrishnan et al. 2016). Kleiner et  al. (2011) have proposed the idea of the auction mechanism (negotiation-based pricing), in which the least and highest preferred price is declared by drivers and riders, respectively. To reduce the conflict of interest between prices asked by drivers and passengers, service providers are required to enforce a limit within which both drivers and riders can choose a price. 5.1.2.2.2  Compensation Mechanism An attractive reward mechanism encourages drivers and riders to participate in ride-sharing (Faruhata et  al. 2013). Local governments and ride-sharing organizations can implement reward-based ride-share incentives to encourage ride-share participants (Agatz et al. 2011). Avego, currently known as Carma, was initially rewarding $30 to drivers and riders for picking up 20 riders and taking 20 rides per month, respectively. iCarpool and Carpool World encouraged users to ride-sharing with mile-based rewards (i.e., sharing a ride for a fixed number

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of miles to become eligible for monetary reward) (Amey et al. 2011). Rewards can also be allocated in terms of different trip performance measures. For example, rewards can be provided to passengers for pickup or drop-off delays, excess detour distances, or detour times in the form of compensation. Sometimes instead of providing compensation, trip performance measures can be improved through transferring passengers between ride-sharing vehicles (multihop ride-sharing). Although transferring riders to other vehicles is a good strategy for system-wide travel delay reduction, it may create inconveniences for both drivers and riders. As opposed to door-to-door ride-sharing, the meeting point concept can minimize the detour distance of drivers. Park and ride infrastructures are important to facilitate the meeting point concept, which allows users to park their vehicles and ride-share from a meeting point. Optimized locations of meeting points can reduce the overall users’ waiting time, system-wide VMT, and detour. 5.1.2.2.3  Safety Considerations Safety is one of the most important concerns in ride-sharing. Sharing a ride with an unknown person(s) usually raises personal safety concerns. Uber app has the option to get the real-time location map and share the estimated time of arrival with friends and family to address this concern. If a rider is matched with a driver, they can access certain information about each other (i.e., name, photo, driver’s license plate number, and rating) before confirming the request. It is even possible for a rider to contact a driver after the journey (e.g., in case riders left behind something valuable in the vehicle) (Uber 2019). Moreover, service operators can install sensors or video cameras in ride-sharing vehicles by maintaining sufficient privacy strategies to reduce security concerns. 5.1.2.2.4  Other Policy Considerations Organization-level incentives can be used to promote ride-share. Ride-sharing models are well suited for work-related trips. As most work-related trips take place during peak periods, travel time and cost savings by avoiding personal cars are great incentives for people with similar O–D to participate in ride-sharing. The size of the organizations plays an important role in the development of an institutional ride-share program. Large organizations can facilitate more participants to ride-share because of a large number of employees available with them, more willingness to ride-share with coworkers, and more common destinations, which are favorable for ride-matching (Amey et al. 2011, Agatz et al. 2011). Estimating and disseminating the benefits of ride-sharing to participants and the society can increase ride-sharing. Personal car users sometimes ignore the external cost of using this travel mode (e.g., cost of pollution). To demonstrate the benefits of ride-sharing appropriately, associated direct and indirect costs such as fuel cost, delay cost, marginal cost of adding a passenger, and cost related to diversion of ideal route for pickups and drop-offs and direct and indirect benefits such as congestion and VMT reduction, and pollution reduction should be included in the net-benefit estimation. The estimated costs and benefits may vary with the preferences of users. Drivers may detour more to pick up passengers if their value of time is low but may not deviate much between their own O–D if their value of time is high. Moreover, the value of time can be used to favor

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ride-sharing by deploying innovative infrastructures and traffic management decisions such as introducing high-occupancy vehicle (HOV) lanes and enforcing peak period ride-sharing. Yet, there could be an equity concern as people who are unable to form groups for sharing rides cannot avail of these innovative features. Several studies have recommended modifications in vehicle interior designs as activity-based vehicle design features (e.g., maximize storage options and foldable seats for shoppers; user-friendly child booster seats for families; adjustable swivel seats; Wi-Fi and power outlets; sound isolation and independent lighting for commuters) can promote ride-sharing (Hensley et al. 2017).

5.1.2.3  Carsharing Service Models Carsharing can be defined as a short-term rental car service. Members of carsharing need to confirm reservations to access a vehicle. Carsharing users often use public transit (PT), bicycle, and walking in addition to sharing car, which reduces systemwide VMT and emissions (Shaheen et  al. 2010). In the United States, carsharing services started in 1988 (Shaheen et al. 2015). Western Europe and the United States are the leaders in the carsharing industry with the highest market penetration. Around 10 million people have used carsharing services around the world by 2017 and expected to reach 36 million with an annual growth rate of 16.4%. The value of the global carsharing market is expected to reach $11 billion in 2024 (Waszkowski 2019). Vehicles in the carsharing service model can be accessed in convenient locations such as in neighborhoods, college campuses, shopping malls, and transit stations (Shaheen and Meyn 2002). Carsharing operators own or lease vehicles and distribute them throughout their respective operational areas. Carsharing services are usually charged for total mileage or total time or both. Primary carsharing models required members to return vehicles to locations, where the vehicles were picked up. In contrast, nextgeneration carsharing operational models implement one-way carsharing, which provides more flexibility to users. Members in a carsharing service usually access vehicles in two ways: direct key transfer between owner/operator and the user(s) and unattended access by using key box or smartcard. Business-owned carsharing service models (e.g., Zipcar) have to invest high capital to own required vehicles for their services (Shaheen et al. 2012). Including personal vehicles in carsharing models is an alternative to reduce high investment need in implementing carsharing services. Personal vehicle sharing is recognized as peer-to-peer (P2P) carsharing. The first personal vehicle sharing model was implemented in Boulder, Colorado, by eGO Car share organization. Over the years, the members of P2P carsharing increased rapidly. An increase of 111% in P2P carsharing membership was observed in North America between January 2016 and January 2017. Vehicles in P2P carsharing are temporarily available for members of a P2P carsharing network (Shaheen et al. 2018). Researchers identified four personal vehicle sharing models: 1. Fractional ownership: In this model, a personal vehicle is purchased and operated by carsharing operators at a reduced price.

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2. P2P carsharing: In this model, only personal vehicles are shared by individuals or through P2P car share service providers. If car share service providers are involved in arranging P2P carsharing, they keep a portion of the profit. 3. P2P marketplace: This model directly enables sharing personal vehicles through the internet (Shaheen et al. 2012). 4. Hybrid P2P traditional carsharing model: In this model, carsharing operators provide P2P car share service in addition to managing own fleet. In exchange for arranging P2P carsharing, car share service providers keep a portion of the profit. In P2P carsharing, usually private vehicle owners keep the ownership of the vehicle. In some service models, the title of the vehicles is indefinitely transferred to commercial vehicle operators. In exchange, private vehicle owners receive access to the carsharing service at a reduced cost. It is expected that more carsharing operators will provide more mobility options in the future. Carsharing service with ride-splitting features can provide more affordable trips to users. Ride-splitting features can generate a strong sense of community and engagement among the members of carsharing organizations (Medium 2018).

5.1.2.4  Carsharing Policy Considerations The effectiveness of a carsharing business model depends on the consensus of the stakeholders (e.g., carsharing service providers, government agencies, users). Considering the diverse impacts of carsharing service models, stakeholders select which model of carsharing service should be implemented. The city of San Francisco preferred station-based carsharing service models over freefloating carsharing service models. The city authorities identified that freefloating carsharing service models could reduce PT ridership and bike riders (Sprei 2018). For the successful operations of a carsharing service, a sufficient number of memberships are critical. The profitability of a carsharing service can be maintained if there is an adequate volume of carsharing members who live within a certain distance from a carsharing point. Adequate policy incentives (discussed subsequently) need to be provided to ensure an adequate volume of carsharing population.

5.1.2.5  Parking Regulations Parking plays an important role in the large-scale adoption of carsharing services. Inadequate parking spaces for carsharing services create user inconveniences. The level of government support for carsharing affects the rules and regulations in parking. Shaheen et al. (2010) identified the following elements associated with carsharing parking policy development: 1. Parking allocation: Under the maximum government support scenario, parking spaces are allocated through an informal process or case-by-case basis depending on the request of carsharing operators. In contrast, a highly

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formalized process is followed, and sometimes limited parking spaces are divided among carsharing operators in a minimum government support scenario. Typically, in-between of these two scenarios is adopted in the allocation of carsharing parking. 2. Parking caps: Parking caps limit the parking spaces for carsharing services for both on-street and off-street parking. Parking caps can be allocated by operator, location, or number of service members. Usually, there is no parking limit by number or by percentage under a maximum government support scenario. However, a cap on parking spaces is imposed under minimum government support. 3. Fees and permits: Fees of the allocated carsharing parking spaces depends on a combination of the following factors: (a) permit cost of residential parking if carsharing operators want to use a residential parking lot; (b) meter charge collected from personal vehicles; (c) operation, maintenance, overhead, and administrative costs; and (d) market value of off-street parking space in a jurisdiction. Carsharing operators are exempted from parking fees or required to pay a fee significantly below the market price in the case of maximum government support, whereas a cost-recovery or profit-based method is followed in the case of minimum government support. The city of Portland introduced an auction process to allocate carsharing parking from January 2013. Every year available on-street metered parking spaces are auctioned and assigned to carsharing operators. The minimum bid is determined based on the lost meter revenue in addition to the installation, administrative, and maintenance costs associated with the parking spaces selected for carsharing use (Shaheen et al. 2015). 4. Signage installation and maintenance: Special signage and markings are used for carsharing parking spaces or lots. Carsharing operators are required to pay for production, installation, and maintenance costs in a minimum government support scenario, whereas the local government covers these costs in a maximum government support scenario. 5. Parking enforcement: To ensure the proper utilization of carsharing parking spaces and avoid noncarsharing vehicles (e.g., personal vehicles) using carsharing only parking spaces within the designated period, the enforcement of parking rules is important. Under maximum government support, the penalty for parking in carsharing parking spaces by noncarsharing vehicles is higher than regular violation charges. The importance of a carsharing program from the government perspective has a profound impact on the success of that program in a jurisdiction. Adequate government support can reduce the operational cost of carsharing services and help the expansion of carsharing services.

5.1.2.6  Insurance and Taxes As multiple stakeholders are involved in carsharing services, allocating the liability of carsharing vehicles involved in crashes is a complex exercise. Liability

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issues are more complex in peer-to-peer carsharing services as a carsharing user rents a vehicle that is owned by a different person. Compared with traditional car rental services, any incident with a P2P vehicle is not covered under a personal insurance policy (Megna 2018). Vehicle owners’ licenses might be revoked for renting vehicles to others (Glenn 2016). Oregon state passed a law in 2011, which allows people to share their cars without jeopardizing their own car insurance (Profita 2015). However, the laws did not clear the liability issue. An appropriate liability distribution model should be adopted so that drivers, consumers, and carsharing operators can share risk appropriately. Moreover, taxes (e.g., sales tax, rental-car specific tax, excise tax, and transaction charges) in the operation of carsharing should be selected considering the overall benefits of carsharing in the transportation system and the environment.

5.1.2.7  Other Shared Vehicle Services In addition to the two major shared vehicle services (i.e., ride-sharing and carsharing) discussed in the previous sections, ride-hailing and ride-splitting services have become popular in the last few years. Ride-hailing services are operated by Transportation Network Companies (TNCs) (e.g., Uber and Lyft) with drivers using their personal vehicles. In typical ride-sharing services, drivers and passengers have similar origins and destinations. Ride-hailing services provide services similar to that of a taxi service. A ride-hailing service becomes a ridesplitting one (e.g., Uber Pool and Lyft Line) when passengers from two or more requests share a ride and share the trip costs. Uber and Lyft are the two leading ride-hailing services in the United States (Mazareanu 2019a). Approximately 110 million users monthly use Uber worldwide (Mazareanu 2019b). Several studies reported that ride-hailing services have been replacing taxi and bus services. A study on the users of ride-hailing in San Francisco Bay Area revealed that 39% of taxi trips and 24% of bus trips were replaced by ride-hailing trips (Shaheen et  al. 2015). Ride-hailing trips were found to be shorter than taxi trips. With the mass adoption of ride-hailing services, insurance policies associated with the operation of personal vehicles for ride-hailing become a concern. The California Public Utilities Commission identified three coverage periods for ride-hailing vehicles: (1) when a driver is signed-in to a ride-sourcing app and become available to drive, (2) when a driver is en route for passenger pickup after accepting a ride request, and (3) when a driver is providing a trip. It was proposed that ridehailing operators should provide contingent liability coverage in the first stage if drivers’ personal insurance does not support liability coverage. For the second and third stages, ride-hailing operators should provide commercial liability coverage, uninsured/underinsured motorist coverage, and contingent collision and comprehensive coverage, whereas no insurance should be provided by the operators when the app is off (Shaheen et al. 2015). Microtransit usually offers a fixed route, and a fixed scheduling or a flexible route, and on-demand shared vehicle services using high-occupancy vans. Compared with ride-sharing services, drivers are paid by service operators in microtransit. As the decline in ridership is a widespread concern among many PT agencies, small-capacity microtransit can provide on-demand transportation

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service and cover a wider service area with low operational cost. The Austinbased transit service, the Capital Metro, launched an on-demand microtransit service on June 3, 2019, by replacing MetroBus Route 470. Passengers request a ride using the app or by calling the service center. Some other examples of microtransit services in the United States are EmGo (Eugene, Oregon), Rapid On-Demand (Grand Rapids, Michigan), COTA Plus (Columbus, Ohio), and Tri MyRide (Antioch, California) (APTA 2019).

5.1.3  Use of Technology in Shared Vehicle Services The role of technology in improving shared vehicle services is enormous. The primitive form of shared vehicle services has reached the current stage mainly because of innovation in technologies [e.g., global positioning system (GPS), sensor technologies, wireless communication, and cloud computing technologies]. Technological innovation has transformed traditional shared vehicle services to real-time on-demand mobility services. On-demand realtime shared vehicle services require complex decision-making tasks such as driver and rider matching, vehicle routing, scheduling, and fare collection. Advanced algorithms enable efficiency by automatic matching of riders with available drivers and services to accommodate on-demand real-time shared vehicle services. Algorithm-supported smartphone applications assist travelers to easily access shared modes, plan itineraries, and pay for the services. Using real-time information (RTI), advanced algorithms integrate travel planning and fare payment of multimodal mobility options including shared vehicle services in a single smartphone application (Shaheen et al. 2018). Researchers are developing algorithms that can predict future shared trip demand, which will allow shared vehicle operators to dispatch vehicles before ride requests and reduce the waiting time of travelers. Advance vehicle dispatching can reduce the idle time of drivers and fuel usage and increase profit (Mourad et al. 2019). Data security is another concern in shared vehicle service operations. Shared vehicle users usually share data with operators on their origin, destination, payment method, and so on. Improper handling of these personal data can compromise privacy and discourage riders from using the service. In 2014, two former Uber employees revealed that corporate employees of Uber can track drivers and passengers by using an internal company tool named God View (Shaheen et  al. 2015; Morgan 2017). Deployment of data security practices is important to prevent data breach. Big data and artificial intelligence (AI)–based advanced algorithms can be deployed to detect threats and anomalies and provide improved security for shared data (Medium 2018). Electrification and automation will have a significant impact on peoples’ mobility in the future (Sprei 2018). The role of automation and electrification in improving the transportation system is highly recognized, but the associated costs may prevent its widespread adoption. In this context, the shared use of vehicles enabled with modern technologies is an affordable option. The shared use of electric and autonomous vehicles will have disruptive impacts on safety,

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Car-Sharing Service

Facilitate fleet of smaller vehicles

Reduce concerns on electric vehicles batter range

Electric Vehicles

Increase adoption of autonomous vehicles Reduce operational costs of carsharing services

Offer lower price to customers in car-sharing services

Improve utilization and management of autonomous vehicles

Reduce certain components (e.g., steering wheel, sideview mirrors)

Autonomous Vehicles

Enable rapid growth of autonomous vehicles

Figure 5-2.  Interdependency of carsharing services, autonomous and electric vehicles. Source: Based on the concept of “Virtuous Cycle” proposed from Mui (2016).

mobility, and the environment. Figure 5-2 shows the interdependency among carsharing, electrification, and automation in providing efficient transportation services. The convergence of shared vehicle services and automation can transform the mobility patterns of people and goods. Shared mobility alone cannot change mobility patterns. Shared mobility can partially replace personal vehicles, as the majority of US residents prefer to own and travel in personal vehicles (Poole 2018). Automation of shared vehicles can reduce users’ inconveniences, as shared autonomous vehicles (SAVs) can provide convenient on-demand services. Automation will alter the service models of shared vehicle services (Narayanan et al. 2020, Wang and Yang 2019). Carsharing users do not need to perform firstmile and last-mile trips to and from carsharing stations, respectively, with the use of autonomous vehicles for carsharing purposes. Parking issues associated with carsharing can be resolved by using automation, as autonomous vehicles can relocate themselves easily, thus minimizing parking needs and rebalancing one-way carsharing.

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In the context of ride-sharing, drivers’ origin, destination, and travel time-related constraints for matching drivers and riders can be avoided in an automated shared-ride scenario. However, the use of autonomous vehicles in a ride-hailing service can result in more systemwide VMT than ride-splitting services because of the zero occupancy of autonomous vehicles to serve the next request. Three business models have been identified by Stocker and Shaheen (2017) for SAVs: 1. Business-owned SAV, in which service is provided through an organization. SAV services can be owned and operated by the same organization or sometimes two or more organizations initiate partnerships to provide a shared vehicle service. 2. Individually owned SAV, in which personal vehicles are shared through decentralized peer-to-peer operations. In such operations, the rules are selected by individual owners without the involvement of any third party, which reduces the trip cost. 3. Hybrid SAV services, in which personal- and business-owned SAV are operated by SAV-sharing organizations. Electric vehicles can reshape mobility services because of the associated infrastructure development (e.g., charging infrastructures). Quieter rides, quicker charging systems, and the smoother acceleration potential of electric vehicles provide comfortable ride experiences to users. Moreover, zero emissions from electric vehicles (no tailpipe pollutants) is conducive for environmental sustainability. However, the cost of owning an electric vehicle is expensive compared with that of owning an internal combustion engine vehicle of similar size. Its long battery life can justify the higher initial capital cost of owning an electric vehicle. Toyota Prius and Tesla electric vehicles come with a battery warranty of 150,000 and 100,000 mi, respectively (Poole 2018). Shared vehicle operators or drivers can save in fuel and maintenance costs (because of lower moving parts) compared with sharing vehicles powered by internal combustion engines (Cella 2019). The application of electrification and automation in shared vehicle services at a reasonable extent can be seen in the near future. In Austin, the estimated mode share of shared autonomous electric vehicles (SAEV) was between 14% and 39% by assuming the SAEV service characteristics of $0.75 to $1.00 per mile fare, an 80 mi vehicle range, the availability of Level 2 charging infrastructure (provides a higher-output from a 240 V power source compared with the lower output provided by Level 1 charging infrastructure from a low 120 V power source), and an automation cost of $25,000 per vehicle (Chen and Kockelman 2016). The implementation of SAEV will increase the average transit trip length as shorter distance transit trips may be replaced by SAEV because of longer wait times and lower accessibility of transit services for shorter trips. The use of SAEV will reduce personal vehicle use. One SAEV can reduce 7 to 10 personal vehicles in Tokyo, Japan (Lacobucci et al. 2018).

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5.1.4  Future Research Directions As many emerging mobility services are being introduced every year, future research in shared vehicle services should focus on integration with other services such as PT, walking, and bicycling in a mobility-as-a-service (MaaS) platform. The development of policy frameworks to encourage shared vehicle services for different trip purposes (e.g., commuter trips, shopping trips, recreational trips) should consider land-use patterns (e.g., urban, rural), as trip patterns vary by land use and require different features in shared vehicle service models. Shared vehicle services can be promoted in urban areas to reduce congestion, whereas shared vehicle services are mostly driven by mobility needs in rural areas. To evaluate the performance of these services, activity-based shared vehicle service models can be developed, which can assist mobility policy analysis and development. The performance expectations from shared vehicle services vary among stakeholders (i.e., government transportation agencies, matching agency, drivers, and riders). Riders prefer minimum wait and travel time, and trip cost. Drivers prefer maximum profit with minimum detour. Service operators prefer maximum profit and the maximum number of matchings, whereas transportation agencies prefer a reduction in congestion and air pollution and the establishment of shared vehicle services as a sustainable transportation mode. Maximizing one stakeholder’s interest can negatively impact other stakeholders (Rahman et al. 2021). Therefore, future research should explore strategies to accommodate the conflicting needs and priorities of stakeholders in implementing shared vehicle services.

5.2  SHARED BICYCLE SERVICE A wide range of research studies define the concept of “sustainable transportation” from two perspectives: sustainable transport system and sustainable mobility (Daly 1992, Gordon 1995, CST 2005). The first perspective focuses on a transport system that is sustainable if it provides affordable and efficient transport service to all generations in an environmentally sound and equitable manner. The second perspective sheds light on people’s capability in moving and participating in activities without sacrificing other essential human or ecological values today or in the future (WBCSD 2001). These two perspectives together imply that promoting nonmotorized transportation modes, including cycling and walking, is vital for sustainable living. As compared to walking, which may be limited to a relatively short travel distance, cycling is more advantageous in capturing the three pillars of sustainability. In an era where a rapid development of information technology is seen, the emerging technologies for cycling are primarily reflected through the development trend of shared bicycle service (SBS). Today, IT-based systems are working toward making this type of service an integral part of smart

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cities. A prerequisite to bring about rapid transformation in a positive way is to conduct an in-depth research into SBS. This section introduces and discusses the definition and operations of SBS and its engineering and planning issues.

5.2.1  What is Shared Bicycle Service? Since its launch in Amsterdam in 1966, decision-makers, professionals, and the general public have heaped praised on SBS and promoted it as a cost-efficient solution to the “first mile/last mile” (FM/LM) transportation issue in cities and as an environmental-friendly and affordable active transportation mode to address urban problems (e.g., air pollution, traffic congestion) as compared to driving a private vehicle. Although the specific operations of SBS have evolved since the 1960s, it is broadly defined as a sustainable transportation strategy that enables bicyclists to gain short-term access to rental bikes on an “as-needed” basis (Shaheen et al. 2015). The main advantages of SBS lie in its sustainable feature deriving from the nature of cycling itself and its transport flexibility in terms of short-term use when needed. With the development of intelligent transportation technologies such as smart ID cards and mobile apps, the definition of SBS is extended as an innovative service through automated systems in cities. Overall, this concept can be disassembled into three dimensions illustrated in Figure 5-3. First, who is the responsible party to take charge of the system? In other words, who should be the one that enables bicyclists to access and use the service? Although many bikeshare programs are operated and managed by private businesses, the nature of SBS as a public service calls for an active role of

Figure 5-3.  Dimensions of shared bicycle service.

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government, just like the issue that PT is facing worldwide. One key argument under this dimension is that how should SBS be financed? The financial issues involve membership and usage fees and funding sources. Second, how and where do bicyclists gain the short-term access as needed? This pertains to the location choice and SBS connectivity within a transportation network. For example, discussions on docking stations and dockless systems have generated increased interest (Gu et al. 2019, Lazarus et al. 2019). In addition, arguments are being made in favor of placing shared bikes and related facilities close to transit stations to better serve “the last mile” and/or place them in locations with good proximity to residential and other destinations with high demand to better serve “the first mile.” This has become an important challenge of transportation and land-use planning for providing accessibility and connectivity to the booming users of bike sharing. Third, how should rental bikes be designed, provided, and maintained? Because of the feature of public use, the design of a shared bike in terms of its shape, color, weight, height, and other equipment (e.g., bicycle basket) invites more concern than a privately owned one. Moreover, the configuration of docks, either as a whole docking facility or as separated docks in one station, needs to be specified by considering available space, surrounding land uses, and potential demand. Initially, SBS was provided to the public for free in some cities like Amsterdam. In the last few years, the provision system has evolved to digital payment through smart phone apps, mainly benefiting from the emerging technologies in information and communication. Nevertheless, many disputes on the design and maintenance of physical equipment (e.g., bike-sharing docks) and automated systems have emerged from the perspectives of users and business providers (Figure 5-4).

Figure 5-4.  Generations of shared bicycle service operation models. Source: Developed based on the spotlights discussed in DeMaio (2009) and Shaheen et al. (2010).

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5.2.2  How is Shared Bicycle Service Operated? By the end of 2016, 900 bike-sharing programs had been in operation worldwide with the use of nearly 2.3 million bikes. Although each of them had been operated in the context of the political, economic, and urban conditions prevailing at that point of time, four models of different generations can be used to summarize the main types of SBS operations (Figures 5-3 and 5-5) (DeMaio 2009, Shaheen et al. 2010).

5.2.2.1  First Generation The first generation of SBS is mainly featured with a free and unlocked system. In 1965, a White Bicycle Plan in Amsterdam, Netherlands, officially introduced the concept of bike sharing to the public as a solution to deal with the problem of air pollution resulting from traffic congestion in Amsterdam’s inner city. Although this plan was suggested and devised by a Dutch designer and political activist Luud Schimmelpennink, it was specifically implemented by the Dutch anarchist group Provo, which is an organization against capitalism, communism, fascism, bureaucracy, militarism, professionalism, dogmatism, and authoritarianism. The

Figure 5-5.  Examples of four generations of shared bicycle service. Source: (a) Onderwijsgek via Wikimedia Commons, (b) Ehedaya via Wikimedia Commons, (c) Frédéric Bonifas via Wikimedia Commons, (d) そらみみ via Wikimedia Commons.

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plan was initially proposed to the municipal authorities for promoting safe streets, which should be accessible only by walking, cycling, and public transport. After the plan was rejected by the local authorities, Provo supplied 50 free bicycles without locks on their own for public use. This plan failed within days as the police confiscated these bicycles on the grounds of encouraging theft. Similarly, other free shared bicycle programs such as the green bike scheme in Cambridge, UK; red bike program in Madison, Wisconsin; and yellow bike program in Portland, Oregon, also failed because of the authorities citing the issue of stolen or lost bicycles. Luckily, the Yellow Bikes in La Rochelle, France, succeeded with their free offers to the public and other localized features. These three programs well explained the first dimension of SBS. On a superficial level, bike theft was cited as the major reason for the two cases of failure. However, the fundamental reason was the lack of support from governments or politicians. For instance, La Rochelle’s success was closely related to the mayor’s effort. Although the implementing agency has the responsibility to operate SBS, the government should be responsible for oversight, performance evaluation, and subsidy provision (Moon et al. 2019). With the free feature getting prominence in this generation, bicycles were unlocked and made available to bicyclists in several unregulated areas of the city (Shaheen et al. 2010). This “free-style” characteristic seems to offer much flexibility for bicyclists. However, similar to the problems posed by bike theft, overflexibility leads to chaos and other problems because bicyclists are left in the dark about where to find bikes when they need them the most and, in the absence of any tracking information, they do not know when other users would return their bikes, particularly when a small number of bikes is in circulation. Moreover, it makes maintenance complicated and even impossible when ownership and responsibility are unclear or unspecified. At the beginning of this generation, shared bikes were the only used ones with single-color painting because a limited budget placed constraints on the production of new bikes. Thereafter, both new and used bikes were designed with bright colors and more attractive logos.

5.2.2.2  Second Generation Primarily keeping in mind the issue of bike theft in the first generation, shared bicycle programs in the second generation stipulated locking bikes at designated docking stations with coin-deposit systems. With these systems, users could unlock and borrow bikes with a small deposit [e.g., the Bycyklen (City Bike) system in Copenhagen, Denmark, in 1995 required the use of a coin] and then return the bikes to get back their refunds. The 1995 Bycyklen system marked the start of the first large-scale urban bike-sharing program in Europe in this generation. Like the White Bicycle Plan in Amsterdam operated by Provo, the City Bike Program was administered by a nonprofit organization, rather than a local government. This reflects one main feature of SBS in this generation that nonprofit organizations (NGOs) were frequently created to manage bike programs (Shaheen et al. 2010). However, different from the first generation of shared bicycle programs that possibly faced challenges from the government and politicians, such

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as the police confiscation of Provo’s bikes, in the second generation, some local governments provided support by forming related organizations with funding (Shaheen et al. 2010). This is seen as a significant progress in this generation. In addition to the incorporation of designated stations and coin-deposit locks that reflect the improvements in physical design, shared bicycles in this generation were specially designed with better materials and advertising plates to promote utilitarian use and address issues such as cost. In spite of these improvements, a lack of technology in tracking users puts a spoke in the wheel of shared bicyclists in regard to long-time use and fails to address the problem of bicycle theft (DeMaio 2009, Shaheen et al. 2010). Although the support provided by local governments to bike-sharing programs is slightly better than that by past governments, the locations of designated bike stations are not well planned to connect with activity destinations (e.g., grocery stores, job centers) and other public transport facilities (e.g., bus stops). The limited powers and resources of nonprofit groups are cited as the reasons for this lack of professional planning. This may explain the fact that most stations are spread too far apart, leading to poor connectivity and poor maintenance of both bicycles and coin-deposit equipment.

