Advances in Traffic Transportation and Civil Architecture 9781032514383, 9781032514390, 9781003402220

Table of contents :
Cover
Title Page
Copyright
Table of Contents
Preface
Committee Members
Traffic safety and transport management planning
Airport group flight optimization in the context of air-rail
combined transportation
1 Introduction
2 Flight Evaluation Index System
3 Flight Optimization Model
3.1 Model assumes
3.2 Objective function
3.3 Constraints
3.3.1 Flight uniqueness constraint
3.3.2 Peak hour capacity limits
3.3.3 Constraints in connecting high-speed rail and flights
4 The Example Analysis
5 Conclusions
References
Simulation research on urban distribution capacity allocation based on spatial right of way
1 Introduction
2 The Logic of Distribution Capacity Resource Allocation
2.1 Distribution capacity allocation logic considering new energy vehicle charging
2.2 Distribution capacity allocation logic considering space right of way
3 Distribution Capacity Allocation Method Based on Multi-Agent
3.1 Overview of the multi-agent simulation model
3.2 Agent establishment
3.3 Agent interaction logic
3.3.1 Distribution node agent
3.3.2 Logistics park agent
3.3.3 Charging station agent
3.3.4 Traditional fuel distribution vehicle agent
3.3.5 New energy distribution vehicle agent
3.3.6 Order agent
4 Case Study
4.1 Simulation parameter setting
4.2 Right-of-way scheme setting
5 Result Analysis
6 Conclusions
References
Evaluation of public transport service quality based on improved SERVQUAL model
1 Introduction
2 Improvement of the Measurement Model
3 Evaluation of Public Transportation Service Quality
3.1 Constructing the evaluation index system of public transport service quality
3.2 Fuzzy comprehensive evaluation model based on analytic hierarchy process
3.2.1 Building factor set
3.2.2 Construct the evaluation set and numerical set
3.2.3 Build the weight vector
3.2.4 Constructing the membership subset and membership matrix
3.2.5 A first-level and second-level fuzzy comprehensive evaluation
4 Empirical Research
4.1 Questionnaire design
4.2 Reliability and validity analysis of the questionnaire
4.3 A fuzzy comprehensive evaluation of bus service quality
4.3.1 Fuzzy comprehensive evaluation calculation
4.3.2 According to the above calculation process, the expected comprehensive evaluation results can also be calculated, and the results are sorted out in Table 4
5 Conclusion
References
Research on the optimization of high-speed rail express operation process&x02014;taking Nanning East Station as an example
1 Introduction
2 Literature Review
3 Analysis of the Operation Process of High-Speed Rail Express at Nanning East Station
3.1 Introduction to Nanning East Railway Station
3.2 The operation process of high-speed rail express at Nanning East Station
3.2.1 Customers fill in orders
3.2.2 Express arrival
3.2.3 Inspection and weighing
3.2.4 Sorting and packaging
3.2.5 Loading operation
3.2.6 Transportation to the destination
4 Simulation of High-Speed Rail Express Operation Process at Nanning East Station
5 Suggestions for Optimizing the Operation Process of High-Speed Rail Express at Nanning East Station
5.1 Strengthen informatization construction
5.2 Reasonable planning of the delivery time
5.3 Cancel inspections and strengthen security inspections
5.4 Realize the mechanization of some links
References
Study on the environmental benefits of developing Sea-River through transportation in the Shandong Bohai Bay region
1 Introduction
2 Basic Conditions for the Development of Sea-River Through Transportation in the Bohai Bay Region
2.1 Development status
2.2 Bohai Bay area Sea-River through transportation development opportunities
2.2.1 Waterway advantage
2.2.2 Resource advantages
2.2.2.1 Advantages of inland river ports
2.2.2.2 Hinterland city advantage
2.2.3 Policy support
3 Bohai Bay Area Sea-River Through Transportation Development Layout
3.1 Sea-River through transportation channel
3.2 Sea-River through transportation core area
4 Analysis of Environmental Benefits of Sea-River Through Transportation Based on Carbon Emission Model
4.1 Carbon emission models for automobiles and ships
4.1.1 Automotive carbon emissions model
4.1.2 Ship carbon emission model
4.2 Analysis of environmental benefits of Sea-River through transportation in the Bohai Bay region
4.2.1 Xiaoqing river route
4.2.2 Beijing-hangzhou canal + tuhai river route / yellow river route
5 Suggestions on the Green Development Path of Sea-River Through Transportation in the Bohai Bay Region
5.1 The government provides policy and financial support
5.2 Establish a specialized agency for coordinated planning
5.3 Improve the layout of the port
5.4 Establishing an integrated information platform
5.5 Building a green, low-carbon port
6 Conclusions
References
Research on the influence of exit obstacles on crowd evacuation efficiency based on Pathfinder
1 Introduction
2 Establishment of the Model
2.1 Parameter setting
2.2 Determination of optimal location
3 Obstacle Simulation
3.1 Design of work condition
3.2 Analysis of effect
4 Conclusions
References
Traffic theory research on the attractiveness of subsidy policy for new energy vehicles based on the multinomial logit model
1 Introduction
2 Model and Method
2.1 Random utility theory
2.2 Multinomial logit model
3 Intention Survey and Data Analysis
3.1 Data acquisition
3.2 Data analysis
4 Results
4 Conclusions
References
Analysis of the emission factors of new energy vehicles compared to fuel vehicles during the lifecycle
1 Introduction
2 Carbon Emissions Throughout the Life Cycle of Light Vehicles
2.1 Factors influencing carbon emissions of fuel vehicles
2.1.1 Known conclusions and data
2.1.2 Panel conclusion
2.2 Carbon emission influencing factors of new energy vehicles
2.2.1 Known conclusions and data
2.2.2 Panel conclusion
3 Emission Factor&x00027;s Data Collection and Analyze
3.1 Fuel vehicle emission factors
3.2 New energy vehicle emission factors
3.2.1 Three types of new energy vehicle emission factors
3.2.2 EV300&x00027;s emission factors under different supplies
4 Proportion and Comparison of Emission Factor Data of Multiple Vehicle Models
4.1 The proportion of multi-vehicle emission factor data
4.2 Comparison of energy consumption data of four new energy vehicles
4.3 Comparison of emission factor data of four new energy vehicles
4.4 Energy consumption data comparison of EV300-CM new energy vehicles
4.5 EV300-CM new energy vehicle emission factor data comparison
5 Conclusion
References
A model for shared parking optimization for supply and demand matching considering overtime parking
1 Introduction
2 Parking Spaces Programming
2.1 Definitions of parameters and variables
2.2 Two-stage stochastic programming model
3 Model Comparison and Evaluation
4 Numerical Experiments
5 Conclusions
References
Application of traffic simulation in urban expressway traffic organization design
1 Introduction
2 Overview of the Hongyun Avenue Expressway Reconstruction Project
2.1 Project overview
2.2 Traffic organization scheme
3 Setup of Traffic Simulation
3.1 Contents of traffic simulation
3.2 Traffic simulation process
4 Setup of Traffic Simulation Parameters
4.1 The setting of traffic volume
4.2 The setting of intersection parameters
4.3 Traffic simulation evaluation standard
5 Traffic Simulation Analysis and Optimization
5.1 Analysis of traffic simulation results
5.2 Verification of optimized traffic organization scheme
6 Conclusion
References
Multi-location inventory optimization model in supply chain environment
1 Introduction
2 Model Building
2.1 Model underlying assumptions
2.2 Definition of symbols
2.3 Model building
3 Algorithm Design
4 Example Analysis
4.1 Background of the algorithm
4.2 Determine order points and safety stock
4.3 Solving and analyzing the ordering algorithm
4.3.1 Concentrate ordering:
4.3.2 Disperse ordering:
Table 2.The number and cost of disperse order.
4.4 Solving and analyzing the transfer case
5 Conclusion
References
Research on collaborative scheduling mechanism and method of automatic guided vehicles in complex warehouse
1 Introduction
2 AGV Collaborative Scheduling Mechanism
2.1 Phase I: Pallet assignment and optimal scheduling model
2.2 Phase II: AGV robot operation scheduling model
2.3 Algorithm
3 Conclusion
References
Socioeconomic contribution of rural road construction and revampment: A case study in China
1 Introduction
2 Contribution of Rural Roads
2.1 Review from the academic sector
2.2 Experiences from experts and government officials
2.3 Contribution indicators of rural roads
2.4 Contribution indicators of rural roads
3 Factor Analysis of Contribution Factors
3.1 Basic idea and steps of factor analysis
3.2 Adequacy analysis
3.3 Factor extraction
3.4 Component rotation and interpretation
4 Conclusions
References
Noise cancellation in highway construction
1 Introduction
2 Detection and Evaluation
2.1 3D GIS tool
2.2 Deep audio representation and BLSTM network
2.3 Real-time information release system
3 Noise Control Techniques
3.1 Negative noise control techniques
3.1.1 Green belt bamboo constructions
3.1.2 Movable noise barriers
3.1.3 Multi-objective optimization model
3.2 Active noise control techniques
4 Conclusions
4.1 Price
4.2 Application condition
4.3 The construction of the difficulty
References
Integrated emergency evacuation and supply scheduling for disaster emergency rescue
1 Introduction
2 Problem Description
3 Symbol Definition and Model Construction
4 Numerical Experiment
4.1 Parameter setting
4.2 Optimization results
5 Conclusions
References
Case study of cathodic protection of concrete piles to extend the service life of marine jetty
1 Introduction
2 Methods and Materials
3 Application of Cathodic Protection System
3.1 Electrical connection of steel bars
3.2 Design and fabrication of sacrificial anode framework
3.3 Anode assembly
3.4 Underwater installation of sacrificial anode
3.5 Cathodic protection potential measurement
4 Conclusion
References
Comparison of carbon and pollution emissions of conventional diesel and new energy short-haul heavy-duty trucks over their full life cycle based on the GREET model and assessment of influencing factors
1 Introduction
2 Results
2.1 Research analysis
2.2 Introduction to the research subject and analysis of salient features
2.2.1 Low-sulfur diesel vehicles
2.2.2 LNG natural gas vehicles
2.2.3 Gaseous hydrogen energy vehicles
2.2.4 Electric vehicles
2.2.5 Biomass fuelled vehicles
2.3 Data comparison
2.4 Comparative analysis
3 Conclusion
References
A cellular automata model with simulation-based optimization for multi-lane expressway
1 Introduction
2 Multi-Lane Cellular Automata Model
2.1 The arrival of vehicles and boundary conditions
2.2 Car-following process
2.3 Lane-changing process
2.3.1 Lane-changing rules for lane-1
2.3.2 Lane-changing rules for lane-2
2.3.3 Lane-changing rules for lane-3
3 Simulation-Based Optimization Method
4 Case Study