5.2.2.3  Third Generation Although both conceptual and technical innovations in the first and second generations of shared bicycle programs have been instrumental in providing this mobility option to the public, the third generation is the key stage that makes these programs gain worldwide popularity. This generation started with the Bikeabout program at Portsmouth University in England in 1996 and saw a steady growth with the launch of the Vélo’v program in Lyon, France, in 2005, which is the largest program of this generation so far (DeMaio 2009). Although the fourth generation of this program has started, the majority of the existing shared bicycle programs in the world are still equipped with third-generation features. The lessons of regulations and management make both decision-makers and bicycling advocators realize the importance of setting an appropriate institutional and regulatory framework to optimize the benefits of SBS. As compared to the previous two generations, public–private partnerships in the third generation are more clearly defined but with more complexity. Moon et al. (2019) list 10 general functions that reflect the major components of an institutional and regulatory framework for a dock-based shared bicycle program: planning, detailed system design, asset ownership, social outreach, tendering and contracting, development of the financial model, implementation and operation, infrastructure implementation, oversight and evaluation, and expansion planning. Most of these functions come under the responsibility of the implementing agency and the main role of the government is oversight and evaluation. In the case of the Vélo’v program in Lyon, Grand Lyon (renamed to “Métropole de Lyon” in 2014) as the public party contracted the service to the transnational advertising company J-C Decaux through its Cyclocity subsidiary. The role of the public party is the key

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element to fit SBS into the city’s policy agenda. Such public–private partnerships also contribute to an improvement in the location choice of docking stations by planning and regulating the use of public space and their connectivity to the roadway system and public transport system. As compared to the second generation, the new design and provision component of SBS in this generation is kiosks or user interface technology with an IT-based system for bicycle check-in, checkout, monitoring, and tracking. The main features of this IT-based system are high-tech kiosks, Smart Cards and cell phone technology, and embedded GPS, enabling information tracking with membership service and deterring theft for nonmembers with a large deposit and/ or credit card information. Users can access the service through on-board GPS, the internet, and mobile payments to unlock a bike from the docking station. These modern features not only bring convenience and efficiency to both service users and operators but also significantly address the theft issue, which was the main challenge in the previous two generations.

5.2.2.4  Fourth Generation The popularity of SBS and its shortcomings have been captured by the concept of smart in the third generation, leading to increased interest in this concept from the perspectives of both research and practice. Although most bike-sharing programs are still equipped with third-generation features, the fast-developing technologies and the growing knowledge on sustainable transportation are pushing the fourthgeneration SBS forward. This generation started with the launch of BIXI in Montreal, Quebec, Canada, in 2009. Although this program is being operated by a nonprofit organization that was created by the city of Montreal, Canada, many private sector organizations are expanding their investments in SBS for profit, such as Nextbike in Germany. This implies that more and more advertising and for-profit organizations (e.g., ofo and Mobike in China) are evincing great interest in SBS in addition to public agencies, local authorities, and NGOs, which was not the case previously. The negative side to this positive development is that the parties involved in these partnerships are relatively more complex than the ones in previous generations. This factor may bring new challenges to the regulatory framework. DeMaio (2009) and Shaheen et  al. (2010) synthesized the features of the fourth-generation SBS into (1) flexible docking stations and dockless bikes; (2) bicycle redistribution improvement; (3) smartcard integration with other transportation modes; and (4) technological advancements in powering stations, GPS tracking, touch screen kiosks, and pedal assistance. The first two features exercise significant influence on the location choice of SBS. Instead of making docking stations a one-time infrastructure installation, some bike-sharing programs (e.g., BIXI) design mobile docking stations to allow easy installation and transfer to different locations based on usage patterns and user demands. This innovation reduces the pressure of location choice before the launch of the program and also increases the system efficiency with the flexibility to redistribute

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the bikes as needed. Some bike-sharing programs (e.g., MoBike) provide dockless bikes to address the issue of finding docking stations to return bikes and reduce the cost of installing expensive kiosks. On the one hand, these systems bring convenience with low cost, and on the other hand, they bring chaos. For example, the graveyards in China reflect the consequences of the rapid growth in providing dockless shared bikes, such as bicycle piling up and blocking crowded streets and pathways. The third feature significantly contributes to an improvement in connectivity between SBS and other transportation modes, such as PT, taxi, and ride-hauling, through smartcards and subsidy. For instance, Pittsburgh is the first US city to offer free bike share use to transit riders. Many bike-sharing programs place docking stations next to transit stations. Although this will need multiagency collaboration and coordination, this integration to some extent supports the role of bike sharing in serving the FM/LM connection to PT. Particularly, SBS has been viewed as one promising option of mobility as a service to provide seamless trips without any extra burden on affording private vehicles (Utriainen and Pöllänen 2018). The popularity of flexible docking stations partially benefits from green power technologies such as solar panels and rechargeable batteries. The ongoing improvements in GPS tracking and touch screen kiosks help bicyclists access and use the system with better security and route record. Moreover, the provision of electric bicycles in the fourth-generation sharing systems enlarges the user pool through enabling long-distance trips and cycling in hilly areas. In particular, people with certain disabilities may be able to bike with electric pedal assistance. These technological improvements in designing bike-sharing systems make the service more sustainable and human-oriented.

5.2.3  Engineering Issues The success of bike share is largely determined by a good design of a bike-sharing system, including the number of stations and their locations, number of bicycles in the system (capacity), and bicycle repositioning or rebalancing. These components are vital to increase the efficiency of the bike-sharing system and promote bikesharing ridership. The key motivating factors for people to use shared bikes are the convenient access to SBS when needed and the availability of bikes to check out and empty slots for returns (Çelebi et al. 2018). One of the major challenges in the design of an attractive SBS lies in the estimation of the spatial distribution of the potential demand for bike trips to locate bike-sharing stations and determine the desired inventory level at each station. With advancements in information and communication technologies (ICTs), it is now becoming possible to leverage big data such as social media, smart phone, and GPS to improve the accuracy of bike demand prediction. Another major challenge is the bike rebalancing problem arising from the nature of many one-way bike trips or demand asymmetries (Çelebi et al. 2018). The service level of SBS is drastically affected by imbalances in the distribution of bikes among stations. Thus, an effective and economical

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bike-repositioning system is crucial to meet the dynamic and stochastic demand and ensure the quality of SBS. In addition, to guarantee a safe, inviting, and accessible environment for cyclists, it is essential to provide a well-connected, safe, and comfortable bike network between bike-sharing stations as well as between SBS and other transportation modes such as bus and subway. In most US cities, transportation networks built over the past century largely favor automobiles over cycles. Streets lacking bikeways make bicycling dangerous and discourage people from using bikes. Thus, local efforts on adding bike lanes, especially protected bike lanes and multiuse trails, are necessary to ensure a safe, connected, and attractive bicycling environment for promoting SBS and bike ridership. Lastly, the engineering challenge is related to the impacts of SBS on a transportation system that consists of three components: vehicle, guideway, and operation plan (Boyce 2009). SBS is a transportation mode using a bike as one type of “vehicle” to move people. The operation of SBS needs to be in coordination with other vehicles within a guideway system. For instance, the location of bikesharing stations and the physical layout of shared bikes in a station should be selected and designed to minimize conflicts with travelers using other vehicles. One fundamental goal of promoting SBS is to attract trips previously made by private vehicles. However, this mode substitution rate has been investigated with disappointing results (Ricci 2015). Instead, high mode substitution because of the rise of bike sharing can be seen in sustainable travel modes such as walking and riding a bus. Therefore, designing and managing a transportation system to organically integrate SBS with other transportation modes is a challenging but critical task for the engineers and planners of sustainable transportation systems.

5.2.4  Urban Planning Issues The opportunities and challenges of providing SBS lie in not only how it should be physically designed, provided, and managed, but also how its role should be institutionally and psychologically defined and planned.

5.2.4.1  Stakeholders in Planning Shared Bicycle Service The previous section specifically discussed the features in the dimension of a regulatory framework for the four generations of SBS. During those four generations, the major stakeholders involved include NGOs, local governments and public authority, advertising companies, and profit organizations. Their roles are different across generations and these changes in the planning and implementing process influence the SBS operating model, revenue sources, and corresponding pros and cons. Although there is no last word on any “correct” decision-making scheme of SBS, the role of governments is a key factor in sustaining SBS. The argument over the role of the government in this aspect of SBS is somewhat similar to the case of PT as SBS was promoted as a semimass transit for a while in the past decade, particularly when integrating it with other forms of public transportation. Since the 1970s, a series of government reform movements in the United States has positioned local governments as a catalyst

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in “steering” rather than “rowing” (David and Ted 1992). In the case of planning SBS, it is best for the government to set up a regulatory framework by considering public interest, rather than providing SBS directly to users. This is how some bike-sharing programs work in some cities such as BIXI in Montreal, Canada. However, this governmental role does not play itself out properly in some places. For instance, instances of growing and frequent complaints about bicycles being dumped on street corners or in parks or blocking roads in China as a result of a boom in the bike-sharing industry in this decade have come to light, calling for a clear regulatory framework on operation and maintenance. In addition, users themselves are also a part of stakeholders whose needs and interests should be considered by the government. The more the parties involved in the service, the more the challenge for the government to play the “steering” role. Therefore, coordination and collaboration among key stakeholders are crucial for the sustainable development of SBS.

5.2.4.2 Planning Shared Bicycle Service within an Auto-Oriented Urban Structure Although a proper regulatory framework is important to fit SBS into a city’s policy, the big challenge in promoting its use is the popular urban structure and culture centered on the automobile. In Europe, this challenge is relatively small because Europe does not have a sprawling urban structure and the emphasis is on higher densities, protransit policies, and a more compact urban growth. However, most medium-sized cities in North America, such as Fresno in California and Cincinnati in Ohio, are facing this challenge in a big way within an auto-oriented urban environment since the 1950s. The challenge could be specified as follows. First, low-density suburban development has lengthened the average travel distance for daily activities, making trips by bicycles impractical in many situations. This has been worsened by the inadequacy of PT, by curvilinear roads, and by disconnected streets since postWorld War II. This raises concern over the potential of promoting SBS to address the FM/LM problem. The promotion of this concept may work in neighborhoods that already have some transit service covered but can be problematic in areas without transit service. Second, safety is a big concern for cyclists, what with insufficient bikeways and necessary facilities in an autoprioritized urban environment. Moreover, an environment without many pedestrians and cyclists may create an unsafe atmosphere that is prone to crimes. Third, culturally and psychologically, people who have been living in an auto-oriented urban setting cannot easily change their existing travel mode choices. Therefore, education programs and strategies need to be well designed and promoted to reduce these cultural and psychological barriers. Although urban sprawl in many Asian cities has not reached the same level as that of North America, similar barriers as discussed from the point of view of the auto-oriented development trend in those cities are expected. For instance, China has formulated urban transportation policies to encourage the use of motor

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vehicles as an economic development strategy since the beginning of the 1990s (Yang et al. 2015). More and more traditional narrow streets are being upgraded to broad lanes to encourage vehicle drivers to prioritize road use. Like in the United States, but at a more serious level, the public perception that using nonauto transportation modes (e.g., public transit and cycling) constitutes a lower social status makes the transition from driving to biking more difficult. From the perspective of urban planning, the ways to deal with these challenges are essentially related to the question that services like SBS should be planned for whom. It is, therefore, important to better understand the demands of different social groups for this service in different urban contexts.

5.3  FIRST MILE/LAST MILE SOLUTIONS The term FM/LM originated from telecommunications, and it referred to the distribution network connecting local exchanges with customers (Strover 2000). Afterward, people started using the term in the supply chain sector to represent the stage of sending packages to service providers and the stage of delivering packages to customers (Chopra 2003). In the context of urban transportation, FM/LM has been widely used to describe the beginning and end of PT trips. PT riders often have to travel a certain distance from their place of origin to PT stops and from PT stops to their final destinations, which is alternatively known as the FM/LM gap. FM/LM determines whether PT services are reachable to the public and impacts PT system level-of-service, and this connection is essentially for social well-being (Mavoa et al. 2012). In particular, transportation-disadvantaged people, who have no or limited access to private vehicles, are more reliant on PT to travel around. A large number of zero-vehicle households in the United States are low-income and minority households (AASHTO 2021, Tomer 2011). According to an investigation of 211 PT survey reports in the United States (Clark 2017), PT riders are disproportionately minorities (60%) and low-income people. At the national level, 21% of surveyed PT users report a household income of less than $15,000, and in small cities, this number has more than doubled (i.e., 48%). Increased access to PT by bridging the FM/LM gap can greatly support minority and low-income communities and ensure an inclusive society. PT, as a more affordable and sustainable mobility choice than private vehicles, can bring down traffic congestion, reduce environmental footprints, and advance transportation equity. Seamless FM/LM connectivity is a critical success factor to maximize the attractiveness and benefits of PT services by extending the PT point-to-point mobility. In addition, the rapidly developed ICTs are playing an increasingly important role in integrating between transportation modes and facilitating FM/LM travels. The rest of the section reviews the common features of the FM/LM feeder modes and their advantages and disadvantages, and the potential FM/LM solutions in light of ICT advancements within the context of urban multimodal transportation.

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5.3.1 Common Transportation Means Used for Connecting First Mile/Last Mile The access to PT is usually determined by the feeder mode used and its network connectivity to PT. Intuitively, using a more efficient and speedy FM/LM connector can extend the reach of transit service to further areas. Walking, biking with traditional bikes and e-bikes, and driving an auto are common PT feeder modes. Each mode has a different FM/LM access pattern and its own characteristics of suitability in urban environments. Traveling on foot is the most common and flexible way for FM/LM trips, especially in dense urban areas, with no costs. For pedestrians, the average walking speed normally ranges from 2.85 to 3.27 mph (Bohannon 1997). Limited by speed, the walking distance is comparatively short and restricts the reach of transit. Typically, the walking distance is within 0.25 mi to a bus stop and 0.5 mi to a rail station (Kittelson and Associates et  al. 2013). In the United States, a dispersed land-use pattern is predominant outside urbanized regions. In lowdensity areas, people living outside the pedestrian-transit catchment area find it difficult to access PT services or meet their needs using PT (Zhao et al. 2003). Most commuting cyclists can bike at a speed between 10.56 and 12.43 mph (Hendriksen et  al. 2010). Bicycles, as an FM/LM connector, can easily reach places within a distance of 2 mi away from PT features (Hochmair 2015; Lee et al. 2016; Rietveld 2000; Zuo et al. 2018; Zuo and Wei 2019a, b). However, the bicycle as a transit feeder mode is highly underutilized mainly because of the risks and dangers prevalent in auto-dependent environments. In most US cities, only limited on-street bikeways are available, and cycling to PT is largely discouraged. Safety concerns from high-volume motor traffic make cycling a less prerefered mobility mode. Apart from the availability of safe bike networks, other factors like topographical condition, physical health, and travel distance can influence people’s choice of riding a bike. E-bikes, thanks to their motors, are more convenient, easier, and effortless against regular bikes and can help overcome some of the barriers to cycling such as hills and long distances. E-bikes with a travel speed of 30 mph on average will make FM/LM travel easier and faster, thus extending transit service span. But still, lacking safe bikeways remains a barrier for local e-bike access from and to PT nodes. The speed advantage of automobiles can help expand the service range of PT significantly as compared with nonmotorized feeder modes. However, auto is the costliest access mode to PT services (Tabassum et al. 2017). Taxicabs have long been available as an FM/LM mobility service. Their share of the market is limited due to the high cost and difficulty in hailing and accommodating on-demand service (Merlin 2017). Given the problems of urban traffic congestion, parking restrictions and insufficient parking lots, and high parking costs in city centers, park-and-ride is a more viable alternative than driving a private car for an entire trip. This option became popular throughout the United States and Europe in the 1990s (Habib et al. 2013, Karamychev and van Reeven 2011). Parkand-ride combines the flexibility of private cars for traveling outside urban areas

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with the efficiency of PT in moving a large number of people in urban areas (Cairns 1998). The overall capacity of a park-and-ride system is restricted by the parking capacity near PT stations (Cairns 1998, Habib et al. 2013). In addition, high traffic congestion can take place around park-and-ride stations (Habib et al. 2013, Wright and Nelson 2014). Kiss-and-ride is another similar way of combining private vehicles and PT to complete trips. Different from park-and-ride, it requires no parking spaces but needs a driver, usually a household member, to drop off and/or pick up the transit rider at a PT stop/station. Shuttles also serve as an important transportation means of community circulation to connect regional PT (SCAG 2009). Shuttles can be either publicly (e.g., by PT agencies) or privately operated (e.g., by employers, institutions). Shuttle services are finely tailored to local needs and are usually operated only within certain designated areas and hours to complement fixed-route PT services.

5.3.2  First Mile/Last Mile Strategies The access to PT is largely dependent on the characteristics of land use and transportation systems, mainly including locations of transit features, surrounding land uses, availability of parking facilities at stops/stations, and FM/LM access environments.

5.3.2.1  Land-Use Planning Land use impacts traffic mobility and accessibility in a variety of ways. Transitoriented developments (TODs) and form-based planning are effective land-use strategies to promote transit use through compact and mixed-use developments around transit features. The concept of TOD was first proposed in the 1990s by Peter Calthorpe and encourages the development of vibrant communities around transit facilities (Calthorpe 1993). TOD communities are typically designed to be walkable and bikeable through the planning of small-size blocks and pedestrianand cyclist-friendly facilities. To date, many locations all over the world, such as Hong Kong in China, Montreal and Toronto in Canada, Paris in France, and Portland and San Francisco in the United States, have developed TOD communities. Typically, a half-mile pedestrian-transit catchment area is used as a geographical unit in designing TODs. A common argument is that the denser the land use within the catchment area is, the higher the transit ridership will be. However, there rises one criticism of TODs that they can increase housing values near PT stations, thus forcing low-income households to move further away from these stations. To prevent this problem of gentrification, equitable TOD planning should ensure the supply of affordable housing units in TOD communities. The San Francisco Bay Area, which is an example of one of the most successful TOD practices in the United States, has developed 24 transit village projects (13 completed to date)—dense, mixed-use communities—near Bay Area Rapid Transit (BART) stations over the last two decades. To serve households of all income levels, BART is planning to build and provide about 7,000 affordable housing units (35% of BART’s housing) to the public by 2040.

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5.3.2.2  Integration between Public Transit and Other Feeder Modes 5.3.2.2.1  Network Connectivity Accessible and safe paths to and from PT is central to guaranteeing community access to PT services. Improving street network connectivity that better supports walking and cycling is among the most cost-effective FM/LM strategies. Streets with sidewalks or streets with a speed limit of 20 mph or less are accessible or considered safe to pedestrians. Building continuous bikeways, especially dedicated bike lanes, to PT is essential for the integration of bike and transit (El-Assi et al. 2017, Griffin and Sener 2016, Martens 2004). The introduction of bikeways and sidewalks to auto-dependent streetscapes, accompanied by landscaping, streetlights, and other safety-related features, makes roadways more attractive for pedestrians and cyclists. In the United States, several transit authorities such as BART in Bay Area, California, and the Washington Metropolitan Area Transit Authority in Washington, DC, have developed transit-specific bicycle and pedestrian master plans to improve walking and cycling access to PT. 5.3.2.2.2  Parking and Electric Charging Facilities The availability of secure and convenient parking is another critical factor for a successful integration of PT systems with other transportation modes such as bicycles and automobiles. Locations and parking capacity near park-andride stations largely determine the ease of transfer between car and PT and the efficiency of park-and-ride systems. Provisions of automobile parking near transit stops or stations can be expensive and limited by land availability (Habib et al. 2013, Lesh 2013). Largely because of these reasons, park-and-ride lots are usually located outside of urban areas. Improving the availability and quality of bike parking facilities near or at transit stops and major adjacent destinations are vital to boosting bicycle-transit intermodal connectivity. Facilities such as lockers, storage rooms, and bicycle racks should be provided based on the bicycle-transit demand. Priority locations of parking amenities near transit should be considered for convenient bike-and-ride. In addition, enhanced security and protection from weather may need to be provided, especially at major stations and terminals. With the increasing market share of electric vehicles (EV), the provision of convenient charging facilities near transit stations greatly supports the use of EVs as a PT feeder mode along with the growing cohort of EV drivers. In the United States, a few programs that combine EV with PT have been implemented. For instance, Los Angeles pioneered the integration of EV charging with PT systems. In 2013, five metro transit locations in Los Angeles had EV charging stations installed, and by 2019, this number had increased to 15. The Massachusetts Bay Transportation Authority (MBTA) installed 30 EV chargers at MBTA park-andride lots. Priority and reserved parking spaces and free charges at these MBTA stations are offered to EV drivers. With the increasing e-bikes either privately owned or shared, charging stations for e-bikes near transit stops are necessary to encourage bike-and-ride. Sacramento Regional Transit (SacRT) has partnered with JUMP, an electric shared micromobility provider, to install charging bays inside SacRT light rail stations. SacRT installed the first charging bay at the City

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College Station in September 2018 and expanded it to seven stations a year later. Eventually, a total of 23 such charging bays will be implemented as initially planned. These charging bays that allow e-bikes to be parked and charged within light rail stations speed up the integration of e-bikes and PT.

5.3.2.3  Innovative Motilities as Potential First Mile/Last Mile Connectors Shared mobility has emerged in global cities not only as an innovative transportation mode but also as a potential solution to the FM/LM problem of PT (Shaheen et  al. 2015). The US Department of Transportation defines shared mobility as “the shared use of a motor vehicle, bicycle, or other lowspeed transportation mode” that “enables users to obtain short-term access to transportation on an as-needed basis, rather than requiring ownership” (Shaheen et al. 2015). Figure 5-6 shows different types of shared mobility services. In shared mobility services, those small vehicles that are either fully or partially humanpowered, such as regular bikes, e-scooters, and e-bikes, are defined as “shared micromobility” (NACTO 2019). 5.3.2.3.1  Shared Micromobility From a revolutionary concept in the 1960s, bike sharing witnessed a slow pace of growth until advancements happened in bike-tracking technology. In 1996, the first automated bike rental system was implemented in the United Kingdom, and this speeded up growth in bike-sharing services throughout Europe and Asia (DeMaio and Gifford 2004). The transition to dockless bike sharing began around

Figure 5-6.  Shared mobility service models. Source: Shaheen et al. (2015).

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a decade after the advent of docked shared bikes. China was the pioneer country to implement a dockless bike-sharing platform in 2015. Afterward, dockless bike sharing exploded across the world. Mobike, Lime, JUMP Bikes, Motivate, Ofo, and Spin are a few examples of popular dockless bike-sharing service operators. The bike-sharing service provides flexible FM/LM connectivity, thus expanding transit door-to-door mobility without the need to own and carry a bike/scooter. NACTO (2019) reported that in 2018, 84 million shared micromobility trips were taken in the United States, of which 38.5 million trips were on e-scooters and 36.5 million trips on station-based shared bikes. In 2019, the national total of shared micromobility use reached 136 million trips, and scooters (86 million trips) overtook pedal bikes (docked and dockless) as the most preferred vehicle for shared micromobility vendors (NACTO 2020). By the end of 2019, dockless scooter programs had been available in 109 US cities. At the same time, dockless e-bikes are also growing in popularity, with 4 million more trips taken from 2018. In New York City, the use frequency of e-bikes is up to 15 times per day during high-demand months as compared to about 5 times per day for regular bikes (NACTO 2019). E-bikes were introduced to San Francisco in May 2018. By the end of 2018, e-bikes had comprised a third of the city’s shared micromobility fleet. Shared micromobility provides people with flexible choices for short-distance trips and convenient ways to complement PT trips. Using data sourced from a number of US cities, NACTO (2019) summarized that around 60% of stationbased shared bike users ride to connect to transit, and the figure is about 27% for shared-scooter users. In the United States, many PT agencies such as the Greater Dayton Regional Transit Authority, the Kansas City Area Transportation Authority, and the Sacramento Regional Transit District are forming partnerships with third-party shared micromobility providers to build an interconnected PT and a shared micromobility system to manage FM/LM travel. 5.3.2.3.2  Ride-Sharing and Carsharing Advances in ICTs, combined with smartphone applications and vehicle location positioning, enable a host of new mobility services, including carsharing and ride-sharing services (TRB 2016). Both services provide alternatives to owning a private vehicle. Carsharing is an automobile rental service, typically for a short period. As of May 2019, carsharing services were available in 59 countries, with 236 operators in 3,128 cities worldwide (Phillips 2019). There are three types of carsharing services—station-based (e.g., Zipcar, Communauto, and Flinkster), free-floating (e.g., ShareNow, GIG Car Share, and Emov), and peer-to-peer (e.g., Turo, Getaround, and Snappcar) services. Among these three models, stationbased carsharing is the dominant one and has continued to grow over time. As reported, 21 carsharing programs were there in the United States with a total membership of 1,439,399 users and 15,244 cars in January 2018 (Shaheen and Cohen 2020). Figure 5-7 shows the growing market size of carsharing in the United States from 2009 to 2018. The increasing popularity of carsharing, together with its affordability and flexibility in one-way travel, makes carsharing a great mobility option to short-distance travels including FM/LM trips.

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Figure 5-7.  Carsharing market size in the United States: 2009–2018. Source: Created based on the data presented in Shaheen and Cohen (2020).

Different from carsharing, ride-sharing (specifically, on-demand ridesharing) is designated for renting car ride services, which can be shared by usually up to four passengers at the same time. In ride-sharing services, drivers and passengers are typically linked up through smartphone applications. Uber is the pioneer in the on-demand ride-sharing market that allows passengers to book a ride via a mobile application. Uber has been dominating the US market since its introduction and had established operations in more than 70 countries by December 2021. The ride-sharing market has been witnessing explosive growth worldwide, with TNCs in Africa (e.g., Bolt and Jrney), Asia (e.g., Didi, Grab, and Ola), Australia (e.g., GoCatch, Shehah, and Rydo), Europe (e.g., BlaBlaCar, Gett, and Via), North America (e.g., Uber and Lyft), and South America (e.g., 99 and Cabify). An increasing number of US transit agencies are collaborating with TNCs such as Uber and Lyft to improve FM/LM mobility options and/or enhance suburban mobility. Fare discounts are typically provided to promote ridership. Starting from July 2015, the Metropolitan Atlanta Rapid Transit Authority (MARTA) partnered with Uber and later with Lyft to provide customers ridesharing services to destinations out of MARTA reach. MARTA riders can enjoy a 20% to 50% Uber or Lyft discount for their trips to and from MARTA rail stations. Both carsharing and ride-sharing services enable passengers to have short-term access to PT on an “as-needed” basis. Integrating PT with on-demand ride-sharing and carsharing services can complement PT services, especially for low-density areas with scattered demand and poor access to PT nodes, by walking and cycling. 5.3.2.3.3 Microtransit Microtransit is a transportation means to supplement fixed-route PT services. It is usually a privately operated shared transportation system that offers fixed routes and schedules, as well as flexible routes and on-demand services. Bridj,

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Chariot, and Via are some of the leading microtransit service providers. In general, microtransit vehicles are vans and small buses. Microtransit providers can leverage crowdsourced data to aggregate demand by adjusting routes and stops in a responsive and real-time manner (Westervelt et al. 2017). Enabled by technologies similar to ride-sharing, microtransit can augment traditional fixedroute PT services and serve as an FM/LM connector. In August 2016, Chariot announced Chariot Direct in San Francisco, California, as a variation to its microtransit service to provide FM/LM services from BART hubs to places in the city (Shaheen et al. 2015).

5.3.3  Technologies Powering First Mile/Last Mile Connection Benefiting from ICTs such as Bluetooth, smartphones, and other rapidly developed telecommunication networking technologies (Sumalee and Ho 2018), RTI can be exchanged among vehicles, infrastructure, and travelers, which can further facilitate seamless FM/LM connections. The high uncertainties associated with waiting times for PT vehicles lead to an extension of the PT travel time, in turn, leading to delays, and the reliability of transit travel time becomes a question mark. ICTs enable PT authorities to collect transit RTI and publish accurate transit vehicle arrival times. In the past decade, many PT agencies have installed positioning sensors (e.g., GPS) on transit vehicles and disseminated transit vehicle real-time locations and trip updates to passengers through websites and mobile applications. With such information and tools, passengers can trace their intended routes and, therefore, plan their trips in advance. An integrated digital platform (such as a web- or mobile-based multimodal trip planner) that provides multimodal RTI can greatly assist trip planning involving PT and its feeder modes, as well as the estimation of entire trip time and cost. In addition, ICT tools facilitate convenient transfers between PT and other transportation services. The almost ubiquitous smartphones allow PT riders to check the availability of shared mobility services and make reservations for shared rides or shared-use vehicles (Lane and McGuire 2014). The Valley Metro Regional Public Transportation Authority launched a pilot Pass2Go—a mobile trip planning platform with RTI of public transportation and various FM/LM options in an integrated payment system (Figure 5-8). In addition to public transit RTI, the platform also attempted to present information from TNCs (such as Uber and Lyft) and local bike-sharing provider(s) to users for assisting their FM/LM travel decision-making. As indicated by a Pass2Go user survey, 74% of 332 respondents reported improved access to PT because of the improved RIT and trip planning methods from the Pass2Go app. The advent of autonomous vehicle (AV) technologies allows the emergence of SAVs, which can provide an inexpensive on-demand FM/LM connection (Krueger et al. 2016). In 2018, Waymo launched a pilot program to provide Valley Metro employees in Phoenix, Arizona, FM/LM rides to nearby metro stations with AVs (a photo of Waymo’s AVs is shown in Figure 5-9). In Frisco, Texas, the Frisco Transportation Management Association has been working in cooperation with Drive.ai to deploy self-driving on-demand service starting from 2018. This

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Figure 5-8.  Example of trip options in Pass2Go. Source: Martin et al. (2020).

Figure 5-9.  Waymo’s AV units at a Valley metro station. Source: Waymo (2018).

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private–public collaborative effort is targeted at improving FM/LM mobility with innovative technologies. In relation to carsharing and ride-sharing services, a fully automated vehicle that does not require a driver will further expand the scope of carsharing, ride-sharing, and microtransit and also make these options more attractive. Meanwhile, SAVs are advantageous for addressing the issue of vehicle imbalance inherent in carsharing systems and for relocating vehicles in advance to better match vehicle supply and travel demand (Fagnant et al. 2015). In addition, SAVs can exploit RTI to better coordinate with the spatial and temporal allocation of PT services (Shen et al. 2018), thus reducing passengers’ waiting time for PT vehicles and the total travel time as well.

5.4 SUMMARY Section 5.1 focused on shared vehicle services. The shared vehicle service models follow the concept of a sharing economy, which promotes the idea of renting and borrowing goods and services instead of owning them. Theoretically, shared vehicle services are envisioned to reduce traffic demand by increasing mobility options, resulting in less use of private vehicles. Advanced applications of communication, internet and mobile technologies, social-networking sites and computing infrastructures enhance the potential of shared vehicle services by enabling real-time on-demand transportation services to mass populations. On the contrary, shared vehicle services pricing and safety considerations, parking regulations, insurance and tax issues are among the factors that may influence the trends in the shared vehicle service market in the United States, including the latest private vehicle sales, in the long run. SBS has been viewed as one key strategy to promote green transportation. Section 5.2 unfolds a scroll of SBS from its definition to the operation mechanism and to the main issues from the perspectives of both engineering and urban planning. The definition of SBS is discussed in three dimensions: responsible party; location choice and connectivity; and design, provision, and maintenance. Although the essential definition of SBS is consistent as a rented service with available bikes at certain locations, the variations within these dimensions decide the different features and operation mechanisms across the SBS programs at both spatial and temporal scales. Based on these three dimensions of the definition, this section specifically presents and discusses the operation mechanism of SBS for four generations, covering who is taking charge of the system, where and how shared bikes are located, and how shared bikes are designed and provided from the 1960s to the present day. SBS-related engineering and urban planning issues are summarized in terms of the different social, political, and urban contexts. Seamless FM/LM connectivity is a critical success factor to maximize the attractiveness of PT services and expand PT point-to-point mobility. Section 5.3

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reviews FM/LM issues in PT services and commonly used FM/LM connectors such as walking, bikes, and autos. The advantages and disadvantages of the FM/ LM connector and the FM/LM access pattern and its suitability in an urban environment are comprehensively presented. Planning countermeasures to the FM/LM problem are introduced in this section, including transit-oriented landuse planning, improved network connectivity between feeder modes and PT, convenient parking facilities at PT stops/stations, and emerging shared mobility options as FM/LM connectors. The speedy development of ICTs enables smarter and more flexible mobility services to mitigate the problem of FM/LM. The availability of real-time exchange of information among vehicles, environments, and travelers, the possibility of fully automated vehicle technologies in the future, and the potential applications of ICTs for minimizing FM/LM gaps and facilitating FM/LM trips are also discussed in Section 5.3.

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CHAPTER 6

Cooperative and Automated Traffic Control Heng Wei, Gaurav Kashyap, Zhixia Li

6.1 TRAFFIC SIGNAL CONTROL METHODS IN CONNECTED AND AUTOMATED VEHICLE ENVIRONMENT Connected and Automated Vehicle (CAV) technologies have great potential to provide opportunities for innovating traffic control systems in terms of safety and operational efficiency, as well as increasing energy savings (Guo et al. 2019). For example, CAV data can be used to provide equivalent “detection” datasets as a ubiquitous mobility data source to better estimate current traffic flow states and predict vehicle arrivals, as well as the parameters involved in signal timing. In particular, V2X communication technologies can make it possible to adapt CAV operations to deal with signal timing to reduce overall congestion and fuel consumption in urban areas (Guo et al. 2019). Guo et al. (2019) conducted an intensive and in-depth research, resulting in a valuable categorization of existing studies for CAV-based traffic control into six types of approaches, namely, driver guidance, actuated (adaptive) signal control, platoon-based signal control, planning-based signal control, signal-vehicle coupled control (SVCC), and multivehicle cooperative driving without traffic signals. In addition, a method for eco-traffic signal timing driving and traffic signal control has been applied to optimize traffic signals for the environment (CVRIA 2016). With this rapid development of CAV technologies, eco-driving and traffic signal control has been included in connected vehicle reference implementation architecture (CVRIA). Based on such research understanding, Table 6-1 provides brief descriptions about each type of traffic signal control method in a CAV environment. Figure 6-1 illustrates the underlying evolution of the approach development philosophy and associated research paradigms, which were adapted and modified from the survey of Guo et al. (2019). The upper arrow represents the trend of considering more vehicle driving management in traffic control systems, whereas the lower arrow represents the trend of considering more traffic flow management 223

Under the constraints of the physical limits (e.g., acceleration, speed, and turn angles), the optimal speed guidance is provided to human drivers of CVs or communicate with AVs to help improve the control performance. Reducing the uncertainty of compliance of human drivers in avoidance of safety risk (e.g., running red lights) and/or saving fuel by driving in economic models are among the main objectives of the advanced driver guidance (Li et al. 2012, Katsaros et al. 2011, Schuricht et al. 2011, Tang et al. 2018, Ubiergo and Jin 2016, Wu et al. 2010b). In this type of control method, the state variables include vehicle speed and position that are involved in the equations of vehicle dynamics or traffic flow models. Vehicle accelerations and turn angles are among traffic inputs, and the signal timing and phases are viewed as environment conditions (Guo et al. 2019) Actuated or adaptive traffic control relies more on the prevailing real-time traffic information without the need for too many short-term future traffic conditions. Based on CAV data providing accurate information on the position of arriving vehicles, actuated or adaptive control can dynamically adjust the timing parameters to respond to real-time traffic arrival changes by extending or shortening the current phase or adding an extra phase to make on-time changes (Guo et al. 2019). In general, such CAV-data-based signal control results in a more efficient utilization of intersection capacity than traditional fixed-time signal control or inductive loop-based actuated signal control in which signal phases and cycle lengths are preselected based on historical traffic patterns (Roess et al. 2004; Zhang and Wang 2011) In platoon-based traffic signal control, it is essential to identify the platoons by categorizing individual vehicles into pseudo platoons and predict their arrival time in advance. Based on such a traffic prediction, the signal timing plans are scheduled to allow the platoons to pass the intersections with no or little interruptions along the corridor. The concept of the platoon-based traffic signal control was introduced several decades ago (Mirchandani and Head 2001). However, only the V2X technique that emerged in recent years is potentially viewed as a real opportunity to make it possible to identify the platoon so that platoon-based optimal signal timing plans can be devised accordingly (Guo et al. 2019, He et al. 2012, Liang et al. 2018, Lioris et al. 2017, Xie et al. 2012). In the platoon-based “oldest arrival first” traffic signal control method, the concept of vehicular ad-hoc networks (VANETs) has been proposed to collect information on the real-time speed and position of CAVs. The collected information is then grouped into approximately equal-sized platoons with an attempt to minimize the difference between the maximum and the minimum required processing time (Guo et al. 2019, Pandit et al. 2013)

Advanced driver guidance based on CAVs

Platoon-based signal control

Actuated and/or adaptive signal control

Description

Type

Table 6-1.  Summary of Traffic Signal Control Methods in CAV Environment. 224 Disruptive Emerging Transportation Technologies

In the planning-based signal control, arrival distributions can be assumed in advance such as Poisson or uniform arrivals, or arrival times of the platoons featured with uniform arrivals within each platoon are viewed as the given condition. In other words, the planning-based methods often estimate the actual arrival time of every vehicle and predict traffic conditions in a forward time horizon (Guo et al. 2019). Compared with the platoon-based methods that categorize the incoming vehicles as platoons and ignore the inner dynamics and disturbances among vehicles in the same platoon, the planning-based methods treat all vehicles at the same level In the SVCC method, information between signals and CAVs is exchanged in real time via V2X technologies to optimize vehicle operation and signal timing simultaneously to achieve better traffic control performance of the intersections (Guler et al. 2014, Sun et al. 2017, Xu et al. 2017b, Yang et al. 2016, Yu et al. 2018). The state variables depicting vehicular traffic information include the queue length, travel time, vehicle states (e.g., throttle and exhaust system states, and battery state of charge if electric vehicles are considered), and they can be incorporated into proper traffic flow models. The traffic signal control variables include signal timing and phases, and vehicle operations (e.g., turn angle and gas pedal), which should be constrained by proper constraints related to both signal timing parameters (e.g., minimum/maximum green times, cycle lengths) and traffic flow models. The SVCC can also be implemented to solve transit priority control problems. Although the concept sounds good, the complexity of SVCC models invites more challenges in modeling, which subsequently make problems more challenging to solve under complex constraints in a realistic control infrastructural system It is referred to as “a special adaptive traffic signal control system” wherein the application’s objective is to optimize traffic signals for the environment, while balancing the performance in maximizing the intersection throughputs or the level of service. The Eco-Traffic Signal Timing application processes real-time and historically connected vehicle data at signalized intersections to reduce fuel consumption and overall emissions at the intersection, along a corridor, or for a region (CVRIA 2016). The application evaluates traffic and environmental parameters at each intersection in real time and adapts so that the traffic network is optimized using the available green time to meet the actual traffic demands while minimizing the environmental impact. In general, it is referred to as the intelligent intersection management of CAV operation and control at unsignalized intersections or intelligent roundabouts with the support of V2X communication and other advanced data process analytics, as well as computing technologies (Annam and Wei 2020).

Source: Adapted from the categories by Guo et al. (2019) and other literature reviews.

CAV cooperation intersections with no signal control

Eco-driving and signal control

Signal-vehicle coupled control (SVCC)

Planning-based signal control

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225

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Actuated Signal Control

Actuated Signal Control Platoon-Based Signal Control

Platoon-Based Signal Control

Horizon of Evolution

ITS-VehicleInfrastructure Control Integration in the Future

Coupled

Driver Guidance

Driver/ Vehicles Feedback

Coupled

Feedforward

Figure 6-1.  Illustration of an evaluation of CAV-based traffic control approaches. Source: Revised diagram on the basis of Figure 1 presented in Guo et al. (2019).

in vehicle control systems. The curves divide three stages of transportation-vehicle integration (from lower left to upper right): the past, the current, and the future (Guo et al. 2019, CVRIA 2016, Barth et al. 2011).

6.2 SELF-ORGANIZED INTELLIGENT ADAPTIVE TRAFFIC CONTROL 6.2.1 Introduction Since the advent of smart or intelligent systems and their incorporation into all fields of work, these systems are exercising great influence on traffic control systems. The amalgamation of the human workforce and intelligent systems, which can be described as a sort of cyber-physical system, is now determining how roadway systems function and regulate traffic (Li et al. 2016). A system or integrated mesh of controls and components needs to be dealt with in a structured manner so as to keep the system running smoothly. All these varying components have a part to play and a time to play that part. The unwavering synchronization of all these elements is the responsibility of the self-organized smart system that manages and controls traffic congestion on any roadway according to the intensity of that congestion. For this reason, these systems are termed adaptive. The inspiration behind these systems lay in the ability of natural bodies to regulate their external systems with the alternating internal factors (Floreano and Mattiussi 2008).

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This self-adjusting system has been recognized as a solution to traffic issues arising in urban setups. The self-adaptive quality of the system allows it to adjust the timing and duration of each traffic light staying on/off depending on the weather conditions, cars passing by, or the number of cars passing through a time window determining the traffic flow of that particular time span. The signal timings are set to depend on any of these factors and the function of these intelligent mechanisms is to determine the most influential factor among the many and modulate the signals in accordance with their findings. These systems do not require any preset road conditions to influence how they react to any set of traffic constraints such as the average speed of the moving vehicle (Lämmer and Helbing 2008). The number of vehicles on roads is increasing rapidly by the day and most traffic controls are now governed by smart systems to cope with this accelerated increase. Major challenges or problems with traffic flow are usually observed at road intersections. That is why the self-adaptive and self-adjusting feature of these systems mostly come in handy at these road junctions, where there is a dire need for the system to adjust signal settings based on dynamic traffic demand. A research into metropolitan cities using this technology was conducted to judge how far this technology had spread and how many of the conventional traffic controls were replaced by these new intelligent systems (Greenman 1998, Salkham et al. 2008). The main focus was on intersections on road networks where it is crucial to put in place the most effective method of traffic management, as shown in Table 6-2. The sensitivity programmed in these systems aims to deal with even the slightest bit of load fluctuations on certain roads. These fluctuations may be shortlived; hence, the system readjusts after every spasm of traffic. The demand for travel has been on an increasing trajectory ever since the industrial revolution and the daily increase in the number of motor vehicles dominating the transport network has only added to the strain on the roads. Social and economic factors play a key role in the infrastructure that needs to be built, which will match the availability of land area and the technological advancements in any particular area. Problems such as land availability and other related issues are a bad consequence of socioeconomic factors and emerge more in metropolitan cities than in rural towns and villages. It has been long desired to use a technological solution to efficiently control traffic inflow and outflow Table 6-2.  Intersections Governed by Traffic Signals Using Self-Adaptive Feature. City New York Dublin Porto

Total intersections (A) 45,000 36,000 2,000

Intersections governed by traffic signals (B)

Ratio (B)/ (A) × 100%

10,800 9,000 328

24% 25% 16%

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from a particular patch of road network in line with controlling the emission profile of these vehicles. Such a technical measure is viewed as a staple in transport systems of any urban area. More complex IT concepts, patterns, and controlling structures are being introduced into the mix to reach that eco-mobility goal. The advancement of computer technology has been a constant companion of the evolution of traffic controls, especially in terms of controlling the parameters affecting real-time flow status and incorporating it at each interval. Traditional methods of applying a timed system regulate its function according to the data formerly provided. With timely adaptive quality via a sensor and computer integration technology, such a system would become smarter and more fit to manage the fluctuations that may arise because of multiple road conditions. There might be a necessity for alternate designs at different times of the day or under different environment conditions that influence traffic demand. A demonstration of the simplest self-adaptive traffic control system is conceptually illustrated by Figure 6-2. The interaction of one vehicle with the other and the size

Figure 6-2.  Self-adaptive traffic control system. Source: Moser et al. (2015) and DOT (2018).

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and load exerted by each individual vehicle are considered by the receptor tower to send signals of its findings to the nearest signal control.

6.2.2  System Elements Multiple adjustments and innovations have been incorporated into this system in accordance with the increased complexity of traffic controls in urban setups. The system is responsible not only for the increase in the range of vehicular loads a road network can bear but also for the reduction in traffic accumulation by providing apt signaling routines (Abdoos et al. 2013, McKenney and White 2013). As highlighted previously, the main focus of these systems is to manage traffic overcrowding at junctions or intersections. Because these are crucial spots in the entire game of traffic regulating systems, these factors and other influencing properties are the points of focus in this section. V2X systems are used to denote vehicle-to-everything systems, a criterion developed to outline the different constraints that allow vehicles to communicate with the moving elements of the traffic system around them. How vehicles behave with respect to the surrounding environment is a crucial factor to include in traffic signal controls to ensure a smooth flow of traffic. This system describes the inflow of information from a vehicle and external factors that impact the motion, direction, and speed of the vehicle. The types include the following: 1. V2V wireless networks to transfer information regarding vehicle-to-vehicle interaction to the nearby cellular tower to be fed to the traffic controller. 2. V2I networks designed to pass information to the signal control on how many interactions and what kind of interaction is the vehicle performing with its surrounding buildings and other structural entities. 3. V2D (vehicle to device) is the direct link of underlying cellular or WLAN systems in coordination with the vehicular movement above the sensors installed in the road network. 4. V2G (vehicle to grid) is referred to as a plug-in electrically driven vehicular entity, for example, battery electric vehicle (BEV). It might make use of this connection by conveying the need for electrical energy or its extra availability, which might aid in saving the extra power supply being given to the vehicle. 5. V2P (vehicle to pedestrian) shows how pedestrian detection systems can be implemented in vehicles, in the infrastructure, or with pedestrians themselves to provide warning to drivers, pedestrians, or both (Qorvo 2018). Figure 6-3 shows a flow diagram of multiple V2X systems interacting with one another and transferring the interactive signals to the nearest receiver via the self-organized system. Currently, the use of these self-adaptive programs is not much widespread as it is supposed to be in showing its revolutionary effects on traffic management. These systems are said to have a deductive mindset of their own to study the varying driving trends and patterns of different types of vehicles to keep the

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Cloud data

RSU

Signal controller

CAV Trajectories Edge data

Figure 6-3.  Conceptual interactions of self-adaptive traffic control systems. Source: Courtesy of Heng Wei.

traffic flow steady with these strategies of evaluating load attributed to their flexibility (Lo and Chow 2002). Not only have traffic controls been digitized, but the vehicles ruling the roads themselves have been noted to possess digital data that can be communicated via cell reception to build a strong vehicle-tostructure interaction. An abundance of real-time data is being made available to these self-adjusting controls, and so far, they have been lagging in terms of processing every constituent of this data to improve the efficiency of results. The technologies work as one individual component, but they have yet to produce a maximized combined result in terms of interpreting all aspects of available data and mastering control over traffic flow and signaling. The field of data-driven controls is still under research, but it is one of the notable areas of focus of this technological innovation.

6.2.3  Optimizing Traffic Signals US DOT (2007) recommended that the cost of installing equipment for traffic lights ranges from $50 to a couple of hundred thousand dollars. The upkeep and maintenance as well as functioning cost ranges from $2,000 to $3,000 a year. The research showed that more than $30 billion needed to be spent to equip all the present intersections in the United States with this technology along with an added $750 million or more as the cost of operation of these traffic signals. These costs indicate that to optimize all existing traffic controls and replace them with smart systems is indeed a tall order. Instead, only a fraction of these costs has been spent on devising an alternative in practice. The existing traffic system has been known to cause unnecessary delays and to stop traffic without there being a need for it. Conventional systems like these function according to predetermined parameters that govern the on/off function of different lights (Wei et al. 2019b). This means that they lack control over changing road conditions or variable traffic flow and end up treating all types of traffic conditions in the same way. Therefore, it has become a necessity to partially replace such systems in all major cities in areas where traffic control

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is becoming more of a concern because of the increasing number of vehicles on the roads. Roughly about 80% of the conventional systems work under one of the following three fixed parameters: 1. Fixed cycle timings—Without any consideration given to multiple traffic loads on different streets, a time constraint is provided to each of the lights as to how long they will continue to work per cycle of all three lights turning on/off. Each part or road connected at any intersection is given a fixed green-light time which is the same for all parts of that intersection. This is one of the oldest governing parameters to be put to use in the functioning of traffic signals. 2. Green splits—This split is usually calculated by the ratio of traffic load in comparison between two parallel streets. The split will be a fraction of time, as long as the green light for that street remains on. 3. Phase of different approaches—This is the phase or sequence of signals on every street to turn green one after the other in a particular set pattern throughout the intersection. All parts/streets of the intersection are integrated to function with one another, and, thus, no clash between two different signals turning green at the same time can occur. All these can lead to uncalled-for red lights and hence delay traffic that could have been in motion if the traffic load had been assessed and the system acted accordingly. In this scenario, only selective locations are chosen to deploy this technology (US DOT 2007). However, in other areas where traffic density may be low at particular times of the day, these signals are suspended as to avoid untimely delays in traffic movement. A unique result emerges from this assessment. One would assume that these traffic systems with the ability to self-optimize are required to cater to heavy traffic movement, whereas in reality, these are needed more in low traffic conditions. Higher traffic loads can also be managed by conventional setups with the signals being turned on/off according to preset parameters. It is only for low traffic loads that there is a need for a system that will sense the change in traffic dynamics and then increase or decrease the signaling time correspondingly. This situation makes for an even costlier enterprise as it increases the cost per vehicle of the installed system if the system is to be put to use wherever the vehicle density is low at road networks, mainly at intersections. The sensorless traffic controls have managed to subsequently increase the delay time of vehicles moving especially in intersections that might be empty or have hardly any traffic congestion in them during most parts of the day. To avoid getting into costly solutions, many countries make use of part-time signals with a sign placed on the signal “part-time” to inform why the signal might not be working sometimes during the day. The system of traffic lights is not only coordinated among the different streets of an intersection but also determines the functioning of multiple intersections that run side by side at short distances from one another. This concept is known as signal offset. Two types of offsets govern the traffic control between two or more

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consecutive intersections. Simultaneous offset, where the signal turns green on all intersections at the same time. This means a car that might get a green light at one intersection will get a red one, most probably, when it reaches the next intersection. Alternate offset, where the signal turns green at one intersection and red at the next one and this cycle continues. This information can be used to draw the conclusion that one car might get a green light at an intersection while there is a red light at the next one. By the time the car reaches the next one, the light, most probably, would have turned green as well ensuring a smooth flow of traffic. Both these methods have proved their worth in terms of controlling the mass movement of vehicles on any intersection.

6.2.4  Self-Adaptive Signal Controls The ability of a system to perform self-modification during its running duration because of the influence and fluctuation of its governing external and internal factors is known as the self-adaptive control phenomenon. These influences and alterations driving the modification are not bound to only system alterations but encompass any environment factors that might be influencing the decisionmaking ability of the system. The method or the extent of self-adaptation depends on many factors among which are the expectation of the consumer, changes in environment deployed to the control system, or the present settings and components of the system itself. These factors decide the lengths to which the system might go to exhibit a change in its properties that are better suited to the changes affecting them. The following are the multiple factors of the system that act to maintain this self-adaptive quality. These factors have a combined effect on its working and also exert their own individual influences on the successful molding of the working conditions. We can consider these factors to be the dictionary of words we can use in describing and associating with these self-adaptive controls. The engineer is the brain behind the development of these models such that the models are not restricted to a set of controls or devices employed for this structure to be built. If the parameters or the structural components vary with each type of system, it becomes increasingly difficult to compare and contrast the working of each system because lesser common grounds exist (Andersson et al. 2008). Typical technologies or systems are summarized as follows.

6.2.4.1 AALONS-D This is a type of real-time-based traffic control system that acts as a decentralized unit. This scheme can fit the purpose of traffic management at a controlled intersection as compared to a set of intersections interlinked with one another. The algorithm provided to the controlling software focuses on figuring out the best possible path to adopt to minimize the delay in vehicle movement as well as eliminate the chances of prolonged signal times hindering the flow. The algorithm is restricted between two limits of the green times attributed to each phase: the upper and the lower limit. It has the capacity to distinguish between repetitive

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patterns of movement from the random ones and create the signaling plans to fit each pattern. This plan differs from other real-time ones because it constitutes an array of simulations performed prior to installation on an isolated intersection. These simulations have proved to be more effective than those systems that operate on fixed cycles involving predetermined paths to take when certain aspects come into contact with them. Each simulation is provided a different combination of factors ranging from extremities to moderate conditions, from saturated load on roadways to unsaturated loads. This system has also been seen to apply its expertise of the network of intersections as well as the network of roads making up one intersection. The results of this simulation showed the system’s capability to design a plan in uniform and nonuniform conditions that created a progression of performance in each intersection.

6.2.4.2  Genetic Algorithms With the advancements in technology governing each aspect of the digital age, we have come into contact with multiple systems using varying techniques and fulfilling the same goals. The same holds true for self-adaptive traffic controlling systems, which use multiple parameters to achieve the construction of a working module that is capable of self-optimizing its functions with changes in its functioning environment. Another method incorporates the real-time perspective into the process of self-adaptation in traffic signals. The governing factor of this type of system is its acyclic operative module. A fixed-duration scheme was developed using TRANSYT-7F, which holds genetic self-optimizing qualities. This scheme was placed in comparison with simulations created on a micro level to perform tests on the algorithm and record its reaction and behavior on the simulated conditions and its ability to optimize its further actions (Lee et al. 2006). The resulting system is composed of three governing parameters: the first is its genetic optimization feature, the second is an internal component with the potential to create desired traffic simulations, and the third is the database that is responsible for managing all information acquired from the different realtime conditions in its environment, grouping of that database, and deciphering patterns and results from it. The testing conditions provided to this experiment did not deal with extremities but incorporated the effect of moderate traffic conditions, along with maximum and minimum ones. These conditions were paired with different modules that control the working of the system. The results recorded through this microsimulation proved this method to be a better choice in smartly managing traffic congestion as opposed to fixed-time controls.

6.2.4.3  Video Imaging This type of traffic control model focuses solely on video information provided to the controller via cameras installed next to traffic signals for optimizing their working process and progress. This system makes use of a technique known as reinforcement learning. When environmental conditions or any external/internal affecting factors exert certain effects on the system, the video information is

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used to decide the further steps to be taken by the system to bring the conditions back to an optimum level. Previous studies and techniques made use of typical parameters such as the length of a vehicular queue or the delay in movement to denote traffic congestion. These factors cannot be appropriately calculated if we consider a machine working on-site. These factors also limit the complexity of the real traffic conditions and fail to draw the exact picture of the state of congestion. Thus, instead of using software and algorithms to make judgments regarding traffic states, this system relies on video imaging and recording to draw conclusions about the traffic conditions. Video frames obtained from an actual intersection road network worked well in portraying the complexity of traffic conditions better than its competitive systems that use simulated, man-built environments that fail to fully grasp the entanglement of each small component influencing the road traffic scenario.

6.2.4.4  Sustainable Controls This technique focuses on stochastic optimizing of the system such that it creates a functioning space for the system that leads to protection of the environment. In this process, the delay time and regularity of stops is managed keeping environmental impacts in mind. The plan for movement of vehicles through a road network must be integrated so as to limit fuel consumption or the excess amounts of fuel combustion at one point on the road causing massive amounts of smoke to be released by the vehicle at that particular spot. The idea to conserve the environment and design a working system that exerts minimum stress on the surroundings is indeed something that needs more work put into it. So far, the development of this idea has been partial, and the idea has not reached its fruition. The system is said to use microsimulations with the data provided related to emissions of smoke and fuel consumption of different levels to drive the selfadaptive quality of the system. With moderate replacement of delays and stops integrated with one another, the system has proved beneficial for the environment in the simulations being tested.

6.2.5  Signal-Free Autonomous Intersection Control Technically, the application of autonomous vehicles makes it possible to eliminate traditional traffic signals from intersections and, hence, has the potential to maximize intersection capacity, significantly enhancing vehicle mobility at intersections. From a safety perspective, considering that 90% of road crashes are attributed to driver errors (NHTSA 2012), the application of autonomous vehicles is potentially effective in reducing intersection-related crashes. Although potential benefits have been seen, how to take full advantage of autonomous vehicles and how to maximize the operational performance of autonomous vehicles at intersections is of great interest to transportation authorities. To date, most field tests for autonomous vehicles were restricted to highway segments. Although autonomous vehicle traffic control at intersections has been studied (Dresner and Stone 2004), its practical implementation is difficult due to

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the presence of more conflicts at intersections than those at highway segments. For managing autonomous vehicles at intersections, the right of way is usually controlled by an intersection central controller through V2I communications (Dresner and Stone 2004) or is determined through negotiations between vehicles via V2V communications (Ball and Dulay 2010). Basically, there are two types of control strategies for signal-free autonomous intersection control: centralized control and decentralized control. Recently, a new concept of intelligent roundabout has been proposed (Wei and Kashyap 2019, Annam and Wei 2020).

6.2.5.1  Centralized Intersection Traffic Control Centralized intersection traffic control features a central intersection controller that regulates the entire intersection. Vehicles communicate only with the central controller to get passing instructions. Dresner and Stone (2004) were the first to introduce a reservation-based multiagent system, named Autonomous Intersection Management (AIM). In the AIM system, the intersection is divided into a grid of n by n tiles. When a vehicle approaches the intersection, the driver (agent) communicates to the intersection center (manager) to pass the intersection. Figure 6-4 illustrates the conceptual architecture of a centralized intersection traffic control system. The driver agent sends requests to the intersection manager to reserve the intersection for certain time spaces, which allows traversing the intersection, based on the vehicle’s estimated arrival and departure time. The intersection manager checks what and how much resource (tiles) will be occupied by the requesting vehicle and identifies whether these requested tiles have already been reserved by other vehicles. If the tiles are already reserved, the request will be rejected. Otherwise, the reservation will be made. The vehicle agent is notified by the intersection manager about whether the request is approved or rejected. The instruction of travel will be sent to the vehicle agent by the intersection manager with the approval notice. In the prototype version of Dresner and Stone’s (2004) system, left and right turns were not allowed, and all vehicles traveled at the same speed. Dresner and Stone validated their algorithm using a simulation tool that they developed, in Traffic Control Center (Manage) Intersection

Intersection

Agent

Figure 6-4.  Concept of a centralized intersection traffic control system. Source: Designed based on the relevant concepts presented in Dresner and Stone (2004).

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which they defined certain lane-change and car-following behaviors, signal and stop control operations for comparison purposes, and methods for estimating throughput volume and delay. A second version of their system was much more comprehensive in terms of allowing turns and acceleration in the intersection (Dresner and Stone 2005a, b). The improved system was evaluated in their own simulation environment in comparison with stop-control and signal-control scenarios. The impact of restricting left and right turns being made from designated lanes rather than from any lanes was also analyzed. Theoretically, in the reservation-based system, the restriction was not necessary. Relief from the restriction was supposed to provide more flexibility to drivers. However, the results showed that the scenario under the restricted turn conditions resulted in lesser delay than the scenario of allowing turns from any lane. Dresner and Stone (2008a, b) further stated that the results might be misleading, because the delays incurred by vehicles from the lane-change maneuvers can be longer. In the later versions of the AIM, safety issues were addressed by adding a safety net in the system (Dresner and Stone 2008b). Batch processing of reservation requests was also realized to address the starvation issue because of the unbalanced traffic demands on the mainline and the side road (Shahidi et al. 2011). The unbalanced traffic demands issues were also discussed in other researchers’ work. Li et al. (2013, 2015) found that if the first-come-first-served (FCFS) strategy is appropriately applied when coupled with certain lane configuration designs, the effect of unbalanced traffic demand will be minimal in terms of delay and emission. The AIM was finally tested in a mixed reality platform (Quinlan et al. 2010). Most of Stone’s studies resulted in an exceptionally low delay ( vc & vi (t + ΔT ) ≤ vc & (t , yi (t )) ∈ cell(k ,l )}



(6-2)

where vi(t) = Speed of vehicle i at time t, vc = Constant speed threshold (e.g., 3 mph) as the vehicle is stopped, ΔT = Sampling interval, and yi(t) = Vehicle’s position i at time t.

6.2.6.3  Deceleration Points Rearranged in Descending Order For all the times that satisfy Equation (6-2), the set {t (di ) } can now be rearranged defining a new set of times

U = {t (di ) } sothat y(1) (t (d1) ) ≥ y(2) (t(d2) ) ≥ … y( s ) (t(ds ) )

(6-3)

The average queue length per vehicle between the vehicles is taken as the filtering condition. The set of deceleration points that are within the queue of interest is given by F.