5 Conclusion
References
Construction quality evaluation of expressway asphalt pavement based on digital construction
1 Introduction
2 Digital Construction Pavement Paving System
2.1 Working principle
2.2 Work procedure
3 Establishment of Construction Quality Evaluation Model
3.1 Index selection
3.2 Determination of weights
3.3 Model establishment
3.4 Quality evaluation
4 Conclusion
References
Study on the potential impact about monitoring by remote sensing of the urban center of gravity changes on sustainable development of urban transportation
1 Introduction
2 Study Area and Data
2.1 Conditions of the study area
2.2 Data
3 Method
3.1 Monitoring methods of urban area
3.2 Extraction method of the urban center of gravity
4 Result and Analysis
4.1 Urban expansion of Jinan City
4.2 Study on the center of gravity of Jinan City
5 Conclusions
References
Comparative analysis of the lateral stability of double-trailer combination
1 Introduction
2 Materials and Methods
2.1 Software introduction
2.2 Three modular model parameters
2.2.1 A-double (articulated vehicle + center axle trailer)
2.2.2 B-double (semi-trailer towing vehicle + semi-trailer with fifth wheels + semi-trailer)
2.2.3 C-double (towing truck + dolly + semi-trailer)
3 Results &x00026; Discussion
4 Conclusions
References
Study on energy consumption and emission of heavy-duty vehicle fuel in whole life cycle
1 Introduction
2 Relevant Theoretical Basis
2.1 Life Cycle Assessment
2.2 Green low emission charging and carbon tax principle
2.3 The diffusion mechanism of full-life-cycle carbon emissions
3 The Full Life Cycle
3.1 Study on emission and energy consumption of heavy-duty vehicle fuel in the whole life cycle
3.2 The influencing factors and basic levels of pollutants for heavy-duty diesel vehicles in China
4 Air Pollutant Emission Analysis - Ozone Monitoring in Guangdong Province as an Example
4.1 Data source
4.2 Data processing method
4.3 Operation process
4.4 Policy orientation
5 Conclusion
References
The optimization of bus passenger dwelling behaviors&x02019; simulation model
1 Introduction
2 Methodology
2.1 Model introduction
2.2 Model construction
2.2.1 Simulation scenario construction
2.2.2 Agent abstraction
2.3 Model framework
2.3.1 Input module
2.3.2 Generating module
2.3.3 Computation module and refresh module
3 Simulation
3.1 The bus-arrival simulation
3.2 The passenger arrival simulation
3.3 The passenger dwelling time evaluation
4 Conclusion
References
Economic benefits evaluation of cold central plant recycling and the measures to reduce costs and increase benefits&x02020;
1 Introduction
2 Methods
2.1 RAP recovery and disposal costs
2.2 Asphalt mixture production costs
2.3 Asphalt pavement paving costs
3 Materials and Analysis
3.1 Economic benefits calculation
3.2 Sensitivity analysis
4 Discussion and Countermeasures
4.1 Control the consumption of emulsified asphalt reasonably
4.2 Lay out and construct the mixing station suitably
4.3 Establish economic benefits evaluation mechanism
5 Conclusions
References
Research on the development strategy of chinese rural transportation adapting to the population migration
1 Introduction
2 The Trend of Rural Population Migration in China
2.1 The nation&x00027;s population is going to reach its peak. The rural population will keep decreasing
2.2 The aging degree of the rural population has increased significantly. The number of rural laborers has decreased rapidly
2.3 The number of rural nodes in China will be further reduced with town reformation and village merging
3 Analysis of the Adaptability of Rural Roads and Rural Population in China
3.1 Road network scale
3.2 Accessibility
3.3 Grade structure
4 The Impact of Rural Population Migration on Rural Traffic Demand in China
5 Ideas of Rural Transportation Development Adapted to the Rural Population Migration in China
5.1 Strategies for the development of rural roads in suburban villages
5.2 Strategies for the development of rural roads in gathering villages
5.3 Strategies for the development of rural roads in protected villages
5.4 Strategies for the development of rural roads in removed villages
6 Conclusions
References
Traffic network and transport system modelling
Calibration method of pavement lateral force coefficient based on
passive excitation
1 Introduction
2 Testing Principle of Friction Coefficient of Pavement Lateral Force
3 Model Design and Analysis
3.1 Model design of passive excitation device
3.2 Static mechanical analysis
3.3 Transient dynamic analysis
4 Test and Discussion
4.1 Correlation analysis of the two methods
4.2 Comparative analysis of the two methods
5 Conclusion
References
The dynamic strategy of evacuation based on neural network for ocean liner
1 Introduction
2 Environment Modeling
2.1 Establishment of the network flow model
2.2 Model optimization
3 Dynamic Evacuation Method Based on Neural Network
3.1 Generation of training set path
3.2 Design and training of neural network
3.3 Path planning based on disaster change
4 Result
4.1 The setting of simulation parameters
4.2 Simulation results and analysis
5 Conclusions
References
Route optimization of express network considering high-speed rail express and carbon emission
1 Introduction
2 Path Optimization Model
2.1 Problem analysis
2.2 Symbol definition
2.3 Objective function
2.4 Constraint condition
3 Algorithm Design
4 Computational Results
4.1 Example design
4.2 Results
6 Conclusions
References
Target market positioning of container railway-sea express
1 Introduction
2 Construction of the Target Market Selection Model
2.1 Selection of target market segmentation variables
2.2 Construction of target market evaluation index system
2.2.1 Market segment size and growth potential
2.2.2 Market segment attractiveness
2.2.3 The objectives and resources of freight enterprises
2.3 TOPSIS-based target market selection model construction
2.3.1 Construction of objective function
2.3.2 Calculation of Z1ij and Z3ij
2.3.3 Calculation of Z2ij
3 Case Study
3.1 Selecting a template
3.2 Market segmentation based on the new western land–sea corridor
3.3 Market choice based on the new western land–sea corridor
3.3.1 Segment size and growth potential score calculation
3.3.2 Competitive score calculation
3.3.2.1 Judgment of demand characteristics of typical cargo type transportation
3.3.2.2 Calculation of service capacity scores of four modes of transport for different typical cargo categories of transport demand
3.3.2.3 The railway–sea express competitiveness score calculation
3.3.3 Freight resource score calculation
3.3.4 The railway–sea express target market selection
4 Conclusions
References
Congested roadway resilience evaluation based on taxi GPS data
1 Introduction
2 Data Processing and Model Construction
2.1 Road performance level profile
2.1.1 Vehicle driving status classification
2.1.2 Trajectory data pre-processing
2.2 Road resilience evaluation model
2.2.1 Resilience index
2.2.2 Resistance index
2.2.3 Recovery index
2.3 The ward hierarchical clustering of road resilience
3 Case Application and Analysis
3.1 Study area and data sources
3.1.1 Study area
3.1.2 Data preprocessing
3.2 Road driving status and resilience evaluation.
3.3 Road network resilience assessment and classification
4 Conclusion
References
More robust and better: Automatic traffic incident detection based on XGBoost
1 Introduction
2 Methodology
2.1 Preliminaries
2.2 Traffic incident detection based on ADASYN-XGBoost
2.2.1 Step 1. Spatial-temporal features mining
2.2.2 Step 2. Incident samples augment based on ADASYN
2.2.3 Step 3. Important features selection based on XGBoost
2.2.4 Step 4. Training detection model based on XGBoost
2.2.5 Step 5. Incident detection and test
3 Experiment Study
3.1 Description of datasets
3.2 Experimental settings
3.3 Measures of model effectiveness
3.4 Experimental results and analysis
3.4.1 Augment effects analysis of ADASYN
3.4.2 Detection results analysis of ADASYN-XGBoost
4 Conclusions
References
Reliability analysis of fresh agricultural products cold chain transportation system based on fault tree-Bayesian network
1 Introduction
2 Cold Chain Transport Process and Failure Cause Analysis
3 Modeling Approach
3.1 Factor analysis of cold chain transportation system based on fault tree model
3.2 Reliability analysis of cold chain transportation system based on Bayesian network
3.3 Root node probability importance calculation for system failure Bayesian networks
4 Numerical Example and Result
5 Conclusions
References
Multi-discipline speed control matching evaluation for urban rail transit
1 Introduction
2 Method
2.1 Problem description
2.2 Speed matching-related indicators
2.3 Matching evaluation method
3 Case Analysis
4 Conclusion
References
Research on microwave radar non-contact detection technology for expressway bridges
1 Introduction
2 Measurement of Cable Force Based on Microwave Radar
2.1 Basic principle of microwave radar deformation measurement
2.2 Detect method
3 Engineering Application
3.1 Project overview
3.2 Application of microwave radar technology
4 Conclusion
References
Underground mine rail transport system based on CBTC
1 Introduction
2 Concept and Design: Functional Design of the CBTC System
2.1 Background of the actual problems
2.2 The functional design of the CBTC system to solve practical problems
2.2.1 Safety problem: Back-end collision
2.2.2 Efficiency problem: transportation efficiency problem
2.2.3 Monitoring: Real-time monitoring problems
3 Discussion and Outlook
4 Conclusions
References
Design of science and technology management information system
1 Introduction
2 System Composition and Function
2.1 Project management module
2.2 Report management module
2.3 Funds management module
2.4 Mobile office module
3 System Architecture
3.1 Software architecture system
3.2 Hardware architecture system
4 System Implementation
4.1 System main interface
4.2 Related system interface
4.3 System part code
5 conclusion and Outlook
References
Analysis of travel characteristics of clustered city based on mobile signaling data taking Shijiazhuang City as an example
1 Introduction
2 Materials and Methods
2.1 Data preprocessing
2.2 The spatio-temporal information extraction algorithm
2.2.1 Resident travel chain identification
2.2.2 Identification of job-residence distribution
3 Characterization of Urban Features
3.1 Spatial characteristics and industrial layout
3.2 Jobs and residences distribution and balance
4 The Analysis of Travel Characteristics
4.1 Characteristics of total travel volume
4.2 Travel distance characteristics
4.3 Time distribution characteristic
4.4 Spatial distribution characteristics
5 Conclusions and Suggestions
References
Study on intercity intermodal passenger transport behavior in Chengdu and Chongqing region
1 Introduction
2 Passenger Intermodal Travel Survey
2.1 Questionnaire design
2.2 Analysis of survey results
3 Model Establishment and Parameter Calibration