F = {t (di ) | y(i ) (t (di ) − y(i +1) (t (di +1) )|≤ average queue length per vehicle}

(6-4)

Then, the queue length is estimated as

L ML =| y l − y(nA) (t (dnA) )|

(6-5)

where y l is the location of the downstream intersection of link l, and y(nA) is the location of the last vehicle in queue. Queue size is defined as the number of vehicles resulting from unserved demand on the current cycle (at the end of the effective green phase from the current cycle and the beginning of the effective red phase in the next cycle).

Q(t i ) = Q(t i−1 ) + A(t i ) − D(t i )

(6-6)

where Q(ti) = Number of vehicles in queue at the end of the current cycle; Q(ti−1) = Number of vehicles in queue from the unserved demand on the previous cycle;

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A(ti) = Number of vehicles that arrive at the intersection during the analysis period; D(ti) = Number of vehicles that depart from the intersection during the analysis period.

6.2.6.4  Optimization Model Formulation In this research, we have developed a cooperative coordinated adaptive traffic signal optimization algorithm for arterial progression. This algorithm is incorporated into the Wei-Zone and is composed of four step processes as shown in Figure 6-9. In the first step, depending on the arrival rate of vehicles, optimum cycle length, Copt, and effective green ratio, gi, are determined at different intersection levels along the corridor. The output from the first step is used as an input for the second step. In the second step, the range of cycle length to be tested for the corridor is defined based on the optimum cycle obtained from the first step. For each enumeration in the second step, the cycle length is kept constant for all the intersections along the corridor. The effective green ratios determined for each phase at the intersections are used to determine the effective green time. The

Figure 6-9.  Coordinated adaptive traffic signal optimization for arterial progression.

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third step is used to develop the mobility versus offset relationship. In the third step, offset (∅i) and total delay (TDi) and offset and total stop (TSi) values are used as inputs to formulate an equation of TDi and TSi as a function of offset and cycle length. In the fourth step, dynamic programming (DP) is used to obtain the optimal cycle length, optimal offset, and optimal green time for each phase along multiple intersections in the corridor. The overall objective is to minimize the total equivalent cost (TCi), which is a function of total delay (TDi), total stops (TSi), total emissions (EMi), and fuel consumption (FCi). At the arterial level, the objective of our optimization problem is to minimize the total costs (or maximize total benefits) for the coordinated lane group in terms of dollar value by considering both mobility and environmental factors. This is achieved by selecting the optimal value of offset at different intersections and maximizing the bandwidth for the coordinated group. The coordinated lane group in the model is the one having critical lane volume. The model also ensures that green time is assigned for the uncoordinated lane group based on the flow ratio. For a single objective function, an optimization problem can be formulated with its objective function (total cost) as a linear combination of total delay, total fuel consumption, and the total emission that consists of three emissions, namely, carbon monoxide (CO), nitrogen oxide (NOx), and volatile oxygen compounds (VOC), denoted as ECO, ENOx, EVOC, respectively, and given by

Min TC = wT × TD + w Fuel × Fuel(TD,TS) +

∑ w × EM (TD,TS) j

j

j

Subject to: COPTmin ≤ C ≤ COPTmax , g i = g e ×

(6-7)

yci yc

where COPTmin and COPTmax are the minimum and maximum optimum cycle lengths obtained from different intersections along the corridor. The optimal cycle length for the corridor is determined in an enumerative way from the range of optimum cycle lengths obtained at the intersection level.

6.2.6.5  Dynamic Programming Procedure for Offset Optimization Dynamic programming (DP) is a method/procedure for solving a complex problem by breaking it down into a collection of simpler subproblems. It is applicable to problems exhibiting the properties of overlapping subproblems and optimal substructures. DP has two major advantages: (a) it takes far less time than the naïve methods (e.g., enumerative method) that do not take advantage of the subproblem overlap (like depth-first search), and (b) it can find the optimal solution, thereby outperforming some alternative methods such as greedy algorithm, which picks the locally optimal choice at each branch/stage and does not guarantee an optimal solution. Recent research studies have demonstrated the effectiveness of using the DP models, an offshoot of the combination method, for signal offset optimization by using link performance functions (Day and Bullock 2011; Gartner and Rahul 2009, 2013; Li et al. 2014).

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Link-1

Node-1

Link-2

Node-2

Link-3

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Figure 6-10.  An illustration of offsets and coordinated intersections at the arterial. The DP procedure for offset optimization is described by giving an example of an arterial road with four coordinated intersections, where the coordinated signalized intersections are denoted as nodes 1, 2, 3, and 4 as shown in Figure 6-10. Correspondingly, the arterial links among these nodes are defined as link = 2, 3, and 4. Links 1 and 5 are inbound and outbound flows for node 1 and node 4, respectively. For simplicity, only the coordinated link is shown in Figure 6-10, which is considered for optimizing the offsets. The set of offsets for different nodes is defined as ∅ = [∅1 , ∅2 , ∅3 ,…] . The coordinated intersections can be extended to more than four nodes on a case-by-case basis. For solving the four nodes problem with three coordinated links in Figure 6-11, the computation load for the enumerative method (C1 = K1 × K 2 × K3 × 3) (e.g., if K1 = K2 = K3 = 10, then C1 = 3,000) is much higher than the computation load for dynamic programming (C 2 = K1 + K1 × K 2 + K 2 × K 3) (e.g., if K1 = K 2 = K3 = 10, then C2 = 210). With the increases in coordinated nodes and increases in possible values of offsets (i.e., the increases of K1, K 2 and K3), DP will show more noticeable savings in computation load, which are especially superior for solving the large-scale optimization problem. This research does not restrict itself to implementing the adaptive signal control strategy along isolated intersections. Moreover, it aims at exploring the viability across multiple intersections by implementing the CCACSTO algorithm along a

Offset

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2

2

Offset

Offset

Offset

Ki-1

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Figure 6-11.  An illustration of dynamic programming of offsets.

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corridor. At the corridor level with multiple signalized intersections, mobility– offset relationships are extended to the entire intersection spacing (i.e., the link between two adjacent intersections) in a coordinated direction. Then, based on the mobility–offset relationship considering the platoon dispersion of each link, the optimization problem was formulized with the intersection offsets as decision variables, given the effective green ratios determined at the intersection level. The dynamic programming procedure was adopted to minimize the total costs of delay and emissions in an arterial signal optimization. The optimal common cycle length in the corridor was investigated in an enumerative way with a reasonable range determined at the intersection level. The CCACSTO algorithm outperformed the Transyt-7F by reducing the total delay (TD) by 31.03% and total stops by 16.04%. Similarly, the CCACSTO algorithm reduced the total delay (TD) by 23.07% and total stops by 12.96%. To make the study results produced by the CCASCTO algorithm more robust, this research evaluated the algorithm at four different penetration rates of CAVs using the microsimulation software VISSIM. The simulation test results show that the average vehicle delay and queue length with the CCACSTO algorithm reduced by 46.04% and 56.15%, respectively, under a 50% penetration rate of CAVs.

6.3 SAFE INTERACTIONS OF PEDESTRIANS/CYCLISTS WITH AUTOMATED VEHICLES 6.3.1 Background Currently, most traditional roads in the United States are inconvenient at best and dangerous at worst for walkers and cyclists, because they were historically designed around the needs of the vehicles and drivers. Källhammer et al.’s (2017) study found out that drivers’ view of the dangers associated with pedestrians is largely associated with the possibility that they might move into the vehicle’s path, even when the vehicle is not on a collision course with the pedestrians. Time to arrival (TTA) can be quantitatively connected with rating an alert need and alert timing. Because the computing system for an AV is envisioned to be fully in charge of the split-second road reactions that human drivers so routinely flub, the AVs stand to make streets a safer world where “most cars are driving themselves, pedestrians could reign supreme” (Bliss 2016). In this hypothesis, we expect that pedestrians’ incentives around safety will change, especially in making the pedestrians and cyclists feel fearless while being fully confident of the AV to be “vigilant” enough to avoid collision when they simply cross any at-grade intersection or midblock. In other words, pedestrians and cyclists would be able to safely cross the intersection and ride on streets, even in case of no special crosswalk marks or bike lanes, because they will become confident that the AVs will not touch them (Bliss 2016, Millard-Ball 2016).

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In the context of smart and connected community development in the future, we expect that AVs will facilitate a shift toward pedestrian/cyclist-oriented urban or community neighborhoods, whereas the adoption of the “slow down” AV strategy on the urban traffic network will not act as a disadvantage, which is currently the concern of some people though. We have to confront a long transition period as they are gradually entering the transportation infrastructural systems, whereas fully and partly automated vehicles coexist with human-driven vehicles sharing the roads. Such an AV evolution process also provides an opportunity for transportation professionals to rethink and/or revamp the planning and engineering design of our transportation infrastructural systems to integrate eco-mobility advantages by solidifying environment-friendly interactions between AVs and vulnerable users (i.e., pedestrians and cyclists and people with disabilities). To this end, it is, hence, imperative to identify ways for a better understanding of the nature of people–AV interactions. It is worth noting that in today’s roadway systems, the interactions between human-driven vehicles and cyclists/pedestrians are inherently about human (driver)-to-human (nonmotorized user) interactions. In the AV mobility environment, however, such an interaction should be directed toward the robot (AV)–human (nonmotorized user) interactions. Vissers et al.’s (2016) research identified critical elements in vehicle–pedestrian/cyclist interactions in a future partly or fully automated era and provided an overview of the current knowledge, theoretically and empirically, about the interaction of pedestrians and cyclists with (partly) automated vehicles. They also proposed their understanding how to ensure AVs to accurately adjust their behavior toward safe interactions between AVs and pedestrians/cyclists. Furthermore, they addressed an infrastructurerelated need to be reflected in the priority regulations and in the systematic and engineering design of transportation infrastructures such as intersections, roundabouts, and pedestrian crossings (Vissers et al. 2016, Parkin et al. 2016).

6.3.2  General Considerations of Transition Effect Theoretically, the interactions between road users are supposed to follow a comprehensive set of formal driving rules and traffic regulations, which, in general, can be described as the IF–THEN type of algorithm (Vissers et  al. 2016, Wickens et al. 2004). However, many reasons can be cited for this type of algorithm to be not so straightforward in practice. These reasons have to do with the fact that road users are no machines and no robots. In reality, however, road users who are humans rather than machines or robots, may make errors, resulting in violations of rules and regulations, which may be influenced by “what they know, want, believe and expect” (Vissers et al. 2016). In a transition situation with a mixture of partly and fully automated vehicles and human-driven vehicles, there may exist unreliable expectations of pedestrians and cyclists in distinguishing different types of vehicles and their intent behaviors from each of these vehicles. Those potential problems may be influenced by multiple factors associated with road users; for example, gender, age such as older and young people, varied response

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behaviors such as reaction, hesitation on crossing action (e.g., accepted gap), and an understanding of vehicle “behavior” influencing safety versus speed (a capability of response to avoid collision). In the case of AV–pedestrian/cyclist interactions, road users’ expectations on or faith in AVs’ adequate response might increase as more and more “vigilant” vehicles are governed by automation technologies with reliable in-vehicle warning systems (Vissers et al. 2016). In a transition period, however, safety can still be compromised or can be a matter of concern if road users are overreliant on the response of AVs (e.g., believing that these partly or nonautomated vehicles will automatically stop as they are approaching). In addition to the rules and regulations that theoretically govern road users’ decision-making in interactions, some road users apply a kind of nonverbal communication to exchange their intentions (Keferböck and Riener 2015, Kitazaki and Myrhe 2015, Malmsten Lundgren et al. 2017). Typical examples of nonverbal communication include the use of blinker and light signals and position and speed changes of vehicles. In particular, behavioral cues (e.g., eye contact, nodding, and hand gestures) are of critical importance to predict attention for and awareness of one another (Walker 2005, Kitazaki and Myrhe 2015, Malmsten Lundgren et al. 2017, Rakonitorainy et al. 2008, Sucha 2014, Vissers et al. 2016).

6.3.3  Pedestrian and Cyclist Reactions to Automated Vehicles Several studies have reported some findings regarding the attitude of pedestrians and cyclists toward their possible interactions with AVs. Examples of these findings are listed as follows: • Pedestrians or cyclists are remaining a conservative expectation to future AV’s reaction: Cyclists do not expect to be noted better by an AV than by a human-driven vehicle. Pedestrians or cyclists are also not sure whether AVs will stop for them as compared with human-driven vehicles (Hagenzieker et al. 2020). • A stated-preference survey indicated that pedestrians and cyclists strongly prefer facilities (e.g., cycling paths and pavements) separated from AV traffic, especially as the volume of AV traffic and their own speeds have increased over the years (Blau 2015). • Pedestrians are expected to safely cross the street when drivers are “driving” automated vehicles (i.e., using laptop and smartphones, reading a newspaper, looking at a computer screen, having a rest, or even sleeping) (Malmsten Lundgren et al. 2017). • Interview and questionnaire study of the first self-driving pod on Dutch roads (i.e., the WEpod) indicated mixed findings regarding the pedestrians and cyclists’ confidence in, and the perceived safety of, the self-driving pod (Shapiro 2016). Some feel somewhat safer when sharing the road with the self-driving pod, and some others feel less safe when interacting with the WEpod compared with human-driven vehicles at unsignalized intersections (Rodriguez et al. 2016, Parkin et al. 2016).

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• Parkin et al. (2016) proposed a conceptual AV Use Scenario, Shared Space, an urban design approach to minimize the separation between different types of users to enhance priority for pedestrians and cyclists, which is often absent from much of the public highways. This design aims to reduce motor traffic speeds, while introducing more subtle and naturalistic forms of speed control through different surface colors and textures and roadside features, rather than traditional measures such as curbs, markings, traffic signs, and traffic lights. AVs will navigate an environment that is less well defined and regulated than a typical urban highway network. AVs will be expected to interact with every type of user on an equal basis with no defined priority. AVs will interact with other AVs, human drivers, pedestrians, and cyclists. • Parkin et al. (2016) further proposal another Scenario of fully segregated AV network, wherein AVs are completely segregated from other road users and operate within their own system. AVs will interact only with other AVs and the infrastructure of the network. • Wei (2018) proposed the concept of a synthetic envision in which, at an early stage, CAVs will be first borne by specifically managed or dedicated lanes that are equipped with the required IoT-based infrastructures for CAVs. These types of lanes will further form a backbone network that bears only CAVs. Such a backbone network, termed Managed Infrastructural Network for Demand of CAV (MIND-CAV), can be extendable versus increasing CAV penetration rate and investment status.

6.3.4 Communication in Interactions between Roader Users and Automated Vehicles The communication between automated vehicles and vulnerable road users such as pedestrians and cyclists remains a critical challenge. The current communication strategies involving human drivers will lose their functionality when interacting with AVs (Ackermann et al. 2019). Ackermann et al.’s (2019) study tested specific parameters (deceleration rate, vehicle speed, onset of deceleration, vehicle size, and daylight) within two experimental, video-based simulations concerning detection performance. The results showed significant shorter reaction times for higher deceleration rates and lower speeds. Interactions among the parameters greatly influence the testing results versus vehicle size and onset of deceleration. The study results suggest that solidifying communication between AVs and pedestrians by applying smooth and early decelerations while incorporating vehicle speeds that vary with vehicle sizes will be significant. At the same time, it is also a challenge to enhance AVs’ capability to predict the behavioral intent of cyclists and pedestrians within a certain distance. Some studies have focused on the effects of different means and strategies of AVs to communicate with pedestrians and cyclists. One of the techniques includes using laser projection to detect a crossing pedestrian and then give way. Other techniques build on current informal communications such as a smile that lights up on a display in front of the car so as to enable AVs to detect a crossing

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pedestrian/cyclist and perceive their head movements and gaze directions toward the car (Vissers et al. 2016). In general, the communication technologies aim to allow pedestrians and cyclists to communicate with AVs in the same way in the traditional roadway system. However, it is still not clear to what extent these techniques have been tested in practice (Vissers et al. 2016).

6.3.5  Automated Vehicle Communication with Pedestrians The state of the vehicle described by deceleration, acceleration, or distance to crosswalk can be used to realize communication with pedestrians to show the intention of the AVs (Risto et al. 2017; Zimmermann and Wettach 2017). To achieve an explicit form of communication, different modalities such as visual, audio, or radio signals may be used to convey the status of the vehicle, surrounding, intention, or advisory information for other road users. Consequently, other road users can adjust their behaviors accordingly. Table 6-3 categorizes AV communications with pedestrians, based on the information provided from Rasouli and Tsotsos’s summary (2020). Figure 6-12 illustrates several examples of an AV’s advisory display for crossing.

6.4  ECO-DRIVING AND TRAFFIC CONTROL It has been long recognized that transportation is seen to have a direct, negative impact on the environmental conditions, mainly the degradation of the quality of the air we breathe. Many technological efforts that might help in achieving smart driving practices have been undertaken through the design of laboratory simulations as well as the implementation of most of these designs in real-life situations to aid in the process of obtaining an eco-friendly traffic control system. The addition of carbon in the surrounding air is influenced by a number of factors, which are discussed in the sections that follow. Some appropriate solutions aiming to aid in the eco-control system have been targeted by manufacturing most companies. Complex software is being programmed and installed not only in the traffic light management equipment but is also being placed inside the newly manufactured motor vehicles. The execution of the concept of smart engines is being increasingly explored. This is a promising new branch of signaling control exerting a positive influence on simulations as well as real-time scenarios.

6.4.1  Eco-Signal Control Introducing innovative ways to make newer trends less destructive for the environment has been one of the main considerations to provide ecological solutions to traffic management programs. The companies involved in this task are now being held responsible to help in the struggle toward responsible ecofriendly practices when advocating new ideas and planning the execution of these ideas. These practices include the use of sustainable resources along with a reduction of possible harmful side effects on the environment. Manufacturing

Wearable sensors with AVs can transmit various warning signals indicating the intention of the vehicle drivers to pedestrians [M. S. Gordon, J. R. Kozloski, A. Kundu, P. K. Malkin, and C. A. Pickover. 2016. “Automated control of interactions between self-driving vehicles and pedestrians.” US Patent 9,483,948 (2016)] and guide other road users on where and when it is safe to cross (Clamann et al. 2017). • As illustrated in Figure 6-12, an AV Pedestrians Sensor explicitly displays written messages suggesting the next course of action (Daimler 2014) or projects zebra crossing lines on the ground indicating where and when to cross the street (Daimler 2017).

AV pedestrians sensor

Explicit signals can provide advisory information to other road users on what action or a course of actions that a vehicle driver intends to take. • Different patterns of lighting an array of LEDs are designed to indicate whether a car is yielding or is about to move (Lagstrom and Lundgren 2015). • Road-illuminating directional indicator has been designed in Mitsubishi Electric to project large, easy-to-understand animated illuminations on road surfaces, which indicates the intention of the vehicle’s forward driving. Other indicators such as door opening and reversing behaviors can also be indicated (Mitsubishi Electric 2015).

Guiding signals

AV is able to share information with other road users to help other vehicles act accordingly. • An LED array at a vehicle’s rear end is designed to display indicating a pedestrian crossing in front of the car (Daimler 2017, Rasouli and Tsotsos 2020).

AV sharing information

Eye contact with vehicle drivers is a naturally applied communication way for pedestrians to ensure that they are visible to the drivers when the pedestrians intend to transmit a signal (e.g., by slowing down) to acknowledge their presence. • Human-like features (i.e., moving eyes on AVs) have been used in some studies to detect and follow the gaze of pedestrians in simulating the feeling of making eye contact (Chang et al. 2017, Pennycooke 2012, Rasouli and Tsotsos 2020). Mirnig et al. (2017) used a humanoid robot in the driver’s seat to perform human-like gestures or body movements to communicate with pedestrians. • AutonoMI is an exemplary eye contact communication system where the part of the LED array closest to the pedestrian lights recognizes and acknowledges a pedestrian who is crossing (Graziano 2014, Rasouli and Tsotsos 2020).

Eye contact communication

Table 6-3.  Descriptions of Examples of AV Communications with Pedestrians. 252 Disruptive Emerging Transportation Technologies

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Figure 6-12.  Examples of AV communications with pedestrians. companies are involved in the active production of such components of the system or the entire system design and assembly by themselves to be used by the respective authorities. The efficiency of these tasks is not compromised at the risk of making a system eco-friendly, which limits the extent to which this filter can be incorporated into the systems. In the previous section, we focused on the vehicle, on the roads, interacting with multiple elements in its environment. Its relationships with other vehicles and infrastructure are also considered along with the data being transferred to from vehicle-to-transmission grids. To this end, the ecological solutions make use of vehicle–infrastructure and vehicle–vehicle connections. In this section, we outline the possibilities of such a system, the extent of their functions, and the influence of statistical data collected from multiple sources (Vreeswijk et  al. 2010). With each passing year, the ratio of the road area available versus the number of cars on it is accelerating at an unstoppable rate, which has led to a further burden on road networks, in turn, leading to persistent traffic congestion, delays, unnecessary stops, and other managementbased inefficiencies. The pollutants being emitted into the air constitute nearly 40% of the total carbon dioxide present in the breathable air as well as a mix of almost seventy percent of other air pollutants (EC 2007). Most of these techniques primarily focus on smart-driving and fix the responsibility of limiting harmful emissions on the driver rather than the vehicle itself. The driver’s ability to be agile in his driving practices is the key here, because more than 19% of all fuel wastage is caused by delayed deceleration, leading to a concentrated quantity of emissions occurring at one spot. Other contributions to fuel wastage are owing to slow flow of traffic, whereas more than 10% of fuel depredation is the result of incompetent traffic lights and overspeeding (Vreeswijk et al. 2010). Eco-driving control of traffic is an area that is being explored by many companies and authorities to provide suitable solutions for traffic-related problems

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Figure 6-13.  eCoMove solutions for fuel wastages. as well as giving the public fair access to these solutions. Figure 6-13 shows the types of fuel wastages that occur on roads today and how projects such as eCoMove, striving toward building an eco-friendlier system of road management, will minimize these wastages and create an output out of the vehicles that are much less harsh for the environment. Figure 6-13 also shows the breakdown of the management areas by eCoMove to diminish fuel wastage and emissions by altering the existing driving behaviors. Transport is one of the leading causes of air pollution (Smit et  al. 2009). Fuel combustion is directly proportional to the rate of unhealthy emission of smoke composed of harmful gases in mass quantities; thus, maintaining a system that controls fuel usage will simultaneously have an eco-beneficial effect on the environment. Fuel consumption varies from vehicle to vehicle depending on the size of the engine, mileage of the motor, and road traffic conditions (Kawabe et al. 2011). An example is the changing of light from green to red right before a car reaches a certain intersection, causing it to stop at a more sudden rate, which leads to a forceful usage of extra fuel and emission of extra smoke from the rear end. This might also shift the focus on another reason for fuel wastage, which is the vigilance of the driver. This highlights the responsibility of the driver to predict what might happen along the way. This anticipative behavior may aid in the least use of braking and accelerating movement of the vehicle. This level of complexity can be handled by the driver, although more information might not be appropriately comprehensible by the human mind. The exact combustion rate of fuel or the engine dynamics with each activity is not something that can be easily guessed by the driver himself. Hence came the idea of introducing smart cars to the world. Cars are a source of

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emission, and some factors affect the quantity and intensity of these emissions, including the following: • Driving state—is the car parked, moving, at what speed is it moving in driving mode; • Age and health of the engine; • Is the engine in need of periodic checks or maintenance; • Fuel components and type; • Location controls air conditions’ general density of gases in the existing surroundings

Petrol

Diesel

50 km/h

Figure 6-14.  Fuel consumption versus types of fuel and speed (km/h).

Automatic A/C

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The other main factor that impacts emissions is the weather conditions. Not only do such conditions make it harder to drive smoothly during rain or snow, road congestion increases further. Along with these, the use of heating and cooling equipment in cars is directly influenced by weather conditions. These heating and cooling mechanisms cause their respective types of running emissions from cars. Figure 6-14 illustrates the consumption of fuel versus speed, along with the emission of carbon dioxide from cars in Europe. Data are collected from laboratory simulations versus those taken from real-life functioning of car engines (Fontaras et al. 2017). Road transport is directly linked to any activity in a certain region that requires its use. The effects of emissions and intensity of emissions also vary from region to region. Figure 6-15 illustrates the mechanism of eco-signal control through a flow diagram of the expulsion of smoke and its degrading effects on the environment (Morawska et al. 2009).

100 km/h

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Environment

Land usage

Traffic activity, fleet composition and performance

Emission and fuel consumption

Dispersion

Exposure

Health Effects

Economic Effects

Structure

Figure 6-15.  Mechanism of eco-signal control. This section consists of a various array of methods being applied to achieve the goal of a sustainable and environmentally beneficial traffic control system. These methods differ in their approach and the type of equipment used in each of them, but at their core, all attempt to reach the same destination. Reduction of emissions is the main driving force of any method. In Europe, the amount of carbon dioxide being expelled from vehicles continues to rise, with cars constituting more than seventy percent of this amount. Although these numbers were seen to drop in 2015, they are 20% higher than what was recorded in the 1990s (Zacharof et al. 2017, 2021). With carbon dioxide being a major greenhouse gas contributor the main focus of all the modern systems being designed is the control of carbon dioxide emissions and their elimination.

6.4.2 Eco-Driving Control with Connected and Automated Vehicle Technologies 6.4.2.1  Eco-Driving Control Using Uncertain Signal Timing With the rise in automation in the manufacturing of motor vehicles becoming more and more common, modern systems have helped in achieving a less degrading effect of emissions on the environment. With the use of CAVs, we can control V2I interaction to create a smart planning system wherein the velocity-to-combustion ratio can be calculated to monitor and manage their fuel consumption (Krupitzer et al. 2018). This system should be geared toward having the best possible velocity profile, which can be used for smart fuel consumption, thus eliminating the unnecessary expulsion of carbon dioxide from cars. This is one theoretical example of a driver-assistance system (Barth et al. 2011). Another form of combustion control is the dynamic software program that aims at optimizing the internal combustion engines of cars to maximize fuel usage with the varying requirements presented by interlinked influencing road factors (Mensing et al. 2013). Through transmission towers, information is sent about the details of signal timing and the phase difference between parts of an intersection. To this end, smart systems with velocity calculations are required to react accordingly. So far, this idea has not been put into practice to this extent because a vast level of system penetration is required at the hands of

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Figure 6-16.  Uncertainties of smart systems. Source: Mensing et al. (2013).

these eco-control systems when they come into contact with V2I connections. Multiple uncertainties exist when it comes to implementation, such as the number of cars waiting in queues and the extent of congestion on each road making up the intersection. Figure 6-16 shows the types of uncertainties that exist in regard to the application of smooth calculative processes in these smart systems. These uncertainties can be eliminated by the systems themselves by calculating the location of the next signal and the road traffic congestion to decide the proper route to take to avoid huge traffic. The calculation processes are too complex to be understood and employed as a norm in intersections, and, thus, the developers of this technology are working toward simplifying these processes and their ease of use for the benefit of vehicle drivers. The systems use an algorithm that decides whether a car is supposed to stop at an upcoming red light. This decision is made after knowing the signaling phase and timing, and, thus, it is done even before the light turns red.

6.4.2.2  Eco-Driving Using V2X-Driven Signal Control Eco-driving finds its link with V2X models to make electrically operated transport machinery more fuel efficient. An optimized model should deal with two factors: the operating process of these vehicles and the saved energy, which are calculated from simulations created from real-time references. This model can be applicable to vehicles of both types: carbon-based emission systems and non-carbonbased emission machinery. The strategy for controlling velocities is divided into multiple stages of travel such as stopping times, the need to make stops, traffic congestion, signal duration, and consecutive intersections interlinked with one another causing delays and hindering the smooth flow of traffic. The simulation makes use of all these real-time issues that one might encounter when on the road. Velocity-time control is designed to assist in the working of carbon-based vehicles, whereas non-carbon-operated vehicles make use of predesigned smart driver technology to follow the set standards that qualify the driving mode as eco-driving.