3.1 Factor selection
3.2 Model building
3.3 Model calibration
4 Optimization Strategy Analysis
4.1 Marginal substitution rate calculation
4.2 Sensitivity analysis
4.3 Optimization strategy
5 Development Proposals
5.1 Promote the integration of hub functions
5.2 Promote the integration of intermodal traffic ticketing
5.3 Promote the integration of baggage transportation
5.4 Promote information sharing of intermodal traffic
6 Conclusions
References
Research on GIS detection and scheduling of unmanned vehicles based on convolutional neural network
1 Preface
2 Technical Process
2.1 Extraction of vehicle features
2.2 YOLOv3 target detection algorithm
2.2.1 Data set selection
2.2.2 Experimental evaluation index
2.2.3 Experimental procedure
2.2.4 Experimental results and analysis of the VOC dataset
2.2.5 Experimental results of vehicle dataset
3 System Model
3.1 Model structure
3.2 Intersection vehicle dispatch program
4 Conclusion
References
A study on factors affecting drivers&x02019; dangerous driving
1 Introduction
2 Theories Related to Dangerous Driving
2.1 Dangerous driving behavior
2.2 Hazard perception
3 Main Investigation Methods
3.1 Demographic questionnaire
3.2 Dula Dangerous Driving Index (DDDI)
3.3 Driving Behavior Questionnaire (DBQ)
4 Research Methods
4.1 Data sources and main research methods
4.2 Results
4.2.1 Age and dangerous driving
4.2.2 Sex and dangerous driving
4.2.3 Driving experience and dangerous driving
4.2.4 Type of junctions and dangerous driving
4.2.5 Weather and dangerous driving
5 Other Influencing Factors
5.1 Personality characteristics
5.2 Factors influencing the driving status
6 Conclusion
References
Comprehensive evaluation of transport infrastructure development capacity in five central Asian countries based on factor analysis
1 Introduction
2 Data and Research Methods
2.1 Data source and indicator system construction
2.2 Overview of a factor analysis model
3 Case Study on Transport Infrastructure Development Capacity of Five Central Asian Countries
3.1 Factor analysis adaptability test
3.2 Factor analysis process and results
4 Research Conclusions and Suggestions
5 Conclusions
References
Traffic flow prediction based on the spatiotemporal attention
1 Introduction
2 Model
2.1 Problem definition
2.2 Model framework
2.3 Traffic flow similarity based on the DTW algorithm
2.4 Spatio-temporal matrix
2.5 STFGN
2.6 T-attn
2.7 GAT
2.8 Gated-GAT-1DCNN
3 Experiment
3.1 Dataset
3.2 Data settings
3.3 Model evaluation indicators
3.4 Experimental comparison
4 Summary
References
Research on pedestrian trajectory prediction based on deep learning and pedestrian interaction
1 Introduction
2 Model
2.1 Problem definition
2.2 Pedestrian interaction
2.2.1 Voronoi diagram
2.2.2 Velocity obstacle method
2.3 Deep learning network
2.3.1 Interaction module
2.3.2 Trajectory prediction module
3 Experiment
3.1 Data source
3.2 Experimental parameter setting
3.3 Evaluation indicators
3.4 Result
3.4.1 Unidirectional flow
3.4.2 Bidirectional flow
4 Conclusions
References
Study on the evaluation system of green port development in Suzhou
1 Introduction
2 Literature Review
3 Construction of Evaluation Index System of Green Port Development
3.1 Evaluation system of green port development in Suzhou
3.2 Meaning of evaluation index
3.3 Determination of evaluation index weight
3.3.1 Calculate the maximum eigenvalue &x003BB;max of judgment matrix A and its corresponding normalized eigenvector &x003C9;=(&x003C9;1,&x003C9;2,...,&x003C9;n)T
3.3.2 Consistency test
3.3.3 Calculate the consistency coefficient of the judgment matrix CR, which is the ratio of the consistency index CI to the corresponding average random consistency index RI
3.3.4 Hierarchical total ranking and consistency test
4 Analysis of Evaluation Results
5 Conclusions
References
A methodology for flight departure delay prediction based on stacking technique
1 Introduction
2 Methodology
2.1 Stacking
2.2 Performance measures
2.3 Feature construction
3 Case Study
3.1 Data description and preprocessing
3.2 Feature selection
3.3 Base learner
4 Results and Discussion
4.1 Delay predicting results
4.2 Delay of the previous flight
4.3 Air route condition
5 Conclusions
References
Simulation analysis of pedestrian flow in densely populated places – A large school building as an example
1 Introduction
2 Collecting Basic Pedestrian Data
2.1 Number of pedestrians arriving
2.2 Pedestrian travel speed
3 Parameter Analysis
3.1 One-sample K-S test
3.2 Analysis of fitting results
3.2.1 Class time slots
3.2.2 Dismissal time slot
4 Simulation Verification
4.1 Establishing a simulation experiment model
4.2 Simulation model verification
5 Conclusion
References
The traffic control policy of highway system in China under COVID-19
1 Introduction
2 Materials and Methods
2.1 Two-stage control strategy
2.1.1 First stage
2.1.2 Second stage
2.2 Model establishment
2.2.1 Road network modeling
2.2.2 Cost analysis
2.2.2.1 Detecting cost
2.2.2.2 Detecting cost
2.2.2.3 Exposure cost
2.2.2.4 Delay cost
2.3 Model establishment and solution
2.3.1 Model assumptions
2.3.2 Model establishment
2.3.3 Constraints
2.3.4 Model solution
3 Results &x00026; Discussion
3.1 Case study
3.2 Data collection and processing
3.3 Parameter setting
3.4 Results
3.5 Strategy comparison
4 Conclusion
References
3D scene construction of railroad engineering facilities based on multi-source data fusion
1 Introduction
2 Multi-Source Data Fusion Technology Process
3 Three-Dimensional Scene Construction
3.1 Railway three-dimensional monomer model construction
3.2 Digital terrain model construction
3.3 True 3D realistic model creation
4 Project Examples
4.1 Project overview
4.2 Three-dimensional scene construction
4.2.1 Modeling of railroad structures
4.2.2 Digital terrain modeling
4.2.3 3D scene model integration and display
5 Conclusions
References
Design of wave-maker system for harbor basin driven by servo motor
1 Introduction
2 Structure of Wave-Maker
3 Design Calculation of Key Components
3.1 Screw-nut pair
3.1.1 Support form
3.1.2 Specification of screw
3.2 Selection of servo motor
4 Performance Verification
4.1 Layout scheme
4.2 Result
5 Conclusions
References
A method for evaluating development mode of port-city based on a multiple relative concentration index model
1 Introduction
2 Review of Research on Port-City Relationships
2.1 Literature review
2.2 Research framework
3 Influencing Factors of Port-City
3.1 Theoretical background
3.2 Analytical model for controlling factors
3.3 Extraction of controlling factors
4 Port-City Relationships Evaluation Methodology
4.1 Proposal of the MRCI model
4.2 Classification of the port–city relationships
4.3 Analysis of port-city development mode
5 Conclusions
References
Analysis of the influence of wheelset mode on the high-speed dynamic performance of train
1 Introduction
2 Wheelset Modal Analysis
2.1 Establish the flexible body model of the wheelset
3 Establishment of a Rigid-Flexible Coupling Model
3.1 Wheelset modeling
3.2 Bogie modeling
3.3 Vehicle modeling
3.4 Numerical analysis of acceleration vibration signal
4 Influence of Wheel Pair Mode on Wheelset Acceleration at Different Speeds
4.1 Vertical acceleration
5 Vertical Acceleration of Axle Box Measured on the Line
5.1 Comparative analysis
6 Conclusion
References
A hydrodynamic model of steering tilting stability of partially-filled tank truck
1 Introduction
2 The Dynamic Mechanism of Tank Truck Rollover
2.1 Different motion states of tank trucks under the action of external forces
2.2 Analysis of steering force of tank truck
2.3 Mechanical conditions of tank truck overturning
3 The Liquid Pressure Moment of the Tank Truck During Steering Motion
3.1 Distribution of pressure inside the liquid during the steering motion of the tank truck
3.2 The lateral pressure of the liquid on the tank during the steering motion of the tank truck
3.3 Pressure of liquid on the bottom of the tank during steering motion of the tank truck
4 Hydrodynamic Model of Tilting Stability of Tank Truck and its Preventive Measures
5 Conclusions
References
Building material property and construction technology
Study on the synthesis and performance of low-sensitivity
polycarboxylate superplasticizer for machine-made sand concrete
1 Introduction
2 Experimental
2.1 Materials
2.1.1 Experimental raw materials
2.1.2 Testing raw materials
2.2 Copolymerization
2.3 Performance test method
2.3.1 Gel permeation chromatography (GPC) characterization
2.3.2 Infrared spectrum characterization
2.3.3 Concrete test
3 Experimental Results and Discussion
3.1 Infrared Spectroscopy Analysis (FTIR)
3.2 Gel Permeation Chromatography (GPC)
3.3 Sensitivity concrete test (Dosage)
3.4 Sensitivity concrete test (Temperature)
3.5 Sensitivity concrete test (Mud content)
4 Conclusions
References
Mechanical properties of asymmetric composite welds in the steel shell of immersed final joints along Shen-Zhong link
1 Introduction
2 Engineering Background
3 Engineering Problems and Analysis
4 Experimental Study on Tensile Properties of Asymmetric Composite Fillet Welds
4.1 Specimen design
4.2 Loading procedure
4.3 Analysis of test results
5 Numerical Analysis of Flexural Properties of Asymmetric Composite Fillet Welds
5.1 Modeling
5.2 Result analysis
6 Conclusions and Suggestions
References
Design and research of through reinforced concrete arch bridge on soft soil foundation
1 Introduction
2 Bridge Selection
3 Overall Plan
4 Structural Design
4.1 Superstructure
4.2 Substructure
4.3 Deck design
4.4 Steel frame under the arch
5 Structural Analysis
5.1 Calculation model
5.2 Main loads
5.3 Strength calculation
5.4 Crack calculation
5.5 Fundamental frequency calculation
5.6 Stiffness calculation
5.7 Pile top displacement
6 Conclusions
References
Research on fine tension control technology of hectometre dense steel strands
1 Introduction
2 Project Overview
2.1 Engineering background
2.2 Brief introduction of I beam construction
3 Model and Internal Force Calculation Results
3.1 Model description
3.2 Column deformation check
3.3 Beam force check
4 Beam Prefabricated Construction Technology
4.1 Design of tensioning pedestal
4.2 Construction of tensioning pedestal
4.3 Pre-tensioning method construction
4.3.1 Preparations before stretching
4.3.2 Tensioning operation
4.3.3 Template installation
4.3.4 Concrete construction
4.3.5 Moving beam
5 Conclusion
References
Research on deformation and stress characteristics of locking steel pipe pile cofferdam under different working conditions
1 PREFACE
2 Project Overview
2.1 Project introduction
2.2 Hydrological and geological conditions
3 Monitoring Items and Monitoring Point Layout
4 Monitoring Results and Analysis
4.1 Horizontal displacement of deep steel pipe pile and cofferdam
4.2 Cofferdam structure stress
4.3 The tension of the steel cable hoop
4.4 Surface settlement of soil mass in the cofferdam
5 CONCLUSION
References
Design scheme and key measures of artificial island under complex environment