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A merging Genetic Algorithm (GA) can be formulated when multiple influencing factors exist in the calculations to eliminate the overcrowding and complexity of the system and perform simple calculations with high quality (Wang et al. 2018). The simulation considers four different road conditions to form one intersection and then the results are obtained. With the emerging connected vehicles technology, a new concept of “dynamic eco-driving” is developing. Dynamic eco-driving takes advantage of dynamic real-time traffic sensors and infrastructure information, based on which vehicles work with infrastructure to achieve the optimal results of reducing fuel consumptions and emissions. Dynamic eco-driving has been tested both in simulation and in the field. For studies conducted in a simulation environment, the basic concept is to use algorithms for modifying vehicle speed and acceleration to minimize fuel consumption and emissions. Some models are created in an arterial corridor. Barth et  al. (2011) developed a dynamic eco-driving system for signalized intersections with an arterial velocity planning algorithm. They analyzed the relationship between average speed and fuel consumption, identifying that it is best to maintain a steady-state velocity at a mid-range speed (40 to 60 mi/h). Therefore, four different scenarios were created based on real-life situations, following which an acceleration and deceleration profile design with the arterial velocity planning algorithm was implemented. The results demonstrated that passenger cars with the dynamic eco-system showed significant reduction in carbon dioxide emissions and fuel consumption and improvements in travel time. Xia et al. (2013) also considered the concept of dynamic eco-driving in an arterial corridor. The main purpose of the algorithm implemented is to make vehicles travel with adjusted velocity to achieve the goal of minimizing fuel consumption and emissions. The findings reveal that maximum fuel saving and emission reduction occur during medium traffic volumes (300 vehicles per hour per lane) and low penetration rates of eco-driving vehicles (5%, 10%). The dynamic eco-driving velocity plan can help individual vehicles reduce fuel consumption and carbon dioxide emissions by approximately 10% to 15%. Ma et  al. (2017) studied the dynamic eco-driving model on a simple intersection and a single-lane highway. By deploying Vehicles equipped with Cooperative Adaptive Cruise Control (CACC) will be able to maintain a close distance with the ones ahead and operate in a more coordinated way. Their study revealed that CACC can help vehicles reduce emissions in light traffic conditions and enable them to operate under intersections without traffic signals so that drivers can develop eco-driving behaviors to reduce the number of stopping vehicles. Hu et al. (2016) developed several terrain profiles to study hybrid vehicle activities. The optimization methods were used to achieve ecological vehicle operations in a simulation environment. The results showed that fuel savings ranged from 5% to 8.9% on mild slopes and 15.7% to 18.9% on steep slopes. Jiang et al. (2017) coopted CAVs into the dynamic eco-driving system. They used Pontryagin’s Minimum Principle to optimize the speed profiles of CAVs, which can help optimize the entire traffic flow and increase fuel efficiency at a signalized intersection. They discovered that the variations occurring during

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simulation pertain to the market penetration rate of CAVs and volume/capacity (v/c) ratio. The benefits of the new eco-driving system will be significant if the market penetration rate of CAVs reach 40% and fuel consumption and carbon dioxide reduce to a range of 2.02% to 58.01% and 1.97% to 33.26%, respectively, from a v/c ratio of 0.5 to 1.2. Zhao et al. (2015) developed an eco-driving feedback system using a driving simulator as a part of eco-driving training. The support system could provide both dynamic and static feedback to improve driving behavior. Dynamic feedback includes voice prompt of non-eco-driving behavior and real-time carbon dioxide emissions. After the training, a reduction of 5.37% for carbon dioxide and a reduction of 5.45% for fuel consumption were achieved. In the real world, the dynamic eco-driving model has been implemented in many ways such as recruiting volunteers to collect data, training, or using smartphone applications to guide drivers to pass through the transportation nodes. Rutty et  al. (2013) recruited 15 participants to drive passenger cars with in-vehicle monitoring technology implemented as a three-phase research program. This monitoring technology focused on combining trips to shorten distances driven in a day and reducing idling time. The results showed that after training, 0.48L/0.3L of fuel were saved per gasoline vehicle/hybrid vehicle per day and 1.1 kg/0.6 kg of carbon dioxide were reduced per gasoline vehicle/hybrid vehicle per day. Suzdaleva and Nagy (2014) introduced the data-based Bayesian approach to model identification and problems related to control in the area of fuel consumption optimization for conventional vehicles after collecting real-world traffic data over a period of time. Optimal pressing of the gas pedal was computed by taking into account future values of recommended speed. Their findings summarize that the optimal controller of eco-driving brings more fuel savings in both simulation and real-world environments and the average fuel consumption improved by 7% and 10% in both these environments, respectively. Jin et al. (2016) proposed a power-based longitudinal control algorithm for a connected eco-driving system by considering the brake-specific fuel consumption of vehicles, roadway grades, SPaT information, and other constraints. The proposed algorithm can reduce energy consumption by 4% if the trip travel time and the arrival speed at the intersection are the same and by 8% if the arrival speed is relaxed. In their study, Edwards et al. (2018) applied Cooperative Intelligent Transport System (C-ITS) technologies. C-ITS was deployed in seven cities in Europe and the common evaluation of the technology was coordinated, focusing specifically on safety and the environment under real-world driving conditions. The findings revealed that both light-duty and heavy-duty vehicles showed improvement in fuel efficiency. In addition, the dynamic eco-driving system has been applied in public transport and hybrid electrified vehicles as well. Xu et al. (2017a) developed a new eco-driving algorithm by limiting instantaneous vehicle-specific power, while maintaining average speed and conserving the total distance. An application was implemented using the new eco-driving system on smartphones to remind drivers when STP levels are high, which indicates higher fuel consumptions and

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emissions. The results indicated that the new eco-driving system could help trains and buses reduce fuel consumption by 5% and 7%, respectively. The implemented new eco-driving system could help reduce PM2.5 levels by 7% and 11% for trains and buses, respectively. In summary, many studies on the dynamic eco-driving system have been done either in the field or in a simulation environment. However, still some research gaps exist, for example, how the human factor will impact the dynamic eco-driving system. Such impacts may cover the tested vehicle (or termed “ego vehicle” which refers to the vehicle that contains the sensors that perceive the environment around the vehicle) and the surrounding traffic when the penetration rate of the proposed systems varies.

6.4.3  Engine Restart Method In this form of control, the calculative property of the system sends information to a car on an upcoming stop through signaling after making sure that all preset requirements for this type of control are met. When the vehicle comes to a stop, the system starts calculating the time before the vehicle needs to be in motion again and schedules an engine restart after being assured that all the necessary prerequisites of the car ready to be in motion are satisfied. The equipment control is mounted on engines and is calibrated with a start/restart engine command that is delivered to the engine at the appropriate time. This invention has been put into use more commonly than the ones mentioned before because of its ease of use and its comparatively simple design.

6.5  INTEGRATED RAMP AND CORRIDOR CONTROL 6.5.1  Overview of Advanced Ramp Metering Technologies Increased traffic congestion and associated vehicle emissions impact ambient air quality and, thus, the health of drivers and urban dwellers. Consequently, one essential question arises in regard to the effective measures to be taken to mitigate freeway congestion. A practical solution to freeway congestion is the ramp metering system that controls vehicles entering into the freeway from an on-ramp. The traditional challenge facing this strategy is how “to ensure the design and deployment of ramp metering system effective to mitigating congestion while reducing vehicle emission?” (Wei et  al. 2019a, Allam and Wei 2015, NCDOT 2013). As shown in Figure 6-17, by controlling the entering rate, the traffic flow in the freeway will remain at a moderate level of service and capacity because of the reduction of interrupted traffic from the ramp. At the same time, it is also expected to reduce vehicle emissions, crashes, and travel times, which in reality, will greatly improve the quality of people’s daily life. On-ramp flow entering mainline freeway may cause perturbation to freeway traffic streams, which will worsen congestion on the freeway, especially when

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Figure 6-17.  Illustration of ramp metering operation (left) and simulated system (right). traffic is approaching the freeway capacity. A range of flow rates in free flow is also possible, where traffic breakdown can occur because of the perturbations from on-ramp traffic. To reduce such perturbations, ramp metering control has been widely used to adjust the rate of vehicles entering the freeway from a local street or connector (Lu et al. 2017, Zhang et al. 2013). This can be achieved by delaying the discharge of vehicles from the on-ramp. However, the on-ramp will fail to accommodate demanding arrivals if the queuing on-ramp vehicles cannot be timely discharged from the on-ramp (Allam and Wei 2015). The traffic released from the arterial signals that feed the on-ramp will increase the possibility of spillback resulting from the aggravation of the on-ramp queue (Wu et al. 2008). In general, there are three categories of ramp metering control algorithms (Chaudhary et al. 2004, Wei et al. 2019a): • Fixed-time ramp metering algorithm: The metering rate is preset for different times of day based on historical or predetermined traffic data. Fixed-time ramp meter algorithms do not consider real-time freeway mainline traffic. • Local traffic-responsive ramp metering algorithms: The metering rates are determined based on real-time traffic conditions on the freeway mainline adjacent to the ramp. • System-wide traffic-responsive ramp metering algorithms: To coordinate a group of ramp meters as an integrated system. This allows the ramp meters to balance queue delay and better manage bottlenecks and congestion, by communicating the real-time traffic data to a central computer system to determine the optimum metering rate for each ramp in the system to balance wait times and queue lengths. A great number of ramp-metering control systems have been deployed nationwide, from the early fixed-time methods based on the static historical data to traffic-responsive strategies that can respond to dynamic traffic situations (Lu et  al. 2017, Papageorgiou and Kotsialos 2002). Various model-based and non-model-based algorithms have been developed to address the need of the

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coordinated and integrated control of freeway and arterial systems. In isolated ramp-metering algorithms, the on-ramp metering rate is determined based on its local traffic conditions. The most well-known algorithm is the Asservissement LINeaire d’Entrée Autoroutiere (ALINEA) (Papageorgiou and Kotsialos 2002). Adaptive or responsive ramp metering systems have been extended to suit the network-wide scenarios with multiple on-ramps. Typical examples include systemwide adaptive ramp metering (SWARM) and ZONE (Wei et  al. 2019a, Allam and Wei 2015, NCDOT 2013, Chaudhary et al. 2004, Chu et al. 2004). The ZONE algorithm was first deployed by Minnesota DOT. With this algorithm, the freeway is divided into multiple zones, and each entry ramp is affiliated to one zone. For each zone, the condition of the mainline is maintained under certain levels of density by controlling the inflow from the on-ramps and freeway connectors. Levinson et  al. (2002) evaluated the system for various performance measures such as mobility, travel time, and delay. The results suggested that the ZONE algorithm outperforms other studies in the tested cases. BOTTLENECK is a centralized algorithm that provides local and system-level control on a selected freeway section and has been used in Seattle, Washington, for a number of years (Wei and Kashyap 2019, Zhang and Wang 2013). Basically, the algorithm has three components: 1. Local algorithm computing local-level metering rates from a look-up table based on the evaluation of upstream demand and downstream capacity of the freeway; 2. Coordination algorithm computing system-level metering rates based on system capacity constraints—it identifies a bottleneck, determines the volume reduction needed to reduce/eliminate the bottleneck, and then distributes this reduction to upstream ramps by predetermined weights 3. Final adjustment to the metering rates. Integrated ramp metering algorithms are aimed at optimizing the surface street signals in coordination with ramp meters for minimizing delays and travel time on freeways and arterials. The METALINE (Papageorgiou and Kotsialos 2002) algorithm is an extension of ALINEA in terms of integrating state feedback. However, the efficiency of METALINE greatly depends on the choice of the control matrices and targeted occupancy vector. Some studies indicated that METALINE performed better than ALINEA against nonrecurrent congestion but showed little effect on recurring congestion conditions (Allam and Wei 2015). The performance of the adaptive and integrated ramp metering systems is highly dependent on the range and accuracy of detected nearby network traffic. Inductive-loop detector has been widely used for adaptive ramp metering systems. Practical use indicates difficulties to achieve the control objectives because of the detectors’ incapability to provide accurate traffic information when congestion occurs beyond the detected areas (Wei et al. 2020; Liu 2016). To address this problem, a better detector location should be identified such that detectors will be able to capture congested traffic conditions (Liu 2016).

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Vehicle-detection technologies have evolved rapidly over the past decade because of advancements in sensors and wireless communication technologies (Wei et al. 2020, Yang and Zuo 2017). Nevertheless, the emerging CAV technology holds technical promise to make it possible to cover the networks concerned over a continuous time horizon with ubiquitous mobility data via V2I and V2V communications (Wei et al. 2020, Kashyap and Wei 2020). Such “floating sensors” have great potential to aid the integrated ramp metering system to achieve the traffic control objectives in real time, which is usually difficult to achieve by using traditional detection systems (Wei et al. 2018, Kashyap and Wei 2020).

6.5.2 Conceptual Methodology for Integrated Ramp and Corridor Control CAV technology enables the exchange of safety and operational information among road users and infrastructure via the V2V and V2I communication technologies. CAV technology holds the technical promise of producing vehicles’ instant positions along with mobility features (e.g., speed, headway between two consecutive vehicles, and even messages notifying intended turns at downstream intersections) to roadside equipment (RSE) via V2I communication. These capabilities make it possible to use CVs as “active floating sensors” that seamlessly cover the highway regions concerned over a continuous time horizon. This function can also pave the way for more innovation to greatly improve traffic control systems and operations at various highway facilities (Wei et al. 2018, 2020). With the availability of the dynamic trajectories of traffic flows transmitted via V2I communication, the ramp meter “brain” controller can timely identify the traffic conditions of the mainline freeway and the on-ramp and the upstream signalized intersection that connects the on-ramp. The ramp flow rate entering the freeway can then be enabled adaptive so that the on-ramp traffic disturbance can be timely reduced on the mainline freeway traffic while reducing the possibility of spillbacks on the local arterials. In this way, the ramp metering system can better capture precise traffic situations on the mainline freeway, arterials, and ramps to optimize the ramp rate in real time. If this optimal synthesis is successfully implemented, it can be transformed into a application procedure to greatly enhance the effectiveness of congestion mitigation strategies (Wei et al. 2020). To formulate the integrated adaptive ramp metering control algorithm by using real-time traffic measurements, multiple priority objectives are being developed to improve the system performance and to delay the mainline freeway breakdown. Figure 6-18 illustrates the flowchart of the research methodology (Wei et al. 2018). Both speed (s) and density (d) are used for congestion detection. The density, d(k, j), is used as a surrogate in practice and calculated by using the following equation:



d(k, j) =

O(k, j) l(k, j)

(6-8)

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Figure 6-18.  Methodology flowchart. Source: Wei et al. (2018).

where O(k, j) is the occupancy; l(k, j) is the length of detectors (the upstream detector at k and the downstream detector at location k + 1. Congestion is detected if

d( j, k) > dcr and s( j, k) < scr

(6-9)

where dcr is the prespecified critical density value and Scr is the chosen critical speed value. With the availability of V2I data, d(k, j) may be measured by using CV sensing data.

6.5.2.1  First Priority Objective Assuming congestion is detected between upstream and downstream detectors k and k + 1 at interval j, the excess demand on the roadway section k is calculated as follows: ΔD(k, j + 1) = V (k, j + 1)ΔT + Q(k, j) + Fon (k, j + 1)ΔT − V (k + 1, j + 1)ΔT  (6-10) where ΔD(k, j + 1) denotes queued vehicles when congestion is detected at time k; Qr(k, j) is the number of queued vehicles at time k; Fon (k, j + 1) is the detected on-ramp inflow rate; and V(k, j + 1) is the mainstream flowrate. The upper limit of Q(k, j) can be estimated by quantifying the number of vehicles within a freeway section (Zhang and Wang 2013). When congestion is detected promptly, Q(k, j) should be small and can be estimated using the mainstream volumes at Stations k and k + 1 and the corresponding on-ramp volumes as follows:

Cooperative and Automated Traffic Control



Q(k, j) ≈

∑V (k, x) −∑V in

out

265

(k, x ) = Q(k, j0 −1) + Vin (k, j) − Vout (k, j) (6-11)

where j0 = Interval when congestion starts to form, Vin = Volume of traffic entering this section through the mainstream and on-ramps measured at Station k in vehicles per time interval, and Vout = Volume of vehicles per interval leaving the section at Station k + 1. The time between the time intervals j0 and j should be no shorter than the typical free-flow travel time under incident-free conditions over the link. To prevent vehicles from further queuing up and finally have the queued vehicles discharged, ΔD should ideally be controlled within less than zero. This condition can be taken into Equation (5-2) and the terms rearranged to yield

Fon (k, j + 1)×T < V (k + 1, j + 1)×T − V (k, j + 1)×T − Q(k, j)

(6-12)

Equation (5-5) is considered the first priority control objective, indicating that the on-ramp flow rate must be constrained under a certain level to effectively address congestion. While developing the algorithm, it is made sure that the objective function is satisfied. This is achieved by controlling the ramp metering rate on the on-ramp. Vehicles are allowed to merge into the freeway only when a minimum headway is found on the rightmost lane that merges with the on-ramp (Bunker and Troutbeck 2003). This strategy not only helps control the on-ramp flow into a freeway, but also delays the formation of internal perturbations at the upstream of the ramp. When a congestion is detected as the density and speed reach a critical value, the ramp metering rate is regulated to allow a vehicle only when a gap is found. The vehicle is then discharged from the ramp to meet the gap with a relatively smooth merge into the freeway.

6.5.2.2  Second Priority Objective To avoid possible queue spillback from the on-ramp, the second priority objective is set up by using the following equation:

Dr ( j + 1) − ron ( j + 1)×T + Qr ( j) ≤ Cr −on

(6-13)

where Dr (j + 1) is the traffic demand on on-ramp at interval j + 1 and is estimated from the demand measured at the previous interval (Xie et al. 2007); Qr(j) are the vehicles stored on the on-ramp at the end of interval j; and Cr is the storage capacity of the on-ramp. Through this inequality group, the maximum storage capacity of an on-ramp can be considered in deciding its metering rate and potential negative impacts can be avoided because of vehicle spillover to local streets. To that end, a feasible ramp metering rate should range between zero and the minimum of the on-ramp storage capacity. A single-lane metered on-ramp allowing one vehicle per green can serve up to 900 veh/h and up to 1,200 veh/h if two vehicles are allowed per green.

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6.5.2.3  Third Objective The primary purpose of the third objective is to minimize the impact of shockwave propagation on the formation of freeway congestion. Treiber and Kesting (2013) Treiber et al. (2000, 2006) reported that a range of flow rate exists in free flow where traffic breakdown can occur because of internal perturbations. It can occur upstream to the merge location. Subsequently, the previous algorithms developed were unable to prevent traffic breakdown from appearing in the vicinity of the on-ramp merge area, although their control objective was achieved. Many algorithms start functioning with the normal ramp meter rate at a fixed design value. Vehicles are allowed to merge into the freeway, which aggravates congestion because of the impending vehicles discharging at the upstream of the on-ramp. A platoon of vehicles starts discharging at the upstream of the on-ramp, which will have a potential risk of causing multiple shockwave propagations. Figure 6-19 illustrates the flowchart for developing algorithms aiming to facilitate coordination of the aforementioned three priority objectives in an integrated manner. The system reaches a critically damped state when the algorithm is responsive to the congestion at the downstream, upstream, and beyond upstream detectors. This is achieved by providing optimal ramp metering without causing congestion or any possibility of shockwave propagation formation. When congestion is detected at the merge location, the ramp metering rate is decreased (higher delays on on-ramp). Conversely, the ramp metering rate is increased when the ramp volumes reach maximum, causing congestion at the arterials. Many current algorithms are believed to be underdamped algorithms.

Speed and occupancy measurements from detectors

Aggregated measurements of detector data and CV-equipped vehicle data

No

Occupancy > Critical Occupancy (Ocr) and Speed < Critical Speed (Scr) Yes

Freeway Condition

First Priority Objective

Fon(k, j+1)×T < V(k+1, J+1)) – V(k, j+1) ×T – Q(k, j)

Second Priority Objective

Dr(k+1) – ron(k+1)×T + Dr(j, k) ≤ Cr-on Ramp Demand (Arrival)

Third Priority Objective

Parameters

Optimal Meeting Rate

Time (µ) = L×Wd

Figure 6-19.  Flowchart of integrated ramp metering control algorithms. Source: Wei et al. (2018).

On-ramp Storage Capacity

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The present research adopts an underdamped algorithm strategy aiming to achieve a critically damped situation. The formation of internal perturbations is detected by the speed of vehicles usually at the upstream detectors. If the speed of the vehicles is below the critical speed, it is understood that the queue is caused due to internal perturbations of the synchronized traffic breakdown. The queueing flow, queueing density, and density of traffic at the upstream detectors are obtained from loop detectors or potentially from CV-equipped vehicles. The time (seconds) required to damp the shockwave produced will then be calculated and will be given as an input to the algorithm. The damping of the shockwave is triggered by increasing or holding the ramp meter rate high until the time when the queue is dissipated from the merge location. The time until the maximum maintained ramp meter rate is calculated by the arrival rate to the queue, length of the queue, and speed of the shockwave. Wei et  al.’s (2018, 2020) research contribution deals with (1) integrated modeling for connecting individual driver behaviors and the aggregate effect of traffic flow in a network-wide context with connections to ramps and local arterials by taking advantage of CAV-equivalent “active floating sensors”; (2) synthesis of a systematic evaluation of traffic operation and mobility in the envisioned CAV environment; and (3) measures to investigate and measure the efficiency of traditional detectors and CAV-enabled detection. Although the case study of a realistic ramp metering system provides a proof-of-concept study to positively support the benefits of the synthetic modeling approach, more scenarios of developing and applying intelligent algorithms for integrated coordination between the ramp metering controller and the signalized intersection controller need to be validated by using the trajectories of the “active floating sensors” data. The results are expected to enhance the number of methods for optimizing locations for aggregating the travel features of the mainline freeway and on-ramp.

6.6 SUMMARY This chapter proactively provides a comprehensive overview of the possibly foreseen scenarios of changes in traffic control strategies because of the introduction of CAV technologies into infrastructural systems. The chapter also provides a summary of the traffic signal control methods in a CAV environment. Specifically, the following subjects are discussed in detail: • Self-organized intelligent adaptive traffic control strategy, including details about system components, involved data analytics and relevant technologies, and optimization models; • Interactions between AVs and pedestrians or cyclists, including the stateof-the-art development of relevant communication means for autonomous transportation systems;

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• Eco-driving and traffic control systems with specific technological considerations; and • Integrated ramp metering and local arterial traffic control system. Although the CAV data–driven and V2X-based traffic control innovations are anticipated to bring evolutionary changes in traffic control technologies, and even a revamping of traffic control infrastructures, a lot of converging challenges remain or come out in the open as the emerging transportation technologies evolve over time. Nonetheless, the future success of road traffic control systems may be dependent on increased coordination among connected and/ or autonomous vehicles, infrastructures, and road users, especially vulnerable users (e.g., pedestrians and cyclists) at crossings, through exchanging information or communication-based intelligent interactions among them. The authors hope that this chapter provides insightful information to researchers and practitioners of interest and is a source of inspiration to them.

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CHAPTER 7

Unmanned Aerial Vehicle and Vertical Takeoff and Landing Technologies Rumit Kumar, Aditya M. Deshpande, Drew Scott, James Z. Wells, Manish Kumar, Shaaban Abdallah

7.1  UNMANNED AERIAL VEHICLE 7.1.1 Introduction Unmanned aerial vehicle (UAV) or drone is a type of aircraft that does not require an onboard human pilot during flight. They are either controlled remotely from a ground control station or they are programmed to fly autonomously by utilizing onboard autopilot and navigation sensors. They can primarily be classified as fixed-wing or rotary-wing UAVs. They rely on electric- or combustion engine– based propulsion systems. The fixed-wing UAVs are launched by conventional takeoff from a prepared runway or, if they are small, using catapults. They are recovered by conventional landing on a runway or using parachutes and recovery nets. The fixed-wing aircraft design is suitable for cruise flights and for achieving longer flight time and range. However, rotary-wing UAVs are more popular because of vertical takeoff and landing (VTOL) capability, and they can hover at a point for extended periods. Both fixed-wing and rotary-wing or multirotor aircraft designs have advantages for different flight phases. Advanced UAV designs have the VTOL capability of rotorcrafts and the cruise capabilities of fixed-wing aircraft. These are called convertiplanes, because they take off like a multirotor aircraft and then transition into a fixed-wing aircraft design during the cruise phase. With developments in aerial technology and robot automation, the use of UAVs is growing rapidly across many civilian applications, including real-time aerial monitoring, aerial photography, wireless coverage services, remote sensing, search and rescue operations, package delivery, and infrastructure inspection. In 2023, commercial use of UAVs is predicted to surpass the consumer drone market. In 277

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2028, it is expected to grow more than eightfold to reach $9.5 billion (Teal Group Corporation 2019). For real-time aerial monitoring, micro aerial vehicles have proven to be beneficial in tasks such as precision agriculture and traffic monitoring. Remote sensing, high-resolution imagery, and low cost of quadrotors have made it possible to use these platforms for the inspection of vegetations in farmlands (Gómez-Candón et  al. 2014). Satellite imagery sensors in real-time crop management lack highquality spatial and spectral resolutions. UAVs address this limitation by providing high spatial resolution image feed and quick turnaround times. Along with benefits in precision agriculture, these systems have also found use in monitoring forest fires (Casbeer et al. 2006). For improved situational awareness, surveillance, and intelligent response to emergencies, UAVs have proven useful (Brown et al. 2015, 2017). The remote sensing and wireless communication technologies available on UAVs have enabled their use in the search and rescue missions (Kumar et al. 2011). With the continuous increase in roadways traffic, there is a constant need for traffic surveillance to avoid road traffic congestions. Although traffic cameras are installed on the road to monitor traffic conditions, there are some blind spots in the system. These lead to a loss of information as the blind spots cannot be monitored. In such scenarios, UAVs have proved useful for the department of transportation (Zhang et al. 2017). UAVs are being deployed near signalized intersections and interstate highways to capture a birds-eye view of the traffic. This provides useful information to the department of transportation for understanding traffic flow patterns and estimate traffic engineering–related parameters. This improves the decision-making process for reducing traffic congestion at different locations. Small UAV designs and their VTOL capabilities have led to the birth of the idea of air taxi in the urban environment. This has the potential to revolutionize the current transportation system. In recent years, there has been a significant increase in interest and investment in the flying car concept. Uber is one of the organizations that is working toward the development of urban air transportation (Uber 2016). An aircraft with VTOL and cruise capability will enable riders to travel faster than using a car. According to a study conducted by National Aeronautics and Space Administration (NASA), the required infrastructure and commercially viable market for the services of on-demand air transport in urban areas and UAV-based package delivery could be in place by 2030 (Hasan 2018). Various aviation technology giants and companies, including but not limited to Airbus, Kittyhawk, Aurora Flight Sciences, and Carter Aviation Technologies are involved in the development of personal aerial vehicles (PAVs). Infrastructure inspection is another area where UAVs have shown promise. Because of their small size and remote sensing capabilities, these systems have found uses in the inspection of indoor (Raja and Pang 2016) and outdoor infrastructure (Mansouri et al. 2018). The remote sensing capabilities and agility of UAVs are also advantageous during post disaster damage assessments of critical infrastructure (Mao et al. 2018). The rest of this section provides details on various UAV designs, applications, and control methods.

7.1.2  Unmanned Aircraft History and Scope Multirotor aircraft designs have primarily gained popularity in the last two decades, but original designs go back to almost a century ago. The early

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multirotor designs were not remotely controlled aircraft and were quite big. They were developed as part of various research projects and required a human pilot (Gablehouse 1969). Louis Breguet’s Gyroplane, Etienne Oehmichen’s multirotor, De Bothezat’s Quadcopter, and D. H. Kaplan’s Convertawings aircraft are the popular designs of early multirotor aircraft (Gablehouse 1969). They were controlled by varying the blade pitch angle and speed of the rotors. They used internal combustion engines for the propulsion mechanism. However, these early designs proved to be inefficient because of a lack of automation and navigation sensors, which increased the work of human pilots. However, the advances in aerial sensors, global positioning system, inertial navigation system, satellite communication, and computer technologies were the turning point, and the remotely piloted vehicles again gained popularity among the aerospace community and led to the rise of modern-day drones, as shown in Figure 7-1. Recently, because of the availability of light-weight autopilot boards, sensors, and communication devices, there has been a rise in the use of small quadcopters for various civilian applications. A quadcopter is a low-cost system and it has flexibility for photogrammetry and remote sensing. Therefore, this system has transformed the technology space of photogrammetry (Samad et al. 2013). It is highly reliable for gathering video information. Different companies such as DJI, Intel, and Flyability Elios have introduced unique and innovative designs for several drone applications. Aerial photography, filmmaking, and drone racing are evolving as new hobbies among multirotor enthusiasts. Drones have been used for the assessment of natural disasters (such as fire, earthquake, flood) (Gomez and Purdie 2016), traffic monitoring (Elloumi et al. 2018), area monitoring (Ren et al. 2019), and infrastructure inspection (Ham et al. 2016). Computer vision and deep learning techniques are combined with flight operations to extend the scope for processing the video flight data collected by UAVs. Similarly, photogrammetry techniques are utilized for three-dimensional terrain reconstruction and evaluation of construction sites (Venkatesh et al. 2018).

Figure 7-1.  Quadcopter design.

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To automate tasks such as mapping of large-scale areas (Zou and Tan 2012), surveillance and area coverage (Deshpande et al. 2017), heavy load transportation (Barawkar et al. 2017), and robot formation (Deshpande et al. 2018), multiple robots can be used. The use of multiple UAVs can allow the work to be completed in a more efficient manner. The use of a UAV swarm for such operations has been proposed (Michael 2010). A recent demonstration from ETH Zurich showed the capability of a UAV swarm for constructing a temporary cable bridge in rescue scenarios (Augugliaro 2013). The spectrum of UAV applications is growing bigger, and, thus, organizations such as Federal Aviation Administration (FAA) and National Aeronautics and Space Administration (NASA) are actively involved in setting new regulations for the integration of UAVs into the national airspace. The drone industry is currently witnessing a boom, and it is estimated to be $89 billion over the next decade (Boyle 2015). Research and Markets presented the job opportunities in this industry and the total market size based on functionality and drone applications. They estimated projections of a $41 billion market for drones by the year 2026 for consumer and civilian applications (Research And Markets, 2021). This information shows that unmanned aerial systems hold great potential for the future, and they will be an integral part of the socioeconomic infrastructure.

7.1.3  Multirotor Design and Technologies Presently, several multirotor designs are available based on different applications. The conventional quadcopter design is the most common, as shown in Figure 7-1. The four motors actuate the four propellers on this system. This design has two pairs of propellers rotating in clockwise (CW) and counterclockwise (CCW) directions. The inherent aerodynamic property of a propeller is to produce thrust and torque around its center of rotation. However, the drag force acts opposite to the direction of motion of the UAV. If all propellers are spinning with the same angular velocity, it results in zero torque and zero angular acceleration around the yaw axis of the UAV (Goodman et al. 2015). The conventional quadcopter design can have different configurations, as shown in Figure 7-2, based on the mounting of propellers. They are described as “Plus,” “X,” and “H” designs. The attitude and position of the UAV are controlled by creating a difference in the angular speeds of propellers according to a control law. The degrees of freedom of the system include linear motion along three axes in the world frame: these are

Figure 7-2.  Conventional quadcopter configurations.