1 Introduction
2 Project Overview
3 Design Scheme of East Man Island
3.1 Plane layout
3.2 Island wall structure
3.3 Land formation and foundation treatment
4 Key Measures
4.1 Island wall foundation treatment
4.2 Pier protection pile
4.3 Backfill zoning and technical requirements
5 Finite Element Analysis and Calculation
5.1 Calculation model
5.2 Calculation conditions
5.3 Calculation results
6 Conclusion
References
Research on debris flow movement law and impact performance based on discrete element method
1 Introduction
2 Model Design And Parameter Selection
3 Model Validation
4 Analysis Of The Movement Law Of Debris Flow
5 Analysis of Impact Performance of Debris Flow
5.1 Impact performance of the rigid barrier
5.2 Analysis of the blocking effect of a rigid barrier
6 Conclusions
Acknowledgment
References
Application of mechanical hoisting equipment in prefabricated buildings
1 Introduction
2 Application Technology of Mechanical Hoisting Equipment
3 Engineering Application
3.1 Project overview
3.2 Engineering investigation
3.3 Structural design based on the application principle of mechanical lifting equipment in prefabricated buildings
3.4 Structural safety analysis
3.5 Application result analysis
4 Conclusion
References
Research status and prospect of highway W-beam guardrail anti-collision technology
1 Introduction
2 Research Theoretical Models
3 Performance Assessment Criteria
4 Status of Guardrail Optimization Research
4.1 Optimization of block
4.2 Optimization of waveform beam plate
4.3 Optimization of column
5 Prospection
6 Conclusions
References
Comparative study of damage characteristics of PHC pile under different seismic ground motions
1 Introduction
2 The Dynamic Finite Element Model
3 Results and Discussions
4 Conclusions
References
Study on sustainability assessment of zero energy buildings based on emergy method – Taking the architecture of Nanjing ecological park as an example
1 Introduction
2 Methodology
2.1 Zero carbon building case
2.2 Emergy method
2.3 Sustainable indicators
2.4 The innovation of this article
3 Results and Discussion
3.1 Emergy calculated details
3.2 Results analysis
References
Effects of corner rounding modification on flow characteristics around the rectangular pier
1 Introduction
2 Numerical Methods and Modeling
2.1 Numerical methodology
2.2 Model establishment
2.3 Meshing and boundary conditions setting
3 Hydrodynamic Characteristics of Bridge Piers
4 Results and Discussion
4.1 Streamline distribution
4.2 Speed distribution
5 Conclusion
References
Study on assembled floating slab track of suburban railway
1 Introduction
2 Floating Slab Track Structure and Calculation Model
2.1 Structural design scheme
2.2 Vehicle-track-foundation coupling dynamic model
2.3 Calculated parameters
3 Isolator Stiffness Analysis
3.1 Calculation condition
3.2 Vehicle dynamic response
3.3 Dynamic response of track
4 Structural Analysis of Slab Joints
5 Analysis of Vibration Reduction Effect
5.1 Vibration reduction index
5.2 Analysis of result
6 Conclusions
References
Effect of salt spray corrosion on fatigue crack growth rate of 60Si2MnA steel
1 Introduction
2 Test Material
3 Test Methods
3.1 Neutral salt spray corrosion test
3.2 Fatigue crack growth rate test
4 Test Results and Analysis
4.1 Salt spray corrosion
4.2 a-N curve under each neutral salt spray corrosion cycle
4.3 Fatigue crack growth rate under each neutral salt spray corrosion cycle
5 Fatigue Fracture Morphology and Mechanism Analysis Of 60Si2MnA Spring Steel after Salt Spray Corrosion
6 Conclusions
References
Research on construction technology of fabricated reinforced concrete piers based on grouting sleeve type
1 Introduction
2 Project Overview
3 Preparation Before Construction
3.1 Technical disclosure work
3.2 Inspection before construction
4 Key Points of the Construction Process of Grouting Sleeve-Assembled Reinforced Concrete Piers
4.1 Pre-embedded sleeve production
4.2 Rebar cage binding
4.3 Template installation
4.4 Concrete pouring
4.5 Formwork removal and maintenance
4.5.1 Formwork removal
4.5.2 Concrete structure maintenance
4.6 Pier and column assembly and grouting
5 Conclusion
References
Key technologies for the construction of super-long and wide double-layer catenary-shaped upper-stiffened steel truss bridges
1 Introduction
2 Project Summary
2.1 Design scheme
2.2 The construction plan
2.3 The challenge of building technology
3 Steel Truss Beam Processing and Manufacturing
3.1 The process flow
3.2 Precision control of steel components
3.3 Virtual preassembly technology
4 Assembly Jacking Construction
4.1 Lifting technology of irregular half truss of steel truss beam
4.2 &x0201C;Walking machine + slider&x0201D; push construction
4.3 Push intelligent monitoring
5 Conclusion
References
Design and construction technology of ultra-high jacking pier support in mountain area
1 Introduction
2 General Situation of Engineering
2.1 Project overview
2.2 2# General situation of auxiliary pie
2.3 General situation of jacking construction
3 Support Design Scheme
3.1 Bracket arrangement form
3.2 Column steel pipe design
3.3 Flat link support wall bar design
3.4 Horizontal track beam design
4 Stent Calculation Analysis
4.1 Overall calculation of the support
4.2 The pier body reserved hole calculation
4.2.1 Calculation of crack resistance of concrete in partial pressure zones
4.2.2 Calculation of bearing capacity of local pressure zone
5 Bracket Construction
5.1 Construction process
5.2 Bracket installation
5.2.1 Steel tube column installation
5.2.2 Attached wall mounting
5.2.3 Flat link installation
5.3 Installation of horizontal and support beams
5.3.1 Lifting frame installation
5.3.2 Installation of support beams
5.3.3 Horizontal beam installation
6 Conclusions
References
Dynamic response analysis of beam bridge with breathing crack subject to vehicle loads
1 Introduction
2 Dynamic Calculation Model of the Vehicle-Bridge Coupling System
3 Breathing Crack Model
4 Dynamic Response Analysis
4.1 A simply supported beam with a breathing crack under the vehicle load
4.2 The effect of different damage models on dynamic responses
4.3 The effect of different damage levels of the breathing crack model on the dynamic response
4.4 The effect of vehicle speed on the dynamic response of damaged simply supported beams
5 Conclusions
References
Research status of steel slag in intermittent graded asphalt mixtures
1 Introduction
2 Physico-Chemical Properties of Steel Slag
3 Mix Ratio Design of Steel Slag Asphalt Mixture
4 The Road Performance of Steel Slag Asphalt Mixtures
4.1 High-temperature stability
4.2 Low-temperature crack resistance
4.3 Water stability
4.4 Slip resistance
4.5 Fatigue performance
5 Applications in Engineering Roads
6 Conclusions and Outlook
References
Pavement surfacing on bridges&x02014;The Gussasphalt concrete preparing techniques of high-temperature resistance
1 General Introduction
2 Engineering Background
3 Guss Asphalt Components Content Adjustment
3.1 Adjustment of various asphalt in the Gussasphalt
3.2 Gussasphalt concrete component adjustment
4 Preparation Optimization
4.1 Stirring time
4.2 Stirring temperature
5 Warm Mix Modifiers Addition
6 Conclusion
References
Interface protective layer based on plastic crystal composite electrolyte
1 Introduction
2 Experimental Method
2.1 Preparation of LATP electrolyte sheet and plastic crystal composite electrolyte interface layer
2.2 Battery pole piece and battery assembly electrode pole piece preparation process
2.3 Material characterization and battery performance testing
3 Experimental Characterization Results
3.1 Characterization of LATP electrolyte sheets
3.2 Characterization of the interfacial layer based on plastic crystal composite electrolyte
3.3 Electrochemical properties
4 Conclusion
References
Research on BIM-based sinking pre-tensioning construction technology
1 Introduction
2 Project Overview
3 Bim-Based Sinker-Tensioner Construction
3.1 Precast beam yard with sunken-frame pedestal structure
3.2 Tandem pre-tensioning method
3.3 Automatic spraying maintenance system
3.4 BIM technology-based three-dimensional visualization of the technical handover
4 Conclusion
References
Research on thermal damage of wheel and rail considering material contact temperature change effect
1 Preface
2 Thermal Damage Calculation Model
3 Finite Element Model and Verification
3.1 The establishment of a finite element model
3.2 Feasibility verification of the model:
4 Influence of Friction Temperature Rise Effect on Contact Parameters
5 The Calculation Results
6 Conclusion
References
Finite element study on flexural behavior of cold-formed thin-wall steel-glulam composite beams
1 Introduction
2 Establishment and Verification of Finite Element Model
2.1 Stress nephogram analysis of section steel
2.2 Stress nephogram analysis of glulam
2.3 Analysis of load-mid-span displacement
3 New Type Steel-Glulam Composite Beam
3.1 Abbreviations and acronyms
4 Parameter Analysis
4.1 Steel type
4.2 The thickness of the steel
4.3 Top and bottom flange thickness of glulam
5 Conclusion
References
Simulation study on shear performance of steel-bamboo composite I-shaped beam
1 Introduction
2 Specimen Design and Material Property Test
2.1 Composite beam design
2.2 Determination of mechanical properties of materials
3 Anasys Finite Element Simulation
3.1 Unit type selection
3.2 Definition of material properties
3.3 Modeling and meshing
3.4 Loading and solving
4 Theoretical Verification of Shear Capacity
5 Conclusions
References
Study on the crack resistance of composite bridge with ultra-high-performance concrete
1 Introduction
2 Experimental Program
3 Analysis of Cracking Performance
3.1 Load-deflection
3.2 Cracks and propagations
4 Prestressing Effect on UHPC Slab
5 Conclusions
References
Chloride-ions penetration resistance of sulphoaluminate cement concrete modified by retarder and mineral admixtures
1 Introduction
2 Experimental
2.1 Materials
2.2 Design and methods
3 Results and Discussions
3.1 Effect of sodium gluconate retarder
3.2 Effect of fly ash
3.3 Effect of silica fume
3.4 Effect of GGBS
3.5 Effect of limestone powder
3.6 Relationship between the charge passed and chloride-ion diffusion coefficient
4 Conclusions
References
Experimental study on axial compressive behavior of concrete column with multiple pressure forming CFST cores
1 Introduction
2 Mechanical Property Test on Prefabricated CFST Cores
3 The Test on Concrete Columns with Multiple Pressure-Forming CFST Cores
3.1 Specimen design
3.2 Specimen fabrication
3.3 Experiment procedure
3.4 Experiment results
4 Conclusion
References
Rectangular pipe jacking and drag reduction technology for long distance silty formation
1 Introduction
2 Engineering Project
3 Long-Distance Rectangular Pipe Jacking Mud Drag Reduction Technology
3.1 Mud drag reduction mechanism
3.2 Optimal design of thixotropic mud
4 &x0201C;Thick Mud&x0201D; Drag Reduction Technology
4.1 Optimum design of mixture ratio of &x0201C;thick mud&x0201D; material
4.2 Engineering effect of injecting &x0201C;thick mud&x0201D;
5 Analysis of Drag Reduction Effect
5.1 Analysis of pipe jacking force
5.1.1 Earth pressure above the pipe joint
5.1.2 Socket bottom soil pressure
5.1.3 Around the pipe joint lateral earth pressure
5.1.4 Front resistance of earth pressure balanced pipe jacking machine
5.1.5 The total jacking force
5.2 Analysis of jacking force measured curve
5.3 Analysis of jacking force measured curve
6 Conclusions
7 Conflicts of Interest
References
Snow melting performance of phase change material asphalt pavement based on data mining algorithm
1 Introduction
2 Research on Snow Melting Performance
2.1 The meaning of data mining
2.2 General requirements
2.2.1 Physical properties
2.2.2 Chemical properties
2.2.3 Economical
2.3 Snow-melting performance of asphalt pavement
2.3.1 Freeze-thaw-scour resistance
2.3.2 Freeze-thaw-scouring mechanism
3 Investigation and Research on Snow Melting Performance of Phase Change Material Asphalt Pavement Based on Data Mining Algorithm
3.1 Purpose of the test
3.2 Test content
3.3 Analysis of total heat transfer coefficient of pavement in the state of melting snow and ice
4 Analysis and Research on Snow Melting Performance
4.1 Effect of electricity and heating under different wind levels
4.2 Analysis of the effect of phase change heating on inhibiting frost formation
5 Conclusions
References
Research on China&x00027;s potential LNG bunkering center selection based on AHP-TOPSIS method
1 Introduction
2 Experiences in North America, the EU, And Asia
2.1 Jacksonville Port, US
2.2 Fourchon Port, US
2.3 Port Tacoma, US
2.4 Port Canaveral, US
2.5 Port of Antwerp, EU
2.6 Zebuluhe Port, EU
2.7 Rotterdam Port, EU
2.8 Gothenburg Port, EU
2.9 Singapore Port, Asia
2.10 LNG Terminal in Kochi, India, Asia
3 Main Considerations and Key Factors for Site Selection of LNG Bunkering Center
3.1 LNG resource development
3.2 Port location advantage
3.3 Local policy support
4 Experiences Application in China
4.1 Demand forecast
4.2 LNG resource of port
4.3 Local policy support
5 Conclusion
References
Civil structural surveying and reinforcement engineering
5G-enabled intelligent detection and green repair technology for
exterior wall leakage
1 Introduction
2 Investigation and Analysis of Damage and Leakage of Exterior Walls
3 Intelligent Detection of Exterior Wall Damage and Leakage
3.1 Conventional detection methods
3.2 High-definition camera and infrared thermal imaging drones
3.2.1 Detection principles of high-definition and infrared thermal imaging camera drones
3.2.2 Research and development of compatibility of survey equipment and image transmission system
4 Intelligent Leakage Diagnosis and Repair System for Exterior Walls
4.1 Main functions of the system
4.2 Key technologies for the development of intelligent diagnostic and repair system
4.2.1 Integration of front-end equipment
4.2.2 Research and development of intelligent software platform
4.2.3 Workflow based on 5G technology
5 Green Technology for Repairing Exterior Wall Damage and Leakage
5.1 Non-destructive repair materials for exterior walls&x02014;transparent waterproof coating material technology
5.2 Prefabricated integrated panel
6 Conclusion
Funding
References
Reliability evaluation of small and medium span bridges in dry cold regions
1 Introduction
2 Materials and Methods
2.1 Establish a bridge reliability evaluation index system
2.2 Example analysis
2.2.1 Project overview
2.2.2 Sample data collection
2.2.3 Data processing
2.3 Model training
3 Results
4 Conclusions
References
Construction of walkability index based on access to metro stations
1 Introduction
2 Methodology
2.1 Selection of factors related to walk access to metro stations
2.2 Weight estimation for selected factors
2.3 Data collection and construction of walkability index
3 Case Study, Results, and Discussion
3.1 Survey implementation
3.2 Brief analysis of survey data
3.3 Model calibration results and discussion
4 Conclusions
References
Physical model test study on instability mechanism of large deformation of deep roadway surrounding rock
1 Introduction
2 Configuration of Similar Materials in Model Test
3 Geomechanical Model Test of Deep Roadway Excavation
3.1 Testing apparatus
3.2 Test monitoring instruments
3.3 Model excavation process
3.4 Test result analysis
3.4.1 Mechanical response analysis of the excavation process
3.4.2 Analysis of strain variation around the roadway
4 Conclusion
References
Stability analysis of reinforced soil slope based on possible slip mode
1 Introduction
2 Slipping Mode
2.1 Active slide mode
2.2 Coulomb slip mode
2.3 Passive sliding mode
3 Discussion on the Applicability of Possible Slip Modes
3.1 Coulomb slip mode
3.2 Active slip mode
3.3 Passive slip mode
3.4 Effect of reinforcement length on stability
4 Universal Analysis of Coulomb Slip Mode
5 Conclusions
References
Study on the causes and countermeasures of pile cracking at an in-service high-piled wharf
1 Introduction
2 Project Overview
3 Test results and analysis
3.1 Detection of crack length and occurrence site
3.2 The crack depth and width detection
4 Cause Analysis and Countermeasure Research
5 Conclusion
References
Numerical study of the influence of volumetric block proportion on excavation-induced ground responses in the talus-like rock mass
1 Introduction
2 Numerical Model Setup
2.1 Model configuration and calibration
2.2 Tunnel excavation model
3 Results and Discussion
3.1 Stress distribution
3.2 Ground deformation
3.3 Coordination number
4 Conclusions
References
Stability analysis and prevention suggestions for a landslide in Sichuan Province
1 Introduction
2 Engineering Geological Conditions
2.1 Topography
2.2 Formation lithology
2.3 Hydrological conditions
2.4 Human activities
3 Basic Characteristics of Landslide
3.1 Morphological characteristics of landslide
3.2 Landslide characteristics
3.2 Landslide characteristics
3.2 Landslide characteristics
3.2 Landslide characteristics
3.2 Landslide characteristics
3.2 Landslide characteristics
3.2 Landslide characteristics
3.2 Landslide characteristics
3.2 Landslide characteristics
3.2 Landslide characteristics
3.2 Landslide characteristics
3.2 Landslide characteristics
3.2 Landslide characteristics
3.2 Landslide characteristics
3.3 Slip zone characteristics
3.4 Sliding bed characteristics
4 Deformation Characteristics and Influencing Factors of Landslide
4.1 Deformation characteristics
4.2 Influencing factors of landslide
4.2.1 The influence of human engineering activities on landslide
4.2.2 Influence of stratum lithology on landslide
4.2.3 The influence of topography and geomorphology on landslide
5 Stability Analysis of Landslide
5.1 Calculation model and formula
5.2 Load combination
5.2.1 The dead weight of the slope
5.2.2 Seismic force
5.3 Selection of calculation parameters
5.4 Calculation results of landslide stability
6 Suggestions on Prevention and Control Measures
7 Conclusion
References
Simplified analysis of the seismic performance of symmetrical large chassis twin tower structure
1 INTRODUCTION
2 Project Overview
3 Structural Elastic Properties Under Frequently Occurred Earthquake
3.1 Dynamic properties
3.2 Overall indicators
4 Comparison of Structural Elastoplastic Properties Under Rarely Occurred Earthquakes.
4.1 Selection of seismic waves
4.2 Shear force and displacement analysis
4.3 Analysis of structural energy dissipation
5 Conclusions
References
Analysis of accident characteristics of vehicle spillage in a highway tunnel
1 Introduction
2 Statistics of Accidents Caused by Spills
2.1 The overall situation for three years
2.2 Statistics of different spills
2.3 Ways to find spilled materials
3 Discussion on Reducing Accidents Caused by Spillage
3.1 Video monitoring system for throwing objects
3.2 Dynamic monitoring of tunnel operation
3.3 Speed limit
3.4 Van transport
3.5 Improvement of lighting conditions
4 Conclusions and Suggestions
References
Wind tunnel numerical simulation analysis of a super high-rise building near the river
1 Introduction
2 Project Overview
3 Numerical Wind Tunnel Analysis
3.1 Analysis and modeling
3.2 Wind pressure on the main surface of the building
3.2.1 Building surface wind pressure under single working condition
3.2.2 Building surface wind pressure under working group conditions
4 Shape Coefficient of Wind Load on the Surface of the Main Building Based on Numerical Wind Tunnel Simulation Calculation
5 Conclusion
References
Study on the deformation characteristics of soft foundation reinforced by vacuum pre-pressure
1 Introduction
2 Typical Strain Model Under Vacuum Loading
2.1 Typical 3D isomorphic model
2.2 The lateral limit deformation model
2.3 The axisymmetric free deformation model
3 Deformation Characteristics of the Soil Within the Vacuum Pre-Pressure Reinforcement Zone
4 Deformation Characteristics of the Soil at the Boundary of the Vacuum Pre-Pressure Reinforcement Zone
4.1 Lateral deformation pattern of the soil at the boundary of the reinforcement zone
4.2 Depth of lateral soil deformation at the reinforcement zone boundary
5 Equation for Calculating Lateral Deformation in Vacuum Pre-Pressure
5.1 Theoretical formulae for lateral deformation
5.2 Engineering applications
6 Conclusion
References
The applications of superelastic NiTinol material in bridge seismic resistance
1 Introduction
2 Materials
2.1 Phases of shape memoir alloys
2.2 Shape memory effect
2.3 Superelastic effect
2.4 Commonly used SMA materials.
3 SMA Basic Elements
3.1 SMA wires, cables, and bars
3.2 SMA helical springs
3.3 SMA Belleville washer springs
3.4 SMA ring springs
4 Applications of Sma in Bridge Structure
4.1 SMA-based dampers and braces
4.2 SMA beam-to-column connections
4.3 SMA base isolators
5 Conclusions
References
Air purification test of enclosed interchange tunnel with covered
1 Introduction
2 Project Overview
3 Design of Tunnel Air Purification System
3.1 Composition of purification system
3.2 Matrix of soil
3.3 System of data processing
3.4 Experimental process
4 Analysis of Experimental Results
4.1 Purification efficiency of NOx
4.2 Purification efficiency of CO
4.3 Purification efficiency of PM10
5 Conclusions
References
Analysis of metro station ridership based on the process of access to metro and multi-scale geographic weighted regression: A case study of Zhengzhou metro
1 Introduction
2 Methodology
2.1 Dependent variable and independent variable selection
2.2 Regression method
3 Case Study, Results, and Discussion
3.1 Introduction to Zhengzhou metro
3.2 Variable selection and distribution
3.3 Analysis of regression results
4 Conclusion
References
Research on the evaluation of anti-liquefaction effect of stone column composite foundation in strong earthquake area
1 Introduction
2 Detection and Evaluation of the Anti-Liquefaction Effect of Stone Column Composite Foundation