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forward, sideways, and vertical motions of the quadcopter. Similarly, the rotational motion around the body axes of the quadcopter includes rolling, pitching, and yawing of the UAV. The vertical motion is achieved by increasing or decreasing the collective angular speed of all motors simultaneously. However, quadcopter being an underactuated system, its forward motion is dependent on the pitching moment of the system. Similarly, the rolling moment is utilized for achieving lateral motion (Nemati et al. 2016b). The autopilot of the multirotor enables semiautonomous and fully autonomous flying capabilities for assisting the pilot. The propellers of a conventional quadcopter design spin at very high angular speeds, and, in general, the control bandwidth for changing the angular speed is limited. This constraint imposes fundamental limitations for achieving certain maneuvers during flight. Figure 7-3 shows a multirotor platform designed and developed in UAV MASTER LAB at the University of Cincinnati. It has a coaxial-rotor configuration where the coaxial rotors spin in opposite directions. The primary advantage of using coaxial rotor configuration is that the moment generated owing to propeller rotation is canceled and it yields a stable platform. The coaxial configuration can exhibit faulttolerant capabilities in case of a rotor failure (Saied et al. 2015). Variable blade pitch quadcopter is another advanced design in which the four propellers of the quadcopter spin at a constant angular speed. However, the motion of the UAV is achieved by manipulating the aerodynamic parameters of the rotational propellers. This is achieved by changing the blade pitch angle of the rotating propellers (Cutler and How 2015). The vertical motion of a variable pitch quadcopter design is controlled by changing the collective pitch of all propellers. Similarly, translational and rotational motion is achieved according to the designed control law of the system. This includes the longitudinal and lateral motions as well as roll, pitch, and yaw maneuvers (Gupta and Kothari 2016). However, it should be noted that a variable pitch quadcopter is still an underactuated system as four control inputs are used to control six degrees of freedom. The schematic of varying the blade pitch angle is shown in Figure 7-4. Various researchers have also shown inverted thrust capabilities of the variable pitch quadcopter, in which the UAV can fly upside down (Cutler and How 2015).

Figure 7-3.  Coaxial multirotor platform at UAV MASTER LAB.

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Figure 7-4.  Variable blade pitch propeller design. Another design methodology exists for using variable pitch quadcopter during long-endurance flight operations. This design consists of a gasoline-engine-based propulsion system (Pang et al. 2016). This technology has the potential to evolve as large-size quadcopters in the future, which can be used for transportation. As previously discussed, the conventional and variable blade pitch design quadcopters are inherently underactuated, and this problem is addressed by the tilt-rotor quadcopter design. In this design, individual propeller motors are actuated to tilt around the quadcopter arm, which provides additional control inputs to the UAV, thus enabling thrust vectoring capabilities as shown in Figure 7-5 (Kumar et al. 2017a). Figure 7-6 shows the schematic of the tilt-rotor over a multirotor arm. The thrust force is perpendicular to the tip path plane of the propeller. The tilt-rotor changes the direction of the thrust force, which leads to horizontal and vertical components of the force and moment. These force and moment components can be utilized to overactuate the system in a tilt-rotor quadcopter (Ryll et al. 2012). Ryll et al. were the first to propose a quadcopter with tilting propellers; they presented the equations of motion and showed controllability analysis to design a closed-loop controller. They further extended

Figure 7-5.  Tilt-rotor UAV diagram.

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Figure 7-6.  Tilt-rotor schematic. their research by performing experimentation for autonomous trajectory tracking and execution of different flight modes (Ryll et al. 2015). The work by Nemati and Kumar (2014) presented detailed equations of motion for the tilt-rotor UAV. They highlighted the maneuvering capabilities of the UAV at specified roll-and-itch angles. The nonlinear sliding mode controller for the tilt-rotor UAV has been described in Sridhar et al. (2017). The tilt-rotor UAV provides better disturbance rejection in the presence of wind gusts (Scholz and Trommer 2016). The tilt-rotor quadcopter can track flight trajectories more efficiently than a conventional quadcopter platform in the presence of sensor noise and external disturbances, as shown in Kumar et al. (2017b). The experimental demonstration of a tilt-rotor UAV landing on a moving ground vehicle is shown in Bhargavapuri (2019); this is a proof of concept for the future applications of drone platforms. The flight operations of quadcopters always come with an increased risk of motor or propeller failure during flight. Currently, emergency parachutes are available as a viable solution for assisting emergency landing of quadcopters in case of propeller or motor failure. The futuristic operational scenario of multirotor requires fault-tolerant systems. In the era of urban air mobility, risk mitigation will be extremely important and safe flight operations will play a key role in day-to-day life. UAV systems should possess fail-safe capabilities. If a motor or propeller failure occurs, the system must be capable of maintaining stability and continuing the mission for achieving safe landing without compromising the objectives of the flight (Nemati et al. 2016a). A robust, fault-tolerant control law and redundant mechanical design of the quadcopter can ensure safe handling of the quadcopter even after propeller failure. Multirotor with six or more propellers are popular fault-tolerant drones as they have structural redundancy. However, the tilt-rotor UAV also possesses fault-tolerant capabilities, and it can handle a situation where there is a complete failure of one motor during flight. Kumar et al. (2018) and Sridhar et al. (2018) have shown fault-tolerant capacities of the tiltrotor UAV using structural and flight controller reconfiguration. They propose the concept of actuating the quadcopter arm for the conversion of a tilt-rotor UAV into a T-copter in case of a propeller failure.

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This section covered a brief literature overview of the ongoing multirotor research and showed the potential of extending UAV applications. With a rise in the use of UAVs, there is also a need for incorporating UAV traffic in the national airspace. The advanced designs will have VTOL capability of rotary planes and cruise capabilities like fixed-wing aircraft. When different companies launch their services for urban air mobility, it will be essential to monitor air traffic and plan smart flight paths for efficient and safe aerial operations. The next section describes an overview of the ongoing research for the integration of aerial vehicles into national airspace.

7.2  URBAN AIR MOBILITY The small unmanned aerial system (sUAS) designs described in the preceding section hold great potential for revolutionizing the future of transportation. Several companies are directing resources toward the development of urban air mobility vehicles, also known as air taxis or personal aerial vehicles (PAVs). NASA’s (2018) urban air mobility market study report describes three primary uses of UAM: (1) Last-Mile delivery, (2) Air-Metro, and (3) Air-Taxi. Case 1 includes delivery of packages weighing less than 5 lb. Recently, there have been advancements in the use of UAVs as freight transportation systems. In this case, the drones are being developed to fly and deliver tons of payload and cargo (De Reyes and Macneill 2020) over large distances. Case 2 is more like a public transportation system that will have predetermined flight routes, vehicle frequencies, and stops for aerial vehicles to transport two to five passengers per flight. Finally, the third case includes air taxi, which is more of a door-to-door ride-sharing-based service. Passengers can be picked up and dropped off at different locations based on the demand of the service across a city. Uber is working toward the development of urban air transportation (Uber 2016). This infrastructure will include aircraft approach and takeoff areas, also known as Vertiport/Skyport, charging facilities, and maintenance zones. CityAirBus, Airbus Vahana, Joby Aviation, AutoFlightX V600, and Lilium Jet 5-Seater are some of the popular names of companies and their air taxi prototypes that are currently in the development and testing phase. When air taxi services are launched, urban air mobility will require new rules in airspace traffic management. This section describes a brief overview of various research methodologies that are going to play a vital role in the safe operations of aerial vehicles for urban air mobility.

7.2.1  Unmanned Aerial Vehicle Traffic Management Widespread use of autonomous vehicles presents many different challenges with respect to their integration with the National Airspace System (NAS), the biggest challenge being management in an efficient and safe manner. The major part of the problem involves robust coordination and cooperation among aerial vehicles, which may include manned as well as unmanned systems. Any failure

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of coordination may result in collisions that can be catastrophic. Multirobot coordination is a well-studied area, with a variety of solutions, including both centralized and decentralized methods. It is needed to handle the increasing heterogeneous aerial vehicle population in airspace (Yanmaz et  al. 2018). Cooperation in the area of UAV traffic will help address the problem of safety in high-density UAV traffic. For safe flight operations in the vicinity of other aircraft, the UAV path should be planned in a cooperative manner (Schouwenaars et al. 2004). This is particularly difficult in the vicinity of other manned aircraft when the pilot intent of manned aircraft is unknown as the pilot may execute any maneuvers at will. This requires an intelligent control system for UAVs that can account for these uncertainties. This may include the onboard computer or an offboard control station for the UAV, which can execute obstacle avoidance and pathplanning algorithms for controlling the UAV in real time. Although challenges for regulating unmanned aerial traffic exist, these systems hold great potential in the future, as discussed in Section 6.1.1. Hence, there is an indisputable requirement of developing regulations and management processes for UAV traffic. The current system for managing aerial traffic in the national airspace will not be sufficient as UAVs are finding more and more applications in the civilian domain (Prevot et  al. 2016). FAA is working toward incorporating necessary changes in the unmanned aerial vehicle traffic management (UTM) framework (FAA, and Office of the Chief Counsel 2015). The air traffic controller (ATC) aims primarily to avoid collisions while organizing the flow of aerial traffic optimally. They also communicate information such as weather and nearby aircraft locations to the pilots within the airspace. ATC enforces separation rules keeping vehicles sufficiently dispersed to avoid collisions. On the contrary, the UTM intends to propagate small decisions in air space from the local level to the flow of traffic in the larger airspace. However, it will be unreliable to depend on human operators to provide information to every flying vehicle. NASA has been developing a UTM structure to solve the issue of integrating low-altitude UAVs into airspace (Angela 2019). This work envisions UAVs to be restricted to low-altitude airspace, whereas normal aircraft traffic such as passenger jets fly at higher altitudes. Currently, there are two ideated concepts for UTM systems under this work. The first system is a portable UTM, transient in nature, which will be deployed locally as needed in cases such as disaster relief. The second is a persistent system, providing continuous support over a permanently set area. NASA is comparing and integrating various technologies for UTM systems in a series of tests called technology capability levels (TCLs) (Aweiss et al. 2018). The first set of tests, also known as UTM TCL1, was performed in August 2015. They focused on geofencing and trajectory scheduling. The second set of tests (TCL2) was demonstrated in October 2016 and focused on beyond-line-of-sight flight operations within areas with sparse traffic. They also tested the technologies to adapt the system with variations in the airspace over time. UTM TCL3 was held in March 2018; this emphasized maintaining safe operating distances between both cooperative and noncooperative UAVs. TCL4 testing is ongoing in 2019; it involves UAV flight operations in densely populated urban areas. This will include

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UAV flights in an urban environment such as package delivery or surveillance for news (Chakrabarty et al. 2019). There are also provisions for establishing UTMcorridors such that these regions can be dedicated to UAV traffic and bigger aircraft will avoid flying via these UTM-corridors. A novel UTM structure is presented in a study by Sarim et  al. (2019), in addition to a path-planning and detect-and-avoid (DAA) system, which is discussed later in this chapter. This work gives both a communication protocol and a flight protocol. The UTM structure is a centralized approach, in which a central UTM server deals with UAV traffic and mission planning. The airspace is discretized into cuboids, and at most, one UAV is assigned in a cuboid at a given time. Breaking the airspace in this way allows a simple approach for both the path planning and detect and avoid, while also allowing efficiency in traffic flow as UAVs claim only a single cuboid at any given time. These cuboids are scaled so that UAVs will remain at a specified minimum separation distance from other aircraft at all times. The UAV will have a communication link with the UTM providing information about the state of the vehicle, such as position, velocity, and battery status. A secondary link will exist to deal with any sudden changes to the environment. In these cases, a change in a planned path may be necessary, which will be communicated over this secondary link. A schematic of a basic centralized UTM, similar to this study, is given in Figure 7-7. Amazon published a white paper on the integration of small unmanned aircraft systems (sUAS) into airspace, flight operations, and a design for a feasible solution (Amazon Prime Air 2015). They proposed that segregated airspace is the most efficient solution. The airspace below 500 ft will be reserved as civil airspace, which will separate sUAS from other aviation vehicles of greater size and speed. This civil airspace is divided again, with a top layer for high-speed vehicles and the bottom layer for local traffic traveling at a lower speed. A layer also exists for the no-fly zone between the segregated civil airspace and the above airspace for added safety. Amazon also proposed a central controller for coordination. These will oversee a fleet of vehicles, coordinating with other nearby operators to manage traffic, including vehicle-to-vehicle communication between fleets. Thus, decision-making is distributed among these local operators. It is also proposed

Figure 7-7.  Example UTM structure.

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that the operators should intervene only in exceptional cases, for example during emergencies where traffic must be adjusted. Google (2015) also proposed an Airspace System to incorporate the UAS in the national airspace, which operates in designated airspace in line with FAA regulations. Here, manned aircraft may also enter the UAV airspace, with UAVs giving the right of way to the manned aircraft. In this system, every vehicle will belong to some operator. Each operator will communicate with an airspace service provider (ASP) over existing cellular networks. ASPs are open to anyone to create; however, they will be registered and regulated to ensure safety and security. Each ASP will monitor nearby UAVs, communicating path-planning information to all airborne systems. These airspace service providers also connect the air traffic control to the UAV pilot over a secured communication network.

7.2.2  Federal Aviation Administration Regulations for Small UAVs The FAA rules for small UAVs are given in Part 107 of the FAA regulations— Small Unmanned Aircraft Systems (FAA 2019). A summary of this section is also provided by FAA (2018). The regulations should be reviewed entirely by any UAV pilot before flying within the national airspace. As UAV technologies grow rapidly, the laws and regulations set out by FAA are likely to change, so the most up-to-date and accurate laws should be found directly through FAA sources. According to current regulations, any unmanned aircraft weighing more than 0.55 pounds must be registered with FAA. FAA defines a small, unmanned aircraft as an aircraft weighing less than 55 pounds in total weight at takeoff without the ability for direct human intervention. However, larger aircraft will have a different set of regulations. The maximum allowed speed for any UAV is 100 mi per hour. The flight of a small UAS is restricted to only during daylight, defined as 30 min before official sunrise and 30 min after official sunset. The allowed altitude is at the most 400 ft above the ground, except in cases within 400 ft of a structure. The flight of UAVs is not currently allowed overhead any person not involved in the operation unless they are within a covered structure. A pilot may not operate the aircraft from within a moving vehicle, except in a sparsely populated area. Each drone operating under regulations laid out in Part 107 must be registered with FAA, and every pilot of these drones must be certified for flying a small UAS. No person may pilot or participate in the flight operation of a UAV if they have a medical condition that will impair the operation of the vehicle. In case of an emergency, the pilot may deviate from any rules presented in Part 107 to the end of responding to that emergency. Small UAVs must give the right of way to all manned aircraft, meaning that they must give way to these manned aircraft by not flying near them. As far as airspace regulations for UAVs are concerned, FAA has designated two types of airspace: regulatory and nonregulatory. This airspace has subcategories such as controlled and uncontrolled, special use, and other (FAA 2016). Controlled airspace is broken up into several classes. Different restrictions are there for each type of controlled airspace. Class A airspace is a very high-altitude zone, at about

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18,000 ft above mean sea level. In general, Class B airspace is 10,000 ft above sea level, surrounding busy airports. Class C is airspace from the ground to 4,000 ft above sea level. These airspaces are controlled by surrounding airports that have a control tower and radar control and possess a certain amount of devices for flight operations. The configuration of Class B and Class C airspaces are individually tailored to each airport. Class D airspace is near airports with an operational control tower, normally from the surface to 2,500 ft above airport elevation. Class E airspace is controlled airspace that is not defined as Class A, B, C, or D. Class G airspace falls under uncontrolled airspace and consists of any airspace not designated as Class A, B, C, D, or E. The Part 107 certified pilot of a sUAS does not require a waiver to fly in Class G airspaces below 400 ft, provided the designated airspace of flight operations is not assigned to Class A, B, C, D, or E airspace.

7.2.3  Unmanned Aerial System Path Planning Path planning constitutes a large part of managing traffic within an airspace. In airspaces crowded with UAVs, which is likely to be the case in urban areas in the near future, planning is important for efficiency and safety. UAV navigation is typically achieved in a hierarchical manner (Beard et al. 2005) where the highestlevel control is the path planning followed by trajectory smoothing, trajectory tracking, and autopilot at successively lower levels. A path planning algorithm should be able to provide a feasible path defined by a series of waypoints from start to goal locations, while avoiding obstacles that include other moving aircraft or stationary structures. Trajectory smoothing transforms the waypoints into a parameterized curve representing the desired trajectory in a 3D environment to be followed by the UAV, while accounting for kinematic and dynamic constraints of the vehicle. The trajectory tracking is, essentially, a control algorithm that transforms the trajectory information into commands, such as desired velocity, desired altitude, and desired heading. The autopilot takes that information and converts it into lower-level control actions. Lower-level control functions of autopilot and trajectory smoothing are well-studied problems in the literature (Lee and Kim 2017). Path planning, on the contrary, remains challenging for tackling the problem of UAV traffic management, because this problem requires solutions to be scalable and to address the dynamic nature of UAV operations (Chakrabarty et al. 2019). Here, we provide a brief review of the methods available in the literature for solving the path-planning problem. The path-planning solution should be able to create optimal, safe, and fuelefficient paths for aerial vehicles. These paths can be in two-dimensional (2D) or three-dimensional (3D) space without considering the dynamics of such vehicles. Thus, these fall under a set of geometric problems (Gasparetto et al. 2015). As the problem scales from a single UAV to multiple UAV systems, the complex variants may include parameters such as external disturbances to the vehicle such as wind, temporal constraints, and motion constraints owing to other vehicles (Coutinho et al. 2018). Path-planning approaches can be broadly categorized as combinatorial algorithms for planning, sampling-based algorithms, and biologically inspired

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algorithms (Debnath et al. 2019). The problems that involve temporal constraints and resource constraints for UAVs can also be classified as trajectory optimization, UAV routing, and UAV task assignment problem (Coutinho et al. 2018). Combinatorial approaches for autonomous vehicle planning use the configuration space of the vehicle to solve for a feasible path. These approaches include algorithms such as roadmap techniques, artificial potential methods, and cell decomposition algorithms (Gasparetto et al. 2015). Graph search algorithms are also categorized under the combinatorial approach. Roadmap techniques for path planning can be categorized as visibility graph-based path generation algorithms (Huang and Teo 2019) and Voronoi diagram-based path planning (Niu et al. 2019). A* (“A-star”) algorithm is a well-known graph search-based path planning algorithm (Hart et al. 1968). This algorithm discretizes the vehicle configuration space as a graph with vertexes as points in the configuration space and edges formed by the connecting links between each pair of vertices. This algorithm computes an optimum path through the graph between the given starting and goal locations to minimize the traveling cost of the vehicle. Although there are other planning algorithms that can outperform A*, its simplicity and effectiveness make it a widespread option for various path-planning applications. Sampling-based algorithms for UAV path planning use a nonstructural finite number of points sampled from configuration space. These points are then connected to form a feasible path from the current UAV location to goal location (Zammit and Van Kampen 2018). The probabilistic roadmap method (PRM) and the Rapidly Exploring Random Tree (RRT) are the algorithms that fall into this category. PRM, RRT, and their variants are generic approaches to obtain a feasible path for automated vehicles. These algorithms are simple and probabilistically complete, that is, they guarantee a solution for the path-planning problem as the time approaches infinity. However, these algorithms do not guarantee an optimal solution (Elbanhawi and Simic 2014). RRT has been used for finite search space problems in the context of path planning (LaValle and Kuffner 2001). It explores the search space through a stochastic process. Where a simple random walk is biased around the starting location, RRT is biased evenly across the whole search space allowing rapid exploration. The work in Balachandran et al. (2017a) uses an RRT in addition to NASA’s geo-fencing and collision avoidance logic to find a feasible path avoiding obstacles and other vehicles. RRT algorithms have also been used on a larger scale through airspace tessellation (Balachandran et al. 2017b). Airspace is divided into several cells through Voronoi Tessellation and A* is used to find a path connecting the cells. Then, RRT is used to determine a path through the interior of each cell. The bioinspired algorithms for path planning of UAVs include genetic algorithm (GA) (Elhoseny et al. 2018), particle swarm optimization (PSO) (Fu et al. 2011), ant colony optimization (Cekmez et al. 2016), bee colony algorithm (Pan et  al. 2017), simulated annealing (Turker et  al. 2015), firefly algorithm (Deshpande et al. 2013), and many others (Roberge et al. 2018). These algorithms are heuristic approaches that rely on improvements in candidate solutions over several iterations to a near-optimal solution. These iterative algorithms are a good

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fit to solve problems to achieve a quasi-optimal solution in which deterministic optimization approaches are computationally expensive (Molina et al. 2018). Many variants of the GA-based path-planning algorithm have been proposed, wherein some have proven to be beneficial even in a dynamic environment allowing better convergence to feasible paths for autonomous mobile robots (Tuncer and Yildirim 2012). Path-planning algorithms based on fuzzy logic for unmanned aerial combat have also been studied in (Ernest and Cohen 2015). Hybrid variants of path-planning approaches have also been proposed. Masehian and Sedighizadeh (2010) presented a path-planning algorithm that is a combination of a bioinspired approach and a sampling-based approach. Here, PSO is used for global path planning and may lead to a locally optimal solution. To account for this limitation of PSO, the authors have used PRM for local path planning, which allows the candidate solution path to escape the local optima. As the path-planning problem scales from a single UAV to multiple UAVs, one may need to consider alternate approaches that can provide comparatively better guarantees of safe and feasible paths for automated vehicles. Mixed-integer linear programming (MILP) has been used to provide optimal solutions for problems with a finite search space and has been demonstrated to provide solutions with a large number of applied constraints (Radmanesh et al. 2016a, b; Radmanesh and Kumar 2016). In (Ishihara 2016), an algorithm with a semi-analytical solution using an elliptic integral formulation of path planning to rapidly solve for a feasible fixedwing UAV path in national airspace is presented. The complexity of the planning problem increased twofold in this work by including the time constraints on the vehicles as well as the external disturbance of the uniform wind field. Fixed-wing UAVs are nonholonomic systems as there are differential constraints on their motion. For such systems, Dubins paths are often used during path planning to provide the shortest curve between two points with a constraint on the curvature for the whole path (Sujit et al. 2014). Shanmugavel et al. (2010) used Dubins paths for cooperative path planning to yield flyable trajectories without conflicts in a multi-UAV scenario. This process includes multiple stages, in which the first stage provides Dubins paths for each vehicle subjected to the differential constraints, the second stage enforces no collisions constraint, and in the final stage, all paths are made equal in length so that all vehicles arrive at their respective goal locations simultaneously. At higher altitudes in the airspace, one may need to account for the general flow of aerial traffic that includes manned and unmanned systems. To avoid complications that may arise owing to conflicts in autonomous UAV paths, the airspace can be divided into separate zones in which UAVs can safely travel within the airspace. In case of high-density air traffic, lane-based systems can be implemented, in which sections of airspace can be partitioned analogous to roads for ground vehicles, with small roads for low-speed flights and highways allowing higher speeds and a greater flow of aerial traffic (Jang et al. 2017). In this ideated system, higher altitudes are reserved for fixed wings or high-speed vehicles, whereas slow-moving and hovering vehicles fly in low-altitude lanes. The entry and exit of vehicles between these lanes are controlled by a central UTM

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system. This proposed system also includes pullout areas in case of emergency landings or other requirements. This system can allow a better flow of aerial traffic and can also simplify the task of path planning.

7.2.4  Detect-and-Avoid Systems A robust UTM system requires a safe operation of flying vehicles and efficient integration into existing transportation infrastructure. One of the main parameters is resolving conflicts among aircraft within the airspace. Although UAV traffic is managed at a top level using a global path planner, a system to detect and resolve projected path conflicts should be executed at a local level. These are known as detect-and-avoid (DAA) or sense-and-avoid (SAA) systems. The aircraft may deviate from the assigned paths for several reasons such as operational delays, excessive wind, noncooperative agents, or other external factors. The DAA system must be able to respond to dynamic changes in the system, such as new aircraft entering the area or mid-air changes during a flight mission. Several algorithmic approaches are available to solve this problem, including MILP formulation, classical control theory (Asep et al. 1996), fuzzy logic (Mostov and Soloviev 1996), and game theory (Tomlin et al. 2000)–based DAA solutions. The detection of conflicts requires information from a central ground control station and information from the airborne UAV sensors. The detect-and-avoid techniques can be broadly classified as (1) on-board DAA, or (2) off-board DAA. The on-board DAA makes use of on-board sensors (such as vision, LiDAR, radar, or obstacle detection ultrasonic sensors) to detect the obstacles and estimate their relative positions. One study (Ramasamy et al. 2016) used LiDAR on small UAVs to detect obstacles and plan new trajectories for flight missions. Another study explored the use of computer vision-based methods for sense and avoid in UAVs (Carnie et al. 2006). The on-board DAA methods may also utilize peer-to-peer communications such as automatic dependent surveillance-broadcast (ADS-B) to detect a potential conflict. The off-board DAA methods rely on ground-based sensory and computational resources to carry out the detect-and-avoid tasks. Typically, ground-based radars are utilized along with path-planning algorithms for achieving DAA. The performance of radar-based detection methods has been studied by Accardo et al. (2013). They compared the radar detection accuracy with reference to the GPS data. However, other techniques utilizing telemetry, LTE communication, and ADS-B can also be used to generate local situational awareness (Lin and Saripalli 2015). LTE communication among UAVs allows continuous monitoring of UAV locations (Yeniçeri et al. 2013). The information from the ground control station may be limited to the UAVs within the airspace; however, this may not include noncooperative agents. In the absence of such information, noncooperative agents and their respective flight trajectories should be detected by on-board sensors. Further, avoidance logic should be executed for trajectory reshaping to ensure collision-free flights, and all agents should reach their final destination without conflicts. Sarim et  al. (2019) proposed a DAA system that resolves the airspace conflicts in a market-based manner. In this work, locations are assigned prices

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that are governed by the demand among the UAVs. They consider the tessellating structure for the airspace into a grid. If more than one UAV occupies a given cell at the same time, then the price of that grid cell goes up. The work is distributed among different agents and each agent seeks to minimize their cost from their starting location to their goal location accounting for both the total distance and the cost for each grid. The performance of this method was compared with that of MILP formulation. It provided solutions of similar optimality as MILP but a great advantage in terms of computational time. Similarly, another DAA solution used an auction-based approach to resolve conflicts within the airspace (Scott et al. 2019). Like the market-based approach, the airspace is tessellated into a grid. The various grid cells are viewed as resources in this formulation. However, instead of a set of prices for UAV assignments, auctions will proceed for each conflict location. UAVs involved in the conflict place bids iteratively, reaching higher and higher bids until all UAVs but one do not place a next-higher bid as it is not a profitable decision. UAVs compare the cost of an alternate path and the original path through the conflict cell to determine that maximum bid that is still profitable. Keep-out geofences have also been utilized by Balachandran et al. (2017b). They use a deterministic algorithm to come up with the new trajectories of all UAVs so that collisions are avoided. The UAVs exchange position and velocity information. Because the resolution algorithm is deterministic, all UAVs arrive at the same solution. This work also shows that geofencing along the flight paths can ensure safe separation between different agents. Dolph et al. (2017) and Sahawneh et al. (2015) presented work for a DAA system by using an ADS-B sensor. ADS-B offers long-range detection of other UAVs with minimal power and weight restrictions. Radar is commonly used in larger aircraft for detection, but for small UAVs, trade-offs must be made between radar range and accuracy because of weight, power, and computational limitations. They describe that the ADS-B sensor is better suited to small UAVs, with an omnidirectional range of 20 nautical miles and low power requirements. ADS-B does rely on GPS information, which can have a low-quality signal in some areas. However, FAA will enforce a rule by 2020 that all aircraft within certain airspaces should have an ADS-B sensor on-board.

7.2.5  Conclusions of Sections 7.1 and 7.2 Advancements in technological areas including computing, sensing, communication, and data processing (artificial intelligence and big data analysis) have resulted in UAV technology becoming mature to a point where it can find applications in several domains, including infrastructure assessment, traffic monitoring, emergency response, precision agriculture, construction monitoring, package delivery, and human transport. Various UAS designs, as well as autopilot systems for controlling the corresponding drone platform, are currently available. However, several challenges need to be addressed before the full potential of UAV technology is realized within the commercial domain. One of the primary challenges is maximizing the range and flight times of drones.

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This will include innovations in power source as well as optimization in power consumption for drone operations. Researchers have already started exploring these areas. Applications of solar power and hydrogen fuel cells have shown promising results for maximizing the flight times of drones (McConnell 2007, Goh et al. 2019). Other challenges arise from making the UAV operations, including their command and control, and data processing, as autonomous as possible so that human supervision and intervention is minimized. In this context, the management of UAV traffic within airspaces is a vital area to be addressed as UAVs become more widely adopted. This is a problem that must be addressed before UAVs can be safely integrated into the National Airspace in any widespread manner. There are several components to any UAV traffic management (UTM) solution, as discussed in this chapter. The UTM architecture is an important aspect, handling the communication and operation among all aircraft. With this comes the issues of path planning and detect and avoid, both of which have had a variety of viable solutions. An additional issue with traffic management is that solutions must be scalable to the great number of UAVs expected to be deployed in urban areas in the future. As the UTM problem is approached and solved, it must be done in a cohesive manner across all airspaces. Although different agencies and research institutions develop a comprehensive UTM solution, FAA has published regulations on small UAVs, restricting their usage in certain airspaces and in proximity to other aircraft.

7.3  OVERVIEW OF VERTICAL TAKEOFF AND LANDING AVIATION 7.3.1 Overview of Current Vertical Takeoff and Landing Technology With a growing need for the application of VTOL aircraft in urban settings, it is imperative to assess the state of existing technology. Today, helicopters are the go-to solution for any situation where a VTOL aircraft is needed with a variety of optimizations available to choose. However, there is no better solution to transport relatively large payloads over short distances by air. Since their inception, helicopters have utilized a variety of mechanical controls, including cyclic and collective. Although this provides sufficient control authority to maneuver a helicopter in tight urban settings, it leaves the most commonly used VTOL machines lacking in desired redundancies. The need for the swashplate system arises from the use of single-rotor designs that cause dissymmetry of lift in the advancing and retreating blades. This becomes the limiting factor in defining the “never-exceed speed” of such an aircraft because of its susceptibility to retreating blade stall. A two-rotor design will, in theory, negate this effect, although tandem-rotorcraft designs usually have their “never-exceed speed” defined by the aerodynamic drag of the aircraft design at lower than that imposed by the dissymmetry of lift.