2.1 Key parameters of the evaluation of the anti-liquefaction effect
2.2 Detection method of anti - liquefaction effect
2.2.1 Methods for testing stone columns
2.2.2 Methods for testing surrounding soil between columns
2.3 Evaluation of three major anti-liquefaction effects
2.3.1 Evaluation of drainage effect
2.3.2 Evaluation of soil densification effect between columns
2.3.2.1 Establish the densification acceptance curve based on SPT
2.3.2.2 Establish the densification acceptance curve based on CPT
2.3.2.3 Establish the densification acceptance curve based on the shear wave velocity Vs
2.3.3 Evaluation of the shear stress redistribution effect of stone column
3 Project Description
3.1 Geological condition
3.2 Anti-liquefaction design scheme and acceptance criteria
3.3 Construction technology of stone column
3.4 Detection and comprehensive evaluation of anti-liquefaction effect of stone columns
3.4.1 Detection and evaluation of the drainage function of stone column
3.4.2 Detection and evaluation of densification effect for surrounding soil
4 Conclusions
References
Construction workers detection using a deep convolutional neural network
1 Introduction
2 Related Work
3 Methodology
3.1 The architecture of cascade R-CNN
3.1.1 Residual network
3.1.2 Multi-scale feature network
3.2 Evaluation metrics
3.2.1 Precision and recall
3.2.2 mAP and AP75
4 Experiment and Results
4.1 Experimental environment
4.2 Experimental data
4.3 Experimental results
4.3 Experimental results
4.3 Experimental results
4.3 Experimental results
4.3 Experimental results
4.3 Experimental results
4.3 Experimental results
4.3 Experimental results
4.3 Experimental results
4.3 Experimental results
4.3 Experimental results
5 Conclusion
References
Strength prediction of rubber concrete based on particle swarm optimization
1 Introduction
2 Establishment and Realization of BP Neural Network Model
2.1 Establishment of BP neural network model
2.2 BP neural network model implementation
2.3 Analysis result
3 Establishment and Comparison of The PSO-BP Neural Network Model
3.1 Establishment of PSO-BP neural network model
3.2 Analysis of running results of PSO-BP neural network model
3.3 Comparison of BP neural network and PSO-BP neural network operation results
4 Comparative Analysis of Model Prediction Performance
5 Conclusion
References
Numerical analysis of the effect of bridge pile construction on the disturbance of adjacent oil pipelines
1 Introduction
2 Project Overview
3 Theoretical Analysis of the Effect of Pile Excavation on the Role of Pipelines
3.1 Deformation of the soil outside the pile excavation hole and the affecting factors
3.2 The deformation and damage mechanism of the pipeline
3.3 Oil pipeline safety control standards
4 Numerical Simulation Analysis of the Impact of Pile Excavation on Pipelines
4.1 Model construction
4.2 Calculation parameters
4.3 Boundary conditions
4.4 Overall model stress and displacement analysis
4.5 Pipeline model stress, displacement analysis
4.6 Pile excavation on the pipeline impact analysis conclusions
5 Conclusion
References
Analysis of the influence law of waterproof curtain setting on seepage flow in deep foundation pit
1 Introduction
2 Influence of Waterproof Curtain on Seepage of Foundation PIT
3 Influence of Curtain Depth on Seepage of Foundation PIT
4 Influence of Curtain width on Seepage of Foundation PIT
5 Conclusions
References
Simulation study on flowability and flatness of reclamation area under different pipeline layouts
1 Introduction
2 Material and Method
2.1 Study area
2.2 Research technical route
2.3 Model establishment
2.4 Model settings
3 Results and Analysis
3.1 Flow property analysis
3.2 Flatness analysis
3.3 Economic analysis
4 Conclusion
References
Analysis of dewatering effect of deep foundation pit excavation on deformation of surrounding structures
1 Introduction
2 Project Overview
3 Numerical Simulation
3.1 The first kind of condition calculation
3.2 The second kind of condition calculation
3.2.1 The water level drops once to the bottom of the pit (condition 2.1)
3.2.2 Dewatering while excavating (condition 2.2)
3.2.3 Alternate excavation of foundation pit
3.2.3 Alternate excavation of foundation pit
3.2.3 Alternate excavation of foundation pit
3.2.3 Alternate excavation of foundation pit
3.2.3 Alternate excavation of foundation pit
3.2.3 Alternate excavation of foundation pit
3.2.3 Alternate excavation of foundation pit
3.2.3 Alternate excavation of foundation pit
3.2.3 Alternate excavation of foundation pit
3.2.3 Alternate excavation of foundation pit
3.2.3 Alternate excavation of foundation pit
3.2.3 Alternate excavation of foundation pit
3.2.3 Alternate excavation of foundation pit
3.2.3 Alternate excavation of foundation pit
3.3 The third kind of condition calculation
3.3.1 The distance between the building and the foundation pit is within 1 times the excavation depth of the foundation pit
3.3.2 The distance between the building and the foundation pit is within 1&x0007E;3 times the excavation depth of the foundation pit
3.3.2 The distance between the building and the foundation pit is within 1&x0007E;3 times the excavation depth of the foundation pit
4 Conclusions
References
Monitoring and analysis of temperature and strain development of long-scale concrete in cut-and-cover tunnels
1 Introduction
2 Monitoring Scheme
2.1 Project overview
2.2 Scheme design
2.3 Construction implementation
3 Results
3.1 Time-varying characteristics of the temperature inside concrete
3.2 Time-varying characteristics of internal and external temperature difference of concrete
3.3 Time-varying characteristics of strain inside concrete
3.4 Comparative analysis of temperature and strain
4 Conclusions
References
Strengthening and restoration of wooden roof frames of ancient buildings
1 Introduction
2 Overview of the Wooden Roof Frame of Ancient Buildings
3 Causes of Breakage of Wooden Roof Frames of Ancient Buildings
3.1 Material damage
3.2 Decay
4 Reinforcement of Wooden Roof Frames of the Ancient Building
4.1 Definition of reinforcement
4.2 General reinforcement method
4.2.1 Appearance defects and repair cracks
4.2.2 Node reinforcement
4.2.3 Rod reinforcement
5 Restoration Measures
5.1 Treatment of cracks
5.2 Anti-corrosion measures
6 New Technologies in Wood Roof Frame Reinforcement and Repair
6.1 Non-applicability of traditional reinforcement repair methods
6.2 New technologies in wood roof frame reinforcement and repair
6.2.1 Carbon fiber reinforcement technology
6.2.2 Glass fiber reinforcement technology
7 Conclusions
References
Leakage risk assessment of cofferdam tunnels based on combined weighting-cloud model
1 Introduction
2 Establishment of Evaluation Index
3 Comprehensive Evaluation Method
3.1 The initial weights based on AHP
3.2 Correction weights based on DEMATEL
3.3 Membership based on cloud model
3.4 Evaluation of leakage in cofferdam tunnel
4 Conclusions
References
Seismic behavior of the rigid frame bridge with tuning-fork-shaped thin-walled high pier under rare earthquake
1 Introduction
2 Dynamic Characteristics
3 The Effect of the Intersection Position in the Tuning-Fork-Shaped Thin-Walled High Pier on Seismic Responses
4 Comparative Study On Seismic Responses Of The Bridge With Double-Limb Thin-Walled High Pier
5 Conclusions
References
Analysis of subgrade effect of collapsible loess mixed with high plastic clay
1 Introduction
2 Effect Analysis of High Plastic Clay in Loess Roadbed
2.1 Properties of highly plastic clay
2.2 Influence of mixing high plastic clay on loess roadbed
3 Comparison of Finite Element Models
3.1 Material performance experiment
3.2 Comparison of finite element analysis
4 Conclusion
References
Study on vertical stiffness influence of three-tower cable-stayed bridge
1 Introduction
2 Engineering Overview
3 Finite Element Model
4 Analysis of Influencing Factors of Vertical Stiffness
4.1 Influence of auxiliary piers
4.2 Influence of tower height
4.2.1 All towers&x02019; heights rise
4.2.2 The side-tower height reduces
4.2.3 The mid-tower height rise
4.3 Influence of tower stiffness
4.4 Influence of beam height
5 Conclusion
References
Development characteristics and stability analysis of a dangerous rock zone in Sichuan
1 Introduction
2 Geological Background of Dangerous Rock Zone
2.1 Topography
2.2 Formation lithology
2.3 Hydrogeological conditions
3 Development Characteristics and Influencing Factors of Dangerous Rock Zone
3.1 Development profile
3.2 Instability mode analysis
3.3 Factors affecting the development
4 Stability Analysis of Dangerous Rock Mass
4.1 Qualitative analysis
4.2 Quantitative analysis
5 Conclusion
References
Selection design of rectangular pipe jacking machine for large section and long distance utility tunnel
1 Project Overview
1.1 Project introduction
1.2 Geological conditions
1.3 Engineering difficulties
2 Rectangular Pipe Jacking Machine Selection Design
2.1 Pipe jacking machine design requirements
2.1.1 Monitoring and correction of the jacking direction
2.1.2 Reasonable selection and arrangement of cutterhead and cutting tools
2.1.3 Treatment of excavation blind area
2.1.4 Control of pushing force
2.1.5 Control of surrounding soil deformation
2.1.6 Safety and economy
2.2 Determination of the type of pipe jacking machine
2.3 The main structure of the rectangular pipe jacking machine
2.3.1 Cutting mixing system
2.3.2 Correction and hydraulic system
2.4 Main parameters of pipe jacking machine
3 Key Technologies of Pipe Jacking Machine
3.1 Crushing device for the blind area of the excavation face
3.2 Reversible rectangular pipe jacking iron structure
4 Conclusion
References
Stress-strain relationship of recycled concrete under uniaxial cyclic compression
1 Introduction
2 Test Overview
2.1 Test materials
2.2 Design and manufacture of the test piece
2.3 Loading device and loading system
3 Test Results and Their Analysis
3.1 Failure phenomenon of cyclic compression test
3.2 Complete stress-strain curve and analysis
3.3 Plastic strain
3.4 Stress degradation
4 RCAC Cyclic Compression Stress-Strain Relationship
4.1 Envelope equation
4.2 Unloading curve
4.3 Reload curve
4.4 Verification of stress-strain relationship under cyclic compression
5 Simplification of the Stress-Strain Equation Under Cyclic Compression
6 Conclusion
References
Finite element analysis of seismic behavior of reinforced concrete columns strengthened with engineered cementitious composite
1 Introduction
2 Simulation Process
3 Conclusion
References
Bathymetry estimation using ensemble adjustment Kalman filter in the numerical simulation of M2 tide
1 Introduction
2 Datasets and Methods
2.1 Datasets
2.2 Hydrodynamic model
2.3 An ensemble adjustment Kalman filter
3 Data Assimilation Experiment Setup
3.1 Sensitivity analysis
3.2 Data assimilation experiment
4 Results
4.1 Bathymetry estimation based on NAO.99Jb
4.2 Performance of NTD
5 Summary and Discussion
References
Author index