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For widespread urban application, as with other methods of transportation, a restrictive policy of noise generation is necessary. This is something that both tailed rotorcraft and NOTAR crafts currently lack, despite efforts to create modifications in conventional designs, like shrouded tail rotors, which reduce noise generation directly under the aircraft but typically require 8 to 12 blade rotors as compared with 2 to 4 blades in conventional designs, increasing the frequency of the noise being generated. With the rise of unmanned systems in the recent past, quadcopter and other multirotor designs have seen a resurgence in small-scale aircraft. These designs carry a major benefit on the control aspect over conventional single-rotor designs, by removing the need for tailed designs to balance the torque generated by the rotors. Multirotors rely on balancing of the rotors in such a manner as to effectively cancel the net torque on the entire system; this is done by utilizing counterrotating rotors. For example, in a quadcopter design, opposite rotors will spin in the same direction, whereas adjacent rotors will spin in opposite directions; this creates two sets of rotors generating equal and opposite amounts of torque on the aircraft when operating at equal speeds. This balance can be disturbed to cause the aircraft to yaw in the desired direction, while maintaining the total lift being generated, by increasing the speed of one set of codirectional rotors and reducing the speed of the other. Thus, multirotors have simpler mechanical designs in contrast to conventional helicopters, leading to ease reliability of controls. In the realm of large payload VTOL aircraft, the tilt-rotor design has proven to be a faster alternative to helicopters at the expense of payload-carrying capabilities, exceeding 300 knots in forward flight as compared with the average maximum of 150 knots for modern helicopters. Tilt-rotors promise lower noise generation in forward flight, which makes them better suited for urban applications than helicopters. The integration of fixed-wing design and the utilization of flight control surfaces allow tilt-rotors to achieve altitudes much higher than those attainable by helicopters, opening them to be employed in applications reserved for conventional fixed-wing aircraft, although tilt-rotors suffer from considerably deteriorated payload capabilities when taking off from higher altitudes. Tilt-rotor technology has yet to see widespread use, with current implementation being limited to the military, although efforts to create medium-body civilian-use tiltrotors are underway.

7.3.2  Need for Automated Flight Systems Currently, FAA requires all helicopter pilots to obtain a private pilot’s license; the requirements mandate the pilot be at least 17 years of age, obtain an FAA medical certificate, pass an FAA written test, and log at least 40 h of flight time, including 10 hours of solo flight. The requirements also demand 3 h of dual cross-country flying, 3 h of dual nighttime flying, 3 h of solo cross-country flying, at least one 50NM cross-country flight, and at least three solo takeoffs and landings. These regulations apply to all pilots who wish to receive a “helicopter rating.” The regulations ensure the proficiency of all pilots in emergency procedures and their ability to safely perform flights according to the requirements of the airspace

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in which they are flying, the instructions of air traffic control, and in accordance with the Aeronautical Information Manual of FAA. These requirements take a significant time to complete and are not ideal for the average person. Learning to drive a car can be done in weeks for a low cost and is achievable by most. However, helicopter training can take years, cost a significant amount, and may not be achievable by all. In addition, personal flight vehicles will require even stricter training to operate in tighter spaces. It will, therefore, become necessary that autonomous flight systems be developed meeting the requirements for manned flight and the capability of operating in tight guidance, navigation, and control (GNC) requirements. Without such control systems, we are unlikely to see widespread application of the technology being developed. This, of course, will also have great effect on the regulations that govern aeronautical operations, which will need to be revised. For example, the Aeronautical Information Manual of FAA does not state a requirement for separation between fixed-wing aircraft and helicopter operations, but only warns “pilots of small aircraft should avoid operating within three rotor diameters of any helicopter in a slow hover taxi or stationary hover.” Further defined separation designations will be needed for different VTOL aircraft for safe operations in tight urban airspace. Air traffic control regulations will also probably need modification; for example, a new airspace classification for urban airspace may be needed for the safe operation of automated manned VTOL aircraft to create separation between automated flights and conventional aviation.

7.3.2.1 Safety Safety is a significant challenge in personal flight vehicles. If a car breaks down on the road, you can simply pull off to the side of the road. If a plane breaks down, there has to be a way for the passenger to land safely. A few ways to implement safety in personal flying aircraft are available: reducing the risk of failure or including backup systems. Personal flight systems will probably require a higher degree of redundancy as well as tighter operational allowance. New regulations will have to define safe operating conditions for urban personal flight, including weather conditions, back-up systems, pilot training, and navigational restrictions; for example, an equivalent of the ETOPS rating will probably have to be created.

7.3.2.2  Airframe Design The concerns of airframe design in the work of FlyUCs are not dissimilar to the ones in conventional aircraft design. FlyUC is a student organization at the University of Cincinnati (UC) dedicated to research and development in the field of VTOL aviation. It started as part of Hyperloop UC, when a group of students began working on the design of a personal VTOL aircraft for Boeing’s GoFly Competition (GoFly Prize 2019). Over time, the team expanded and is now comprised of over 30 students of different engineering fields as well as faculty

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advisors. FlyUC works closely with the Department of Aerospace Engineering and Engineering Mechanics at the University of Cincinnati as well as UC’s UAV MASTER Lab. The team’s vision is to improve existing VTOL technology and also the capabilities and performance of electric VTOL aircraft. We see the future of flight in personal electric VTOL aircraft, capable of performing autonomous flight in urban airspace. The FlyUC’s team designed the airframes keeping in mind durability and easy maintenance. Initial designs used a combination of aluminum alloy tubes and carbon fiber tubes; the aluminum alloy tubes were used to create the main structure and carbon fiber tubes made the cross members. This considerably reduced the weight of the airframe. This approach was later dropped in favor of an aluminum-only design because of a number of factors, including the significantly higher costs of carbon fiber as compared to aluminum alloys, the insufficiency of precision in manufacturing carbon fiber parts, the weakness of carbon fiber aluminum joints as carbon fiber was predicted to weaken and tear near the rivets, and the increased difficulties with maintenance in the case of a breaking of a carbon fiber crossmember.

7.3.2.3 Integration Worries about the safety of a battery pack of the size being considered led to the team shifting its design strategy from using one large battery pack, which will require large amounts of cooling efforts, to integrating groups of cells into the airframe. This will make it easier and more effective to cool the batteries. Considering that there is a theoretical maximum battery weight a propeller-run VTOL aircraft of a given size can carry, spreading the batteries over the airframe will help disperse large stresses caused by such a heavy payload. This also assists in the controllability as the center of gravity and the center of lift of the aircraft will no longer be subject to sudden change because of any movement of a large battery pack. There is the safety benefit to this concept as well, because spreading the battery cells over the airframe will help isolate electronics in case of an electrical fire, while reducing the chance of such a fire by reducing the power density of the battery pack.

7.3.2.4  Struggles with Propulsion Working under the design restrictions laid out by the GoFly Competition, FlyUC for the past year and a half has attempted to find a solution to the problem of propulsion posed by such restrictions. The issue faced by the team is that of generating enough lift in a smaller-than-before designed aircraft. In addition, the vertical takeoff requirement means that the craft has to have a thrust-to-weight ratio greater than 1, which adds greater difficulty. The competition’s rules restrict the aircraft from having any dimension over 102 in., which is also the max width allowance (FHWA 1999) for street-legal vehicles in the US Federal Code. Unlike in the case of street legal vehicles, a personal aircraft needs to generate large amounts of thrust to allow for adequate lift and safe control of the aircraft.

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This creates a problem for the design of the engines, as a rather neat balance of propeller characteristics and airframe size is needed. This situation becomes more difficult to solve with the team’s decision to only use battery-run methods of propulsion. The operation of a propeller is ideal without any intake blockage. This means the flow of fluid entering the propeller must remain streamlined and not be disturbed by physical blockage or by the operation of another propeller. This is not much of a concern with conventional quadcopter designs as all four propellers are carefully tuned to not disturb the flow for another, and if the propellers are placed above the main airframe, there is no risk of the aircraft body obstructing the said flow. This has led to designs focusing on mounting a set of propellers on top of the main airframe, which has led us to consider the effects of blockage in the slipstream on the performance of the propellers. Unlike in the case of single propeller helicopters, where the design utilizes one large propeller, and the blockage created by the aircraft body is under the center of the rotating propeller and is symmetric, as well as streamlined in the case of the FlyUC designs, each of the four propellers has close to 40% of their disk area being blocked by the body in the immediate slipstream. This will cause a significant drop in the performance of the propellers on account of their ability to push air freely being affected. It will also probably cause a discontinuity of stress over the propeller disk area; that is, regions of the propeller disk with blockage in the slipstream will experience a lower mass flow rate of the fluid as compared with other regions, causing the propeller blades to cycle through regions of considerably different stresses several thousand times a minute. This will probably reduce the operational life of the propeller blades drastically. As previously mentioned, the choice of propeller, motor, and the construction of the battery pack must be tuned carefully for proper functioning of the whole system. Careful balancing is, therefore, required as the aircraft body must be able to safely fit and cool a set of batteries. The battery pack must be able to allow the propellers to generate enough thrust so as to provide the aircraft with a 2.0 thrust-to-weight ratio. A satisfactory solution will probably include either the development of customized motors or the use of electrochemical cells to which we do not currently have access.

7.3.2.5 Propellers While designing systems for personal use, it is essential to minimize the spatial dimensions of the aircraft. One solution to the concern of generating more thrust while minimizing the disk area is to use contra-rotating propellers. This design also has benefits for the controls of the aircraft as each set of propeller will have net zero torque; this can easily be exploited to make the aircraft yaw around the axis of any of the four propellers in addition to conventional yaw characteristics. Several factors (Leishman and Ananthan 2006) affect the functioning of a coaxial contra-rotating rotor, such as the separation ratio and the pitch of the propellers.

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A major struggle is the sound generated by propellers. Airports are already a significant source of noise. With personal flying machines, the number of planes will drastically increase. With thousands of these crafts in an area, the sound generated will become tremendous. Therefore, it is a requirement that the noise of these crafts be very low. We believe that further research and optimization can help reduce the noise factor to acceptable levels.

7.3.2.6 Electronics In the beginning of the design phase, the team broke down the conceptual problem of creating an electrical design in a very traditional way: to design and figure out the mechanics to make the quadcopter fly and then worry about the electrical components to power the vehicle. This method showed how that approach does not efficiently use team collaboration and in the long run causes FlyUC to see many design flaws when it is too late. The area that taught this valuable lesson was the battery pack. With the battery pack, there are many parts that interrelate to one another causing optimization of the pack on the quadcopter very difficult to manage, given the current traditional method of approach. The hardest part was optimizing the amount of cells used in the maximum space possible, while meeting the continuous current and max voltage of the motors, assuming the worst case scenario. Maintaining the power through the system to prevent overheating and other safety issues seemed impossible with the weight and space restrictions given. Not only was this concerning, but having to fit the rest of the controllers, sensors, and other devices seemed nearly impossible because the battery pack could not be efficiently designed to meet the motor and spatial needs. Along with these concerns, a proper redundancy system needed to be in place to ensure that the battery pack can safely power the motors for the whole flight duration.

7.3.2.7  Design of Battery Pack Our first step was to look at lithium-ion battery packs to see a general design of how it is constructed. Because the packs are created in a rectangular formation, the band at the top connecting each cell has a high risk of snapping when fast, increased, pressure is placed on the pack. This ruled out the option of using a pre-existing pack. Using the tesla’s patent on battery packs and cells as inspiration, the team decided to implement a honeycomb-like design to put individual cells inside, to protect the integrity of the battery pack. This, in hand, implemented a redundancy mechanism as each cell in the honeycomb structure had a wire connecting it to the larger portion of the battery pack. If one cell were to burn out, then only that cell will be compromised and not relay onto the other cells in the pack. After solidifying the structure of the pack, the cell specification was the next thing that was focused on. The method of approach that helped reduce the time to look for cells was to create a cell count script that would output the amount of cells in parallel and in series that would be needed and apply this script to various cells to gather data to see which type of cell is needed to be looked at. Gathering these data indicated a key relationship between capacity, current, and voltage. This made the question of what

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we need to prioritize in the battery pack difficult. As mentioned previously, other considerations such as space and weight also needed to be taken into account. The electronics team was given only motor specifications and design constraints, which made it difficult to find an optimal solution. While stuck at this roadblock, other designs were studied specifically, such as the Nissan Leaf’s test data to see how their car runs for long intervals of time without overheating and with minimum discharge rates. While studying the data is helpful for inspiration for the methods of approach, the design constraints of meeting power requirements for thrust and space requirements were much narrower than what Nissan had to encounter for their design of the Nissan Leaf. Although these methods led us on a less successful path, the data and optimization issue were a good learning experience.

7.4 SUMMARY The UAV or drone is a type of aircraft that does not require an onboard human pilot during flight. They are either controlled remotely from a ground control station or they are programmed to fly autonomously by utilizing onboard autopilot and navigation sensors. These systems rely on electric or combustion engine–based propulsion systems. In this chapter, we discuss various UAV technologies and their possible impacts on transportation. Design and control strategies of these systems are reviewed. We also discuss the integration of UAVs in the National Airspace System (NAS) with emerging concepts on Urban Air Mobility that focus on applications of drones such as package delivery and freight transportation, air metro, and air taxis. We briefly describe UAV traffic management and Federal Aviation Administration (FAA) regulations for UAV integration into national airspace. Technological advancements in terms of path planning and obstacle avoidance in the dynamic environment of these unmanned vehicles are also reviewed. The section on VTOL aviation discusses the importance of VTOL systems in urban areas to enable the use of flying vehicles in future transportation systems. The UAV has great potential in various applications such as the development of smart cities to cause a positive impact on society (Mohamed et  al. 2018). The UAV can be used for a multitude of applications in cities, including traffic and crowd monitoring, especially in gridlock places where traditional fixedplace monitoring technologies cannot be utilized. For example, infrastructure inspection, transportation emergency response, natural disaster monitoring, transportation and civil security control, merchandise delivery, as well as environmental and pollution monitoring (Browning 2020). However, we have to be aware of the emerging challenges during the integration of UAVs into smart cities because of issues and concerns related to safety, privacy, and ethical/legal use (Mohamed et  al. 2018). From licensing and certification issues to privacy and security qualms, there is currently no way to seamlessly integrate UAVs into

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smart cities (Browning 2020). Nonetheless, the review of the UAV and VTOL technologies provides an advanced knowledge for the readers of interest and as educational materials to supplement relevant subjects in teaching.

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Index Note: Page numbers followed by f and t indicate figures and tables. AALONS-D, ​232–233. See also self-adaptive signal controls active floating sensors, ​263, 267. See also integrated ramp and corridor control adaptive cruise control (ACC), ​81 advanced driver assistance systems (ADAS), ​87 air delivery drones, ​145–148. See also automated delivery and logistics airspace service provider (ASP), ​287 Airspace System, ​287. See also unmanned aerial vehicle traffic management air taxis. See personal aerial vehicles air traffic controller (ATC), ​285 anonymous ride-sharing. See casual carpooling Area of Interest (AOI), ​43 Area X.O, ​126, 127f. See also autonomous vehicle testing facilities artificial intelligence (AI), ​1, 14–15, 194; AI/ML tools and techniques, ​5; application, ​3–4, 5; CV/AV systems, ​5; cybersecurity threat detection and mitigation, ​13–14; data flow for traffic incident management, ​4f; data science, ​2; decentralized congestion mitigation, ​9–11; deployment of CV technology, ​15; global misbehavior detection, ​13; learning algorithm, ​3; ML/AI within edge computing framework, ​24f; narrow AI vs. general AI, ​2; reward and punishment, ​2; smart work zone management, ​ 11–12; state data, ​3; techniques for

transportation application, ​1–4; traffic control, ​6–9; transportation systems, ​4–5, 14–15; wrong-way driver detection and mitigation, ​13. See also transportation, emerging technologies in Asservissement LINeaire d’Entrée Autoroutiere (ALINEA), ​262 augmented reality (AR), ​19 Australian Unmanned Aerial Vehicle (AUAV), ​148 automated: ​fleet management, ​ 142; reverse logistics, ​142–143; warehouse management, ​142. See also last-mile transportation automated delivery and logistics, ​ 139; air delivery drones, ​145–148; applications, ​141; automated freight trucks, ​148; benefits of, ​139–141; delivery robots, ​148; drone-based automated package delivery, ​146f; flying restrictions check, ​147f; freight delivery technologies, ​ 143–145; future research directions, ​ 150–151; future technologies in, ​145; gateway facilitation, ​144; last-mile transportation, ​141–143; on-board status monitoring and control, ​144; platooning of trucks, ​149f; policy considerations, ​150; technology in, ​ 143; tracking of assets, ​143–144; traffic status information, ​144–145; unmanned delivery system, ​147f; warehouse management technology, ​ 145. See also delivery and mobility services

307

308

Index

automated driving, lower levels of, ​ 87; bus platooning, ​88; collision avoidance and emergency braking, ​ 87–88; dedicated AS lane, ​89f; managed lanes for automated shuttles, ​88; steering and lane keeping, ​88. See also autonomous vehicle design elements automated driving system (ADS), ​ 65, 71, 128; for rural America, ​ 127–129, 128f. See also autonomous vehicle testing facilities; surface transportation automation automated emergency braking (AEB), ​87 automated freight: ​ports, ​141–142; trucks, ​148. See also automated delivery and logistics; last-mile transportation automatic dependent surveillancebroadcast (ADS-B), ​291 automotive safety integrity level (ASIL), ​119 autonomous and electric vehicles, ​ 195f. See also shared vehicle services autonomous intersection control, decentralized, ​238f. See also signal-free autonomous intersection control Autonomous Intersection Management (AIM), ​235; BATCH of reservation in AIM, ​236; DCLAIM, ​237. See also cooperative and automated traffic control autonomous micro transit (AMT), ​ 77. See also surface transportation automation autonomous shuttle (AS), ​69, 70; deployment of, ​71; made by NAVYA, ​70f; as micro transit, ​77; multicity deployments of, ​ ​​76f; NAVYA’s Autonom Shuttle Evo technology, ​70f; operation design domain, ​71; transit bus automation, ​

72t–75t. See also Society of Automobile Engineers automation levels; surface transportation automation autonomous transit (AT), ​70. See also surface transportation automation autonomous truck research, ​131 autonomous vehicle (AV), ​63, 105, 214; in compliance with, ​67–68; internal data, ​113; planning, ​289; regulations in United States, ​97f; systems, ​105; technology, ​105, 106f, 106–107. See also autonomous vehicle testing; driving automation; surface transportation automation; unmanned aerial system path planning autonomous vehicle design elements, ​ 83; avigation, ​85–86; behavior architecture, ​86–87; command and control, ​86; drive-by-wire system, ​83; health monitoring, ​ 86; localization, ​86; lower levels of automated driving, ​87–88, 89f; perception, ​84, 85f; world model, ​ 87. See also surface transportation automation Autonomous Vehicles 3. 0 (AV 3. 0), ​68 autonomous vehicle testing, ​105, 106–107, 134–135; analysis frameworks, ​119; AV systems, ​ 105; AV technology, ​105, 106f, 107; combined system testing, ​ 115–116; complete vehicle testing, ​ 116–117; current deployments, ​ 130–131; DOT-approved AV proving grounds, ​123–124; driving tasks, ​107; DSRC, ​107; engine and drive train, ​109; issues for future deployment, ​132–134; mechanical testing, ​107–109; Optimus Ride project 2017 Q3, ​116; impact of policies on, ​131–132; real-world strategies, ​118; safety systems, ​

Index

108–109; simulated vs. real-world testing, ​118; simulation-based strategies, ​118; software and cyber security data testing, ​109–115; software simulation, ​119–123; system of systems testing, ​117; testing areas, ​134; testing facilities, ​ 125–129; version testing, ​117–118 autonomous vehicle testing facilities, ​ 125; Area X.O, ​126, 127f; automated driving systems for rural America, ​127–129; Curiosity Lab, ​ 129–130, 130f; GoMentum Station, ​ 126–127, 127f; Mcity testing facility, ​ 125–126, 125f; NADS and ADS for Rural America, ​128f; SunTrax facility, ​129f; Transportation Research Center, ​126f; upcoming testing facilities, ​129. See also autonomous vehicle testing avigation technologies of autonomous vehicles, ​85–86. See also autonomous vehicle design elements barcoding system, ​145 BATCH of reservation in AIM, ​236. See also signal-free autonomous intersection control battery electric vehicle (BEV), ​229 Bay Area Rapid Transit (BART), ​209 bike-sharing programs, ​204. See also shared bicycle service biometric identification tools, ​144 Bitcoin, ​91. See also distributed ledger technology BOTTLENECK, ​262 bridge monitoring, ​54. See also Internet of Things business-owned operated transportation services, ​172 business-to-business (B2B), ​172, 173. See also mobility on demand business-to-consumer (B2C), ​172. See also mobility on demand

309

business to government (B2G), ​173. See also mobility on demand bus rapid transit (BRT), ​42, 88 California Partners for Advanced Transportation Technology (CPATT), ​82 CARLA, ​122. See also software simulation CARMA Platform, ​95. See also transportation automation technologies carpool. See ride-sharing carsharing, ​173, 190, 212–213; insurance and taxes, ​192–193; interdependency of, ​195f; market in United States, ​213f; parking regulations, ​191–192; peerto-peer, ​190; personal vehicle sharing models, ​190–191; policy considerations, ​191; service models, ​ 190–193; services, ​212. See also first mile/last mile; shared vehicle services CarSim, ​122. See also software simulation casual carpooling, ​186. See also shared vehicle services CAV. See connected and automated vehicles cave automatic virtual environment (CAVE), ​35 cellular networking, ​53 cellular technologies, ​27 cellular V2X (C-V2X), ​31, 67, 107; within 5G vision, ​31f. See also fifth-generation innovative communications technology; 5G cellular V2X center-to-field (C2F), ​17 centralized intersection traffic control, ​ 235–237, 235f. See also signal-free autonomous intersection control clockwise (CW), ​280

310

Index

Cloud Computing, ​15; advantages of, ​ 17; Edge Computing and, ​21–22. See also Edge Computing Cloudlet, ​18–19. See also Edge Computing coaxial multirotor platform, ​281f combined system testing, ​115–116. See also autonomous vehicle testing command and control (C&C), ​83 complete vehicle testing, ​116–117. See also autonomous vehicle testing computer-generated sensory inputs, ​36 conditional automation, ​99. See also driving automation congestion mitigation, decentralized, ​9. See also transportation, emerging technologies in Congressional Research Service (CRS), ​132 connected and automated vehicle (CAV), ​1, 15, 28, 154; applications, ​ 92–94; -based traffic control, ​ 223–226, 224t–225t; distributed ledger technologies for, ​89–92; and internet of things, ​56; technologies, ​ 223. See also transportation automation technologies connected and automated vehicles (CV/AVs), ​3, 5, 25; computing power at traffic signal intersections, ​ 26t; eco-driving control, ​256– 260; edge computing and CAV applications, ​27f; edge computing impact on, ​25; 5G data service impact, ​31–32; technologies, ​63. See also Edge Computing; surface transportation automation Connected eco-driving system, ​82, 83f connected transit signal priority, ​93f. See also transportation automation technologies connected vehicle (CV), ​65, 92, 174; applications, ​92; communication technologies, ​65; in compliance

with, ​65–67; concept, ​66f; platooning, ​93f; safety-critical applications, ​67; technology, ​15. See also surface transportation automation; transportation automation technologies connected vehicle reference implementation architecture (CVRIA), ​223 connected vehicle-supported systems, ​ 77; CACC system, ​81, 82f; collective CV-supported system application, ​ 78f; concept of EEBL, ​78f; concept of FCW, ​79f; eco-driving, ​83f; environment systems, ​82–83f; INFLO bundle, ​79–80; mobility systems, ​79–81; Q-WARN, ​80, 81f; safety systems, ​77–79; SPDHARM, ​80, 81f; work-zone traffic, ​ 81. See also surface transportation automation Cooperative Adaptive Cruise Control (CACC), ​81, 258; system, ​81, 82f. See also connected vehiclesupported systems cooperative and automated traffic control, ​223, 267–268; eco-driving and traffic control, ​251–260; evaluation of CAV-based traffic control approaches, ​226f; integrated ramp and corridor control, ​260– 267; safe interactions of pedestrians/ cyclists with AV, ​247–251; selforganized intelligent adaptive traffic control, ​226; traffic signal control methods, ​223–226, 224t–225t Cooperative Coordinated Adaptive Corridor Signal Timing Optimization (CCACSTO), ​240; algorithms, ​240, 247; calculating spacing and headway from timespace diagram, ​242; coordinated adaptive traffic signal optimization for arterial progression, ​244f; deceleration points rearranged

Index

in descending order, ​243–244; dynamic programming for offset optimization, ​245–247; flow rate, ​ 242; identification of deceleration and acceleration points within queue, ​243; modeling framework for self-organized intelligent traffic control, ​241f; modeling traffic flow parameters, ​242; offsets and coordinated intersections at arterial, ​246; optimization model formulation, ​244–245; queue length, ​243; queue size, ​243–244; research methodology, ​240, 241f; Wei-Zone, ​241, 244. See also cooperative and automated traffic control; self-organized intelligent adaptive traffic control cooperative driving automation (CDA), ​87, 88, 95–96. See also transportation automation technologies Cooperative Intelligent Transport System (C-ITS), ​259 cooperative platooning, ​93. See also transportation automation technologies cooperative vehicles with automation, ​ 68–69. See also Society of Automobile Engineers automation levels counterclockwise (CCW), ​280 courier network services (CNS),  ​170, 176 coworker carpools. See fampools cryptographic architecture, ​99 Curiosity Lab, ​129–130, 130f. See also autonomous vehicle testing facilities cyber data testing, ​113–114. See also software and cyber security data testing cyber-physical system, ​143, 226. See also self-organized intelligent adaptive traffic control

311

cybersecurity, ​112–113; threat detection and mitigation, ​13–14. See also software and cyber security data testing; transportation, emerging technologies in data science, ​2. See also transportation, emerging technologies in data security, ​194. See also shared vehicle services DCL-AIM, ​237. See also signalfree autonomous intersection control decentralized autonomous intersection control, ​238f. See also signal-free autonomous intersection control decentralized congestion mitigation, ​9; centralized intelligence and management model, ​9; USDOT connected vehicle speed harmonization, ​ 10f; vehicle platooning, ​11. See also transportation, emerging technologies in decentralized Nash bargaining traffic signal controller (DNB), ​239 decentralized traffic control, ​237–239. See also signal-free autonomous intersection control decision support systems (DSS), ​1 dedicated short-range communication (DSRC), ​33, 106, 107 delivery and mobility services, ​139, 177–178; automated delivery and logistics, ​139; Mobility as a Service, ​ 151–165, 152f; mobility on demand, ​ 165–177 delivery robots, ​148. See also automated delivery and logistics delivery services, ​139 design simulation, ​120. See also software simulation

312

Index

detect-and-avoid (DAA) system, ​286, 291–292. See also urban air mobility distributed ledger technology (DLT), ​89, 99; applications, ​91; Bitcoin, ​91; for connected and autonomous vehicle systems, ​ 89–92; cryptographic signatures, ​ 89; shared and immutable ledger of transactions, ​91; Smart Contract, ​ 90f; in transportation, ​92. See also surface transportation automation dockless shared bikes, ​204. See also shared bicycle service DOT-approved AV proving grounds, ​ 123–124. See also autonomous vehicle testing drive-by-wire system, ​83. See also autonomous vehicle design elements driver: ​assistance, ​97; simulation software packages, ​122. See also driving automation driving: ​autonomy, ​98t; model, ​ 110–112; simulation, ​36f, 41f, 121–122; tasks, ​107. See also driving automation; software and cyber security data testing; software simulation; virtual reality–based driving simulation driving automation, ​97; AV regulations in United States, ​97f; conditional automation, ​99; driver assistance, ​97; driving autonomy, ​ 98t; partial automation, ​99; SAE International, ​97; self-driving, ​97. See also surface transportation automation drone: ​air delivery drones, ​145–148; -based automated delivery, ​145, 146f; electric drones, ​145; hybrid drones, ​145. See also automated delivery and logistics; unmanned aerial vehicle Drone Delivery Canada (DDC), ​146 dynamic driving task (DDT), ​97

dynamic message signs (DMS), ​12 dynamic programming (DP), ​245 eco-driving, ​83f. See also connected vehicle-supported systems eco-driving and traffic control, ​ 251; eCoMove solutions for fuel wastages, ​254f; eco-signal control, ​ 251, 253–256; engine restart method, ​260; fuel consumption vs. types of fuel and speed, ​ 255f; mechanism of eco-signal control, ​256f. See also cooperative and automated traffic control; eco-driving eco-driving control, ​256; data-based Bayesian approach, ​259; dynamic, ​ 258–260; dynamic feedback, ​259; ego vehicle, ​260; Pontryagin’s Minimum Principle, ​258; of traffic, ​253–254; using uncertain signal timing, ​ 256–257; uncertainties of smart systems, ​257f; using V2X-driven signal control, ​257–260. See also eco-driving and traffic control eCoMove solutions for fuel wastages, ​ 254f. See also eco-driving and traffic control eco-signal control, ​251, 253–256; mechanism of, ​256f. See also ecodriving and traffic control Edge Computing, ​15, 16, 25, 27; architecture, ​20f, 24; building decentralized ITS infrastructure, ​ 24; and CAV applications, ​27f; CAV vehicles, ​25; and Cloud Computing, ​15, 17, 21–22; Cloudlet, ​18–19; comparison of, ​ 21t; demand of, ​17–18; demand on existing transportation infrastructure, ​16–17; existing computing power at traffic signal intersections, ​26t; Fog Computing, ​15, 19–20;