Citation preview

ADVANCES IN TRAFFIC TRANSPORTATION AND CIVIL ARCHITECTURE

Advances in Traffic Transportation and Civil Architecture focuses on the research of traffic infrastructure. This proceeding gathers the most cutting-edge research and achievements, aiming to provide scholars and engineers with a preferable research direction and engineering solutions as reference. Subjects in these proceedings include: — — — —

Road Engineering Bridge Engineering Tunneling Construction Technology and Processes

The works of this proceedings aim to promote the development of civil engineering and construction technology. Thereby, promote scientific information interchange between scholars from the top universities, research centers and high-tech enterprises working all around the world.

PROCEEDINGS OF THE 5TH INTERNATIONAL SYMPOSIUM ON TRAFFIC TRANSPORTATION AND CIVIL ARCHITECTURE (ISTTCA 2022), SUZHOU, CHINA, 19–20 NOVEMBER 2022

Advances in Traffic Transportation and Civil Architecture Edited by

Run Liu Tianjin University, China

Chongchong Qi Central South University, China

Teik-Hua Law Universiti Putra, Malaysia

First published 2023 by CRC Press/Balkema 4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN e-mail: [email protected] www.routledge.com – www.taylorandfrancis.com CRC Press/Balkema is an imprint of the Taylor & Francis Group, an informa business ’ 2023 selection and editorial matter Run Liu, Chongchong Qi & Teik-Hua Law; individual chapters, the contributors The right of Run Liu, Chongchong Qi & Teik-Hua Law to be identified as the authors of the editorial material, and of the authors for their individual chapters, has been asserted in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. Although all care is taken to ensure integrity and the quality of this publication and the information herein, no responsibility is assumed by the publishers nor the author for any damage to the property or persons as a result of operation or use of this publication and/or the information contained herein. ISBN: 978-1-032-51438-3 (hbk) ISBN: 978-1-032-51439-0 (pbk) ISBN: 978-1-003-40222-0 (ebk) DOI: 10.1201/9781003402220 Typeset in Times New Roman by MPS Limited, Chennai, India

Advances in Traffic Transportation and Civil Architecture – Liu, Qi & Law (eds) © 2023 the Editor(s), ISBN 978-1-032-51438-3

Table of Contents xv xvii

Preface Committee Members

Traffic safety and transport management planning Airport group flight optimization in the context of air-rail combined transportation Jiajia Jiang, Baoyin Li, Tianxiang Huang, Shaolan Lv, Qixin Liu & Jianhong Sun Simulation research on urban distribution capacity allocation based on spatial right of way Mengke Zhang & Danhua Fu Evaluation of public transport service quality based on improved SERVQUAL model Liping Wang & Junjun Bian

3

14 21

Research on the optimization of high-speed rail express operation process—taking Nanning East Station as an example Xiaxi Li

29

Study on the environmental benefits of developing Sea-River through transportation in the Shandong Bohai Bay region Shuming Liu & Yuhua Zhu

37

Research on the influence of exit obstacles on crowd evacuation efficiency based on pathfinder Lu Wang, Lihong Yue, Menghua Yang & Haijing Zhang

46

Traffic theory research on the attractiveness of subsidy policy for new energy vehicles based on the multinomial logit model Dawei Wu & Lu Ma

55

Analysis of the emission factors of new energy vehicles compared to fuel vehicles during the lifecycle Sicheng Bing, Runze Hong & Jun Li

62

A model for shared parking optimization for supply and demand matching considering overtime parking Danhua Fu, Xiangdi Li, Mengke Zhang & Fan Zhang

77

Application of traffic simulation in urban expressway traffic organization design Cong Liu, Fangqing Zhang & Wei Chen

84

Multi-location inventory optimization model in supply chain environment Fei Meng

v

91

Research on collaborative scheduling mechanism and method of automatic guided vehicles in complex warehouse Yingli Liu, Minghua Hu & Jiaming Su

101

Socioeconomic contribution of rural road construction and revampment: A case study in China Di Wu, Xu Ji & Chen Qiuren

115 124

Noise cancellation in highway construction Yufan Zhang Integrated emergency evacuation and supply scheduling for disaster emergency rescue Yanping Zhang & Na Cui

133

Case study of cathodic protection of concrete piles to extend the service life of marine jetty Haihong Li, Xiang Fang & Chunlin Deng

140

Comparison of carbon and pollution emissions of conventional diesel and new energy short-haul heavy-duty trucks over their full life cycle based on the GREET model and assessment of influencing factors Huangchen Luo