Index

and 5G, ​22–23; impact on CAV vehicle roadside infrastructure migration, ​25; and innovation technologies, ​23f; and Internet of Things, ​22, 53–54; ML/AI within Edge Computing framework, ​ 24f; Mobile Edge Computing, ​ 19; technologies, ​18, 21, 22f; traditional ITS TMC solution diagram, ​17f; transportation scenarios of applying, ​23–24. See also transportation, emerging technologies in ego motion, ​86 electric drones, ​145 electric vehicle (EV), ​94, 196, 210; Blockchain application, ​94f. See also shared vehicle services; transportation automation technologies electroencephalogram (EEG), ​42 emergency electronic brake lights (EEBL), ​77, 78f. See also connected vehicle-supported systems engine restart method, ​260. See also eco-driving and traffic control enhanced mobile broadband (eMBB), ​22 environment simulation, ​122–123. See also software simulation ERTICO, ​152. See also Mobility as a Service European Commission’s Horizon 2020 program–funded projects, ​ 160. See also Mobility as a Service European Telecommunications Standards Institute (ETSI), ​19 European Union (EU), ​160 EV Blockchain application, ​94f. See also transportation automation technologies explainable artificial intelligence (XAI), ​68 eye tracking (ET), ​41

313

fampools, ​186. See also shared vehicle services FASTLinkDTLA, ​161. See also Mobility as a Service Federal Aviation Administration (FAA), ​146, 280, 299; airspace regulations for UAVs, ​287–288; rules for small UAVs, ​287. See also urban air mobility Federal Communications Commission (FCC), ​33, 67 Federal Highway Administration (FHWA), ​95 Federal Transit Administration (FTA), ​71; transit bus automation demonstrations,  ​72t–75t. See also surface transportation automation field of view (FOV), ​87 5th-Generation (5G), ​19 fifth-generation innovative communications technology, ​27, 34; challenges in United States with 5G cellular V2X, ​33–34; impact of continuous evolution on 5G standards, ​32–33; C-V2X within 5G vision, ​31f; data services, ​28–30; Edge Computing and, ​22–23; enhanced mobile broadband service impact, ​ 30; features of, ​29t; impact on connected and automated vehicle migration, ​31–32; impact on smart transportation infrastructure enhancement, ​ 30–31; massive machinetype communications service impact, ​30–31; testing and demonstration of cellular V2X, ​ 33; ultrareliable and low-latency communications, ​31. See also transportation, emerging technologies in first-come-first-served (FCFS), ​236

314

Index

first mile/last mile (FM/LM), ​198, 207; carsharing market size in United States, ​213f; common transportation used for connecting, ​ 208–209; innovative motilities, ​211; integration between public transit and feeder modes, ​210–211; landuse planning, ​209; Microtransit, ​ 213–214; network connectivity, ​ 210; parking and electric charging facilities, ​210–211; pilot Pass2Go, ​ 214, 215f; ride-sharing and carsharing, ​212–213; seamless FM/ LM connectivity, ​216–217; shared micromobility, ​211–212; shared mobility service models, ​211f; solutions, ​ 207–216; strategies, ​ 209–214; technologies powering FL/LM connection, ​214–216; TOD communities, ​209; Waymo’s AV units, ​214, 215f. See also shared sustainable mobility 5GAA (5G automotive alliance), ​33 5G cellular V2X: ​challenges in United States with, ​33–34; testing and demonstration of, ​33. See also cellular V2X; fifth-generation innovative communications technology fixed-wing UAVs, ​290. See also unmanned aerial system path planning fixed wireless access (FWA), ​30 fleet management, automated, ​142. See also last-mile transportation floating sensors, ​263; active, ​263, 267. See also integrated ramp and corridor control FlyUC, ​295, 296 Fog Computing, ​15, 19–20. See also Edge Computing forward collision warning (FCW), ​ 77, 79f. See also connected vehiclesupported systems 4th Generation (4G), ​27

frames per second (FPS), ​37 free shared bicycle programs, ​201. See also shared bicycle service freight delivery, ​143–145; gateway facilitation, ​144; on-board status monitoring and control, ​144; technology used in warehouse management, ​145; tracking of assets, ​143–144; traffic status information, ​144–145. See also automated delivery and logistics freight ports, automated, ​141–142. See also last-mile transportation General Motors (GM), ​84 Genetic Algorithm (GA), ​258, 289 global misbehavior detection, ​13. See also transportation, emerging technologies in global positioning system (GPS), ​106, 139, 174, 194 GoMentum Station, ​126–127, 127f. See also autonomous vehicle testing facilities graphical user interface (GUI), ​241 guidance, navigation, and control (GNC), ​295 hardware-in-the-loop (HIL), ​118; simulation, ​121. See also software simulation head-mounted displays (HMD), ​35; motion sickness in, ​45 high-end game engines, ​39 high-occupancy vehicle (HOV), ​190 hybrid drones, ​145 iMOVE, ​160. See also Mobility as a Service INFLO bundle, ​79–80. See also connected vehicle-supported systems information and communications technology (ICT), ​153, 174, 204 infrastructure-to-network (I2N), ​32

Index

input and output (I/O), ​121 INRIX, ​144 integrated digital platform, ​214 integrated ramp and corridor control, ​ 260; active floating sensors, ​263, 267; CAV technology, ​263; conceptual methodology for, ​ 263–267; first priority objective, ​ 264–265; floating sensors, ​263; methodology flowchart, ​263, 264f; model-based and non-modelbased algorithms, ​261–262; ramp metering algorithms, ​262; ramp metering control algorithms, ​261, 266f; ramp-metering control systems, ​261; ramp metering operation and simulated system, ​ 261f; ramp metering technologies, ​ 260–263; second priority objective, ​ 265; speed and density, ​263–264; third objective, ​266–267; vehicledetection technologies, ​263. See also cooperative and automated traffic control intelligent network flow optimization (INFLO), ​79 intelligent roundabout (IR), ​239–240; concept of operation of, ​239f. See also signal-free autonomous intersection control intelligent transportation systems (ITS), ​12 Internet of Things (IoTs), ​22, 46, 140; application domains, ​48f, 51, 53; application layer protocols, ​ 52t; applied IoT technologies, ​ 46; architecture, ​47f; bridge monitoring by, ​54; cloud platforms comparison, ​50t; communication technologies and protocols, ​47–48, 49t; connected and automated vehicles and, ​56; Edge Computing, ​ 22, 53–54; impact of 5G migration, ​ 53; licensed radio technologies, ​48; linking with other technologies, ​53;

315

living bridge project, ​55f; sensors, ​ 51; smart city and, ​55f; smart city and ITS applications with, ​ 54–56; standardization migration of, ​48–54; supporting cloud services and application layer protocols, ​51; technologies, ​46–47; transportation infrastructure monitoring & asset management by, ​54; transportation scenarios of applying internet of things, ​54–56; unlicensed radio technologies, ​47. See also transportation, emerging technologies in IRIS, ​160–161. See also Mobility as a Service Karlsruhe Transport Authority (KVV), ​160 lane keep assist (LKA), ​87 last-mile transportation, ​141; automated fleet management, ​142; automated freight ports, ​141–142; automated reverse logistics, ​ 142–143; automated warehouse management, ​142. See also automated delivery and logistics latency-sensitive functions, ​25 learning algorithm, ​3. See also transportation, emerging technologies in licensed radio technologies, ​48. See also Internet of Things life cycle cost analysis, ​150 light detection and ranging (LiDAR), ​ 84, 106 Lindholmen Integrated Mobility Arena, ​161. See also Mobility as a Service living bridge project, ​55f logistics, ​139. See also automated delivery and logistics long-term evolution (LTE), ​27, 50 low-power wide area (LPWA), ​30, 53

316

Index

MaaS Alliance, ​152. See also Mobility as a Service MaaS Global, ​159. See also Mobility as a Service machine learning (ML), ​1, 3f, 14–15; AI/ML tools and techniques, ​5; application-specific use cases, ​3–4; CV/AV systems, ​5; cybersecurity threat detection and mitigation, ​13–14; data science, ​2; decentralized congestion mitigation, ​ 9–11; deployment of CV technology, ​ 15; global misbehavior detection, ​ 13; illustrative data flow for traffic incident management, ​4f; learning algorithm, ​3; ML/AI within edge computing framework, ​24f; smart work zone management, ​11–12; state data, ​3; system of reward and punishment, ​2; techniques for transportation application, ​1–4; traffic control, ​6–9; transportation systems, ​14–15; transportation systems management and operation, ​ 4–5; use cases, ​5; wrong-way driver detection and mitigation, ​13. See also transportation, emerging technologies in machine-to-machine (M2M), ​46 Managed Infrastructural Network for Demand of CAV (MINDCAV), ​250 Massachusetts Bay Transportation Authority (MBTA), ​210 massive machine-type communication (mMTC), ​23, 28, 50, 53; service impact, ​30–31 Mcity testing facility, ​125–126, 125f. See also autonomous vehicle testing facilities METALINE, ​262 Metropolitan Area Planning Council (MAPC), ​150 Metropolitan Atlanta Rapid Transit Authority (MARTA), ​213

microtransit, ​193–194, 213–214. See also first mile/last mile; shared vehicle services mixed-integer linear programming (MILP), ​290 Mobile Edge Computing (MEC), ​18, 19. See also Edge Computing Mobility as a Service (MaaS), ​71, 151, 152f, 197; advantage of, ​154; application of technologies in, ​ 162–164; CAV and electric vehicle, ​ 154; in context of smart cities, ​ 153–154; core characteristics of, ​ 155–156; ecosystem elements, ​ 157–159; elements of MaaS service, ​ 162–163; ERTICO, ​152; European Commission’s Horizon 2020 program–funded projects, ​160; FASTLinkDTLA, ​161; iMOVE, ​ 160; implementation features of, ​ 154–159; initiatives around world, ​ 159–162; integration, ​156–157; IRIS, ​160–161; Lindholmen Integrated Mobility Arena, ​161; MaaS Alliance, ​152; MaaS Global, ​ 159; mobile apps, ​163–164; Moovel, ​159; potential research areas, ​164–165; sensor-based IoT technology, ​153; smart city, ​153; TfGM MaaS study, ​160; topology levels, ​157t; UbiGo, ​161; urban mobility applications, ​154. See also delivery and mobility services Mobility as a Service ecosystem elements, ​157; data provider, ​158; infrastructure, ​158; transportation operators, ​158; trusted mobility advisor, ​159. See also Mobility as a Service mobility on demand (MOD), ​165; business models, ​172–174; business-to-business, ​173; businessto-consumer, ​172; business to government, ​173; contribution in shared mobility ecosystem, ​175–176;

Index

demand side of, ​171–172; ecosystem, ​ 171f; existing transportation services, ​ 169; future research direction, ​ 176–177; future research focus areas, ​176f; for goods delivery, ​166; importance of, ​167–172; mobility needs, ​167–168; peer-to-peer delivery marketplace, ​173–174; peerto-peer mobility marketplace, ​173; Sandbox Demonstration Program, ​ 166; shared mobility ecosystem, ​ 175f; sharing economy, ​168; supply side of, ​170; technologies enabling, ​ 174–175; travel behaviors, ​168–169; vision of, ​169. See also delivery and mobility services mobility patterns, ​195. See also shared vehicle services mobility smart contracts, ​94–95. See also transportation automation technologies model in the loop (MIL) simulation, ​121 Moovel, ​159. See also Mobility as a Service motion sickness in HMD, ​45 multirotor aircraft designs, ​278–279. See also unmanned aerial vehicle Narrow Band-Internet of Things (NB-IoT), ​50. See also Internet of Things National Advanced Driving Simulator (NADS), ​127; and ADS for rural America, ​128f. See also autonomous vehicle testing facilities National Aeronautics and Space Administration (NASA), ​278, 280 National Airspace System (NAS), ​ 284, 299 National Highway Traffic Safety Administration (NHTSA), ​63 National Instruments’ LabVIEW software, ​121. See also software simulation

317

NAVYA, ​70; Autonom Shuttle Evo technology, ​70f; exemplary AS made by, ​70f; multicity deployments of NAVYA’s AS in Florida, ​76f. See also Society of Automobile Engineers automation levels network fundamental diagram (NFD), ​239 neural networks (NNs), ​68 nonprofit organizations (NGOs), ​201 onboard units (OBUs), ​65 one-touch make-ready (OTMR), ​132 operational design domain (ODD), ​71 Optimus Ride project, ​111, 116. See also autonomous vehicle testing; software and cyber security data testing original equipment manufacturers (OEMs), ​67 origin and destination (O–D), ​186 partial automation, ​99. See also driving automation particle swarm optimization (PSO), ​ 289 pedestrian-to-network (P2N), ​32 peer-to-peer (P2P), ​172; carsharing, ​ 190; delivery marketplace, ​173–174; mobility marketplace, ​173. See also mobility on demand perception technologies of autonomous vehicles, ​84, 85f. See also autonomous vehicle design elements personal aerial vehicles (PAVs), ​284; in urban environment, ​278. See also unmanned aerial vehicle pilot Pass2Go, ​214; trip options in Pass2Go, ​215f. See also first mile/ last mile platooning, ​69, 88; bus, ​88; connected vehicle, ​93f; cooperative, ​ 93; of trucks, ​149f; vehicle, ​11

318

Index

Pontryagin’s minimum principle (PMP), ​237 pre-emption, ​93. See also transportation automation technologies Priority Attribution Methods, ​237. See also signal-free autonomous intersection control probabilistic roadmap method (PRM), ​ 289 protected urban network (PN), ​239 public–private partnership (PPP), ​161 public transit (PT), ​190; agencies, ​ 212; services, ​77

reverse logistics, ​142; automated, ​ 142–143. See also last-mile transportation reward and punishment system, ​2. See also transportation, emerging technologies in ride-hailing services, ​193. See also shared vehicle services ride-sharing, ​186, 212–213; casual carpooling, ​186; compensation mechanism, ​188–189; policy considerations, ​186, 189–190; pricing, ​186, 188; safety considerations, ​189; service models, ​ 186, 187f. See also first mile/last quadcopter, ​279; conventional mile; shared vehicle services quadcopter configurations, ​280f; ride-splitting services, ​193. See also design, ​279f; flight operations of, ​ shared vehicle services 283; rotational motion around body roader users and automated axes of, ​281; tilt-rotor, ​282–283; vehicles, ​247; communications variable blade pitch, ​281–282. with pedestrians, ​251, 252t, 253f; See also unmanned aerial vehicle computing system for AV, ​247; Q-WARN, ​80, 81f. See also connected considerations of transition effect, ​ vehicle-supported systems 248–249; interactions between, ​ 250–251; pedestrian and cyclist radio detection and ranging reactions to AVs, ​249–250. See also (Radar), ​106 cooperative and automated traffic radio-frequency identification (RFID), ​ control 143, 145 roadside equipment (RSE), ​13, 263 radio-frequency transponders, ​144 roadside unit (RSU), ​18, 65, 239 ramp-metering control systems, ​ Robot Operating System (ROS), ​96 261. See also integrated ramp and robust UTM system, ​291. See also corridor control urban air mobility ramp metering technologies, advanced, ​ 260–263. See also integrated ramp Sacramento Regional Transit and corridor control (SacRT), ​210 Rapidly Exploring Random Tree Sandbox Demonstration Program, ​ (RRT), ​289 166. See also mobility on demand real-time information (RTI), ​194 seamless FM/LM connectivity, ​ real-time rendering engines, ​39 216–217. See also first mile/last mile real world: ​simulation, ​36–37; security credential management strategies, ​118. See also autonomous system (SCMS), ​96 vehicle testing; virtual reality–based security critical computer data, ​113. driving simulation See also software and cyber security Release X, ​27 data testing

Index

self-adaptive control phenomenon, ​ 232. See also self-organized intelligent adaptive traffic control self-adaptive signal controls, ​232; AALONS-D, ​232–233; genetic algorithms, ​233; reinforcement learning, ​233; sustainable controls, ​ 234; TRANSYT-7F, ​233; video imaging, ​233–234. See also selforganized intelligent adaptive traffic control self-adaptive traffic control system, ​ 228f; conceptual interactions of, ​230f; self-adaptive control phenomenon, ​ 232. See also self-organized intelligent adaptive traffic control self-driving, ​97. See also driving automation self-organized intelligent adaptive traffic control, ​226; CCACSTO, ​ 240–247; cyber-physical system, ​ 226; intersections governed by traffic signals, ​227f; optimizing traffic signals, ​230–232; selfadaptive signal controls, ​232–234; signal-free autonomous intersection control, ​234–240; signal offset, ​ 231–232; system elements, ​229–230; V2X systems, ​229. See also selfadaptive traffic control system sense-and-avoid (SAA), ​291 sensor: ​-based IoT technology, ​153; interfaces, ​112; internet of things, ​51 service-oriented architecture (SOA), ​17 shared autonomous electric vehicles (SAEV), ​196 shared autonomous vehicles (SAVs), ​ 195, 196. See also shared vehicle services shared bicycle service (SBS), ​197; advantages of, ​198; within autooriented urban structure, ​206–207; bike-sharing programs, ​204;

319

dimensions of, ​198f; dockless shared bikes, ​204; engineering issues, ​204–205; first generation of, ​200–201; four generations of, ​ 200f; fourth generation, ​203–204; free shared bicycle programs, ​ 201; generations of SBS operation models, ​199f; operation, ​200–204; second generation, ​201–202; stakeholders in planning, ​205–206; sustainable transportation, ​197; third generation, ​202–203; urban planning issues, ​205. See also shared sustainable mobility shared micromobility, ​211–212, 211f. See also first mile/last mile shared mobility, ​175; ecosystem, ​ 175f. See also mobility on demand shared sustainable mobility, ​185, 216–217; first mile/last mile solutions, ​207–216; shared bicycle service, ​197–207; shared vehicle services, ​185–197 shared vehicle services, ​185; autonomous and electric vehicles, ​ 195f; benefits of, ​185; carsharing service models, ​190–193; data security, ​194; electric vehicles, ​196; electrification and automation, ​194, 196; future research directions, ​ 197; interdependency of carsharing services, ​195f; microtransit, ​ 193–194; mobility patterns, ​195; models, ​185; ride-hailing services, ​ 193; ride-sharing service models, ​ 186, 187f; ride-splitting services, ​ 193; shared autonomous vehicles, ​ 195, 196; sharing economy, ​185; and transformed mobility patterns, ​ 185–194; use of technology in, ​ 194–197. See also shared sustainable mobility sharing economy, ​168, 185. See also mobility on demand; shared vehicle services

320

Index

shooting heuristic (SH), ​237 signal-free autonomous intersection control, ​234; BATCH of reservation in AIM, ​236; centralized intersection traffic control, ​235–237, 235f; DCL-AIM, ​237; decentralized autonomous intersection control, ​ 238f; decentralized traffic control, ​ 237–239; intelligent roundabout, ​ 239–240, 239f; Priority Attribution Methods, ​237; WEI’s CAV Flows Modeling Framework, ​240. See also self-organized intelligent adaptive traffic control signal offset, ​231–232. See also selforganized intelligent adaptive traffic control signal phase and timing (SPaT), ​6, 7f, 8, 240 signal-vehicle coupled control (SVCC), ​223 simulation-based strategies, ​118. See also autonomous vehicle testing slugging. See casual carpooling small unmanned aerial system (sUAS), ​284 small unmanned aircraft systems (sUAS), ​286 smart: ​contract, ​90f, 94; work zone, ​11. See also distributed ledger technology; transportation automation technologies smart city, ​54–56, 55f, 153; applications, ​19. See also Mobility as a Service smart mobility. See Mobility as a Service smartphone, ​162 smart work zone management, ​11; ITS devices, ​12. See also transportation, emerging technologies in Society of Automobile Engineers (SAE), ​63; automation levels, ​64t;

International, ​97. See also driving automation; surface transportation automation Society of Automobile Engineers automation levels, ​63–65; autonomous shuttle, ​69, 77; autonomous vehicle, ​67–68; connected vehicle, ​65–67; cooperative vehicles with automation, ​68–69; exemplary AS, ​ 70f; multicity deployments of AS in Florida, ​76f; NAVYA’s Autonom Shuttle Evo technology, ​70f; platooning, ​69; SAE automation levels, ​64t; transit bus automation, ​ 72t–75t. See also surface transportation automation Society of Automotive Engineers (SAE), ​88 software and cyber security data testing, ​109; AV internal data, ​ 113; cyber data testing, ​113–114; cybersecurity, ​112–113; driving model, ​110–112; entry points for autonomous vehicle hacking, ​ 114f; innocuous entry points, ​114; Optimus Ride project in Boston, ​ 111; security critical computer data, ​113; sensor interfaces, ​112; system design errors, ​114–115; system of software systems testing, ​ 114–115. See also autonomous vehicle testing software-in-the-loop (SIL), ​118; simulation, ​120–121. See also software simulation software simulation, ​119; CARLA, ​122; CarSim, ​122; design simulation, ​120; driving simulator, ​121–122; environment simulation, ​122–123; hardware in the loop simulation, ​121; National Instruments’ LabVIEW software, ​ 121; simulation and testing

Index

regimes, ​120f; software in the loop simulation, ​120–121; virtual reality–based simulation, ​123; VISSIM, ​122. See also autonomous vehicle testing SPD-HARM, ​80, 81f. See also connected vehicle-supported systems state data, ​3 stationary charging location, ​95. See also transportation automation technologies Stuttgarter Straßenbahnen (SSB), ​159 SunTrax facility, ​129f. See also autonomous vehicle testing facilities supply chain, ​139 surface transportation automation, ​ 63, 99–100; application of transportation automation technologies, ​92–96; CV-supported systems, ​77–83; design of autonomous vehicles, ​83–88; distributed ledger technologies, ​ 89–92; driving automation and autonomous vehicle laws, ​97–99; vehicles in compliance with society of automobile engineers automation levels, ​63–77 sustainable transportation, ​197. See also shared bicycle service swashplate system, ​293. See also vertical takeoff and landing aviation system of systems testing, ​117. See also autonomous vehicle testing systemwide adaptive ramp metering (SWARM), ​262 tamper prevention method, ​144 technology capability levels (TCLs), ​285 testing areas, ​134 TfGM MaaS study, ​160. See also Mobility as a Service

321

3rd Generation Partnership Project (3GPP), ​27 third-party app platforms, ​159 time to arrival (TTA), ​247 total delay (TD), ​247 traffic constraints, ​227 traffic control, ​6; adaptive signal timing, ​6; AI/ML solution, ​8; connected vehicles utilizing SPaT, ​7f; multiagent systems engineering, ​8; reinforcement learning, ​9; sensors, ​6. See also transportation, emerging technologies in traffic incident management (TIM), ​ 11–12 Traffic Management Center (TMC), ​4 Traffic Operation Center (TOC), ​17 traffic signal control methods, ​223– 226, 224t–225t; evaluation of CAVbased traffic control approaches, ​ 226f. See also cooperative and automated traffic control Transit Authority of River City (TARC), ​161 transit-oriented developments (TODs), ​209 Transportation as a Service. See Mobility as a Service transportation automation technologies, ​92; application of, ​ 92–96; CARMA Platform, ​95; connected and automated vehicle applications, ​92–94; connected transit signal priority, ​93f; connected vehicle platooning, ​93f; cooperative driving automation, ​ 95–96; cooperative platooning, ​93; CV applications, ​92; EV Blockchain application, ​94f; mobility smart contracts, ​94–95; pre-emption, ​93; security considerations, ​96; smart contract, ​94; stationary charging location, ​95. See also surface transportation automation

322

Index

transportation, emerging technologies in, ​1; applied internet of things in transportation, ​46–56; artificial intelligence and machine learning, ​1; Cloud Computing, ​15, 25, 27; decentralized ITS infrastructure, ​24; Edge Computing technologies, ​15, 18–27; fifth-generation innovative communication technology, ​27–34; Fog Computing, ​15, 25, 27; virtual reality–based driving simulation, ​ 35–45 Transportation Management Center (TMC), ​17 transportation modes, ​42 Transportation Network Companies (TNCs), ​193 Transportation Research Center (TRC) facility, ​126f. See also autonomous vehicle testing facilities transportation systems, ​14–15; management and operation, ​4–5. See also transportation, emerging technologies in transportation systems management and operation (TSMO), ​4–5 Transport for Greater Manchester (TfGM), ​160 TRANSYT-7F, ​233. See also selfadaptive signal controls two-dimensional barcodes, ​145 UbiGo, ​161. See also Mobility as a Service ultrareliable and low-latency communications (uRLLC), ​23, 29; service impact, ​31 University of Cincinnati (UC), ​36, 295 University of Michigan (UM), ​125 unlicensed radio technologies, ​47. See also Internet of Things unmanned aerial system path planning, ​288; algorithms, ​290; bioinspired algorithms for, ​289–290; combinatorial approaches, ​289;

fixed-wing UAVs, ​290; GA-based path-planning algorithm, ​290; higher altitudes in airspace, ​ 290–291; roadmap techniques, ​289; sampling-based algorithms for, ​289. See also urban air mobility unmanned aerial vehicle (UAV), ​ 165, 277, 299–300; air taxi in urban environment, ​278; blade pitch propeller design, ​282f; coaxial multirotor platform, ​ 281f; conventional quadcopter configurations, ​280f; multiple UAVs, ​ 280; multirotor aircraft designs, ​ 278–279; multirotor design and technologies, ​280–284; quadcopter, ​ 279–283; tilt-rotor schematic, ​283f; tilt-rotor UAV diagram, ​282f; traffic management, ​284–287; unmanned aircraft history and scope, ​278–280; VTOL capability, ​277. See also urban air mobility; vertical takeoff and landing aviation unmanned aerial vehicle traffic management (UTM), ​284; Airspace System, ​287; current system, ​285; NASA, ​285; structure, ​286, 286f; technology capability levels, ​285 unmanned aircraft, ​278–280. See also unmanned aerial vehicle unmanned delivery systems, ​177; multilevel fulfillment center for, ​147f urban air mobility (UAM), ​284, 292–293; detect-and-avoid systems, ​291–292; Federal Aviation Administration regulations, ​ 287–288; robust UTM system, ​291; unmanned aerial system path planning, ​288–291; unmanned aerial vehicle traffic management, ​284–287. See also unmanned aerial vehicle; vertical takeoff and landing aviation urban mobility in driving simulation, ​ 42–45, 56; impact of biophilic design, ​44; cognition, ​42; ET and

Index

VR in driving simulation, ​44; heat map and gaze plot, ​43f; stress level during VR simulation, ​44f; visual elements of analytical method, ​43. See also virtual reality–based driving simulation US Department of Transportation (DOT), ​4; -approved AV proving grounds, ​123–124 User Equipment’s (UEs), ​19 V2X systems, ​229. See also selforganized intelligent adaptive traffic control vanpool. See ride-sharing variable speed limit (VSL), ​80 vehicle-detection technologies, ​263. See also integrated ramp and corridor control vehicle platooning, ​11 vehicle to device (V2D), ​229 vehicle to grid (V2G), ​229 vehicle-to-infrastructure (V2I), ​32 vehicle-to-network (V2N), ​32; communication, ​107 vehicle-to-pedestrian (V2P), ​32, 229 vehicle-to-vehicle (V2V), ​31 version testing, ​117–118. See also autonomous vehicle testing vertical takeoff and landing (VTOL), ​ 277, 299 vertical takeoff and landing aviation, ​ 293; airframe design, ​295–296; battery pack’s design, ​298–299; current technology, ​293–294; electronics, ​298; integration, ​296; need for automated flight systems, ​ 294; never-exceed speed, ​293; propellers, ​297–298; safety, ​295; struggles with propulsion, ​296–297; swashplate system, ​293. See also unmanned aerial vehicle; urban air mobility virtual behavioral primitives (VBPs), ​37

323

virtual private network (VPN), ​30 Virtual Reality (VR), ​35–36, 38f. See also virtual reality–based driving simulation virtual reality–based driving simulation, ​35, 36f, 41f, 45; data collection and analysis, ​41–42; EEG sensor used in nonimmersive driving simulation, ​42f; exemplary integrated analytical tool in VR driving simulation, ​41f; exemplary scenario modeling, ​40f; hardware, ​ 37–39; interactivity and interface, ​ 37, 38f; nonvisual systems, ​40; planning stage, ​39; semi-immersive and fully immersive VR driving system, ​39f; simulation of real world, ​36–37; software and scenario creation, ​39; urban mobility in driving simulation, ​ 42–45, 56; virtual reality, ​35–36; virtual reality based on immersion level, ​38f; visual elements, ​40; VR creation stage, ​40–41. See also transportation, emerging technologies in virtual reality–based simulation, ​123. See also software simulation VISSIM, ​122. See also software simulation volatile oxygen compounds (VOC), ​245 volume/capacity (v/c) ratio, ​259 VR software and scenario creation, ​ 39; data collection and analysis, ​ 41–42; EEG sensor, ​42f; integrated analytical tool in VR driving simulation, ​41f; nonvisual systems, ​ 40; planning stage, ​39; scenario modeling in unreal engine to host driving simulation, ​40f; visual elements, ​40; VR creation stage, ​ 40–41; VR driving simulation, ​ 41f. See also virtual reality–based driving simulation

324

Index

warehouse management, ​142; automated, ​142; technology used in, ​ 145. See also automated delivery and logistics; last-mile transportation Waymo’s AV units, ​214, 215f. See also first mile/last mile WEI’s CAV Flows Modeling Framework, ​240. See also signal-free autonomous intersection control Wei-Zone, ​241, 244. See also Cooperative Coordinated Adaptive Corridor Signal Timing Optimization

wireless local area (WLAN), ​163 work zone, ​11; traffic, ​81. See also smart work zone management work zone management (WZM), ​12 world model, ​87. See also autonomous vehicle design elements wrong-way driver (WWD), ​4; detection and mitigation, ​13. See also transportation, emerging technologies in ZONE algorithm, ​262