146

A cellular automata model with simulation-based optimization for multi-lane expressway Mo Chen

154

Construction quality evaluation of expressway asphalt pavement based on digital construction Zhaoguang Hu, He Jiang & Daxian Deng

161

Study on the potential impact about monitoring by remote sensing of the urban center of gravity changes on sustainable development of urban transportation Bin Li, Yi Du, Liwei Lu & Wenlong Yu

169

Comparative analysis of the lateral stability of double-trailer combination Yue Li, Hao Zhang & Hongwei Zhang Study on energy consumption and emission of heavy-duty vehicle fuel in whole life cycle Yuanze Sun, Zexi Zhang & Shihao Zhang The optimization of bus passenger dwelling behaviors’ simulation model Yuxuan Lin

176

184 193

Economic benefits evaluation of cold central plant recycling and the measures to reduce costs and increase benefits Ying Qin & Jin Wang

202

Research on the development strategy of chinese rural transportation adapting to the population migration Menghan Liu, Na Li, Yi Liu & Wei Chen

209

vi

Traffic network and transport system modelling Calibration method of pavement lateral force coefficient based on passive excitation Lu Liu, Jiacheng Cai, Lei Chen & Bing Zhang

221

The dynamic strategy of evacuation based on neural network for ocean liner Hao Liu & Zhiyong Lv

231

Route optimization of express network considering high-speed rail express and carbon emission Jiaxin Niu, Peng Zhao & Ke Qiao

240

Target market positioning of container railway-sea express Yifei Wu

249

Congested roadway resilience evaluation based on taxi GPS data LingYun Liao

256

More robust and better: Automatic traffic incident detection based on XGBoost Junyu Chen, Pan Wu, Jinlong Li & Lunhui Xu

267

Reliability analysis of fresh agricultural products cold chain transportation system based on fault tree-bayesian network Yongqiang Zhao & Meizhen Zhang

275

Multi-discipline speed control matching evaluation for urban rail transit Haizhu Hong Research on microwave radar non-contact detection technology for expressway bridges Jian-Feng Gan, Fa-sheng Wei, Liang Gao, Fa-Qiang Rao, Kai-Qiang Qin & Kai-Quan Liu

284

291

Underground mine rail transport system based on CBTC Pingming Huang & ZeRui Liu

298

Design of science and technology management information system Hualong Cai, Caiying Huang, Yiman Zhong & Shijie Liu

306

Analysis of travel characteristics of clustered city based on mobile signaling data taking Shijiazhuang City as an example Qingqing Li, Shuai Wang, Baopeng Li & Lingyan Gu

314

Study on intercity intermodal passenger transport behavior in Chengdu and Chongqing region Xingying Chen, Yuxuan Wang, Yihe Xu & Yinsong Kou

324

Research on GIS detection and scheduling of unmanned vehicles based on convolutional neural network Xingwang Qiu & Meixiu Shi

341

A study on factors affecting drivers’ dangerous driving Zengzhi Zhang, Zefan Zhang, Kaiyuan Meng & Zeru Jin

vii

352

Comprehensive evaluation of transport infrastructure development capacity in five central Asian countries based on factor analysis Chen Zhang, Wenwen Jiao & Yuxi Zheng Traffic flow prediction based on the spatiotemporal attention Cong Qian & Aihua Wu Research on pedestrian trajectory prediction based on deep learning and pedestrian interaction Dongshu Ling, Xingang Li, Yunchao Qu & Qiyue Zhang

359 368

380

Study on the evaluation system of green port development in Suzhou Xiangtong Chen, Sudong Xu, Nini Zhang, Zhengchun Sun & Bing Han

388

A methodology for flight departure delay prediction based on stacking technique Xunuo Wang, Zhan Wang, Yong Tian & Lili Wan

397

Simulation analysis of pedestrian flow in densely populated places – A large school building as an example Entian Qie, Qingchen Deng, Mengting Li & Wei Cheng The traffic control policy of highway system in China under COVID-19 Dan Shu, Hanlin Xiao, Linqiang Fan, Liying Tang, Ruiting Qi, Jia Zhang & Yugang Liu 3D scene construction of railroad engineering facilities based on multi-source data fusion Yan Xuan & Feng Han Design of wave-maker system for harbor basin driven by servo motor Ping He, Yunjia Sun, Qingze He & Chen Li

407 419

434 443

A method for evaluating development mode of port-city based on a multiple relative concentration index model Jun Huang, Haiyuan Yao & Shanshan Bi

450

Analysis of the influence of wheelset mode on the high-speed dynamic performance of train Ze Li

462

A hydrodynamic model of steering tilting stability of partially-filled Tank Truck Xinming Lin & Lieyun He

470

Building material property and construction technology Study on the synthesis and performance of low-sensitivity polycarboxylate superplasticizer for machine-made sand concrete Guangxing Lai, Jianli Yin, Yuanqiang Guo & Tianxing Lin

483

Mechanical properties of asymmetric composite welds in the steel shell of immersed final joints along Shen-Zhong link Bin Deng, Qingfei Huang, Jian Liu, MingHu Liu, WenLiang Jin & Hai Ji

489

Design and research of through reinforced concrete arch bridge on soft soil foundation Jinliang Qiu, Shuiping Fang, Fang Huang & Shuang Wang

502

viii

Research on fine tension control technology of hectometre dense steel strands Yong-dong Wei, Jing Guo, Jun Zhao, Xu-dong Qin & Xin-Kun Wang

509

Research on deformation and stress characteristics of locking steel pipe pile cofferdam under different working conditions Wen Zhang & Bowen Song

515

Design scheme and key measures of artificial island under complex environment Peng Hu Research on debris flow movement law and impact performance based on discrete element method Fengyuan Wu, Pengfei Qin, Zihao Zhao & Chao Wang

523

534

Application of mechanical hoisting equipment in prefabricated buildings Hong Chen, Wei Luo, Guobao Ning, Limei Geng & Wei Lu

542

Research status and prospect of highway W-beam guardrail anti-collision technology Songrui Liu, Lei Yan, Ping Zhang, Longfei Cheng, Xiaoying Gou & Tiansen Chen

551

Comparative study of damage characteristics of PHC pile under different seismic ground motions Hongqiang Hu, Gang Gan, Yangjuan Bao, Xu Han & Xiaopeng Guo

558

Study on sustainability assessment of zero energy buildings based on emergy method – Taking the architecture of Nanjing ecological park as an example Junxue Zhang, Yan Zhang, Li Huang & Dan Xu

565

Effects of corner rounding modification on flow characteristics around the rectangular pier Linjia Yang & Xiaoyong He

571

Study on assembled floating slab track of suburban railway Zijie Li

580

Effect of salt spray corrosion on fatigue crack growth rate of 60Si2MnA steel Yuan Gong, GuoShou Liu, ShiYue Wang, Zeng Wu, Kai Bao & RongMao Zhang

593

Research on construction technology of fabricated reinforced concrete piers based on grouting sleeve type Chunyan Li

602

Key technologies for the construction of super-long and wide double-layer catenary-shaped upper-stiffened steel truss bridges Cang-Hua Zhou & Yang Wang

608

Design and construction technology of ultra-high jacking pier support in mountain area Jinhua Xu, Shuang Wang, Chao Wang & Xiaopeng Yuan

615

Dynamic response analysis of beam bridge with breathing crack subject to vehicle loads Weilong Wang & Kun Zhang

628

Research status of steel slag in intermittent graded asphalt mixtures Ruzhu Dong, Feng Yan & ShiYue Wang

ix

636

Pavement surfacing on bridges—The Gussasphalt concrete preparing techniques of high-temperature resistance Ziyi Wang, Xv Wang & Shengxi Li

645

Interface protective layer based on plastic crystal composite electrolyte Yonghao Wang

653

Research on BIM-based sinking pre-tensioning construction technology Honglong Deng, Shiming Zhong, Xin Xie, Yawei Wang & Zhiqiang Hai

660

Research on thermal damage of wheel and rail considering material contact temperature change effect Shuai Wang & Zhidong Duan

667

Finite element study on flexural behavior of cold-formed thin-wall steel-glulam composite beams Li Liu, Canming Guo & Fuda Huang

677

Simulation study on shear performance of steel-bamboo composite I-shaped beam Zhanjiang Liu

685

Study on the crack resistance of composite bridge with ultra-high-performance concrete Hanwei Wang, Jie Cao & Jianping Lin

695

Chloride-ions penetration resistance of sulphoaluminate cement concrete modified by retarder and mineral admixtures Zhong-lu Cao, Guan-yuan Jia, Ping Liu & Zhong-chun Su

705

Experimental study on axial compressive behavior of concrete column with multiple pressure forming CFST cores Kun Zhang, Kaiqiang Wang, Qing Sun, Hui Yang, Qi Lin & Lei Huang

712

Rectangular pipe jacking and drag reduction technology for long distance silty formation Jiawei Du, Zhuoyu Guo, Xiaoxu Tian & Shuangyuan Wang

721

Snow melting performance of phase change material asphalt pavement based on data mining algorithm Kaiming Jiang, Shujian Wang, Chuansan Wu, Ling Han & Hui Zheng

729

Research on China’s potential LNG bunkering center selection based on AHP-TOPSIS method Ke Du & Kun Li

735

Civil structural surveying and reinforcement engineering 5G-enabled intelligent detection and green repair technology for exterior wall leakage Jianwei Cheng & Guanghui Zhang Reliability evaluation of small and medium span bridges in dry cold regions Kui Zhang

x

751 760

Construction of walkability index based on access to metro stations Yunhong Lin, Jin Zhang, Xu Wang & Junchen Dai Physical model test study on instability mechanism of large deformation of deep roadway surrounding rock Jianguo Zhang & Sheng Wang Stability analysis of reinforced soil slope based on possible slip mode Xuelei Zhang & Bin Li Study on the causes and countermeasures of pile cracking at an in-service high-piled wharf Yan Feng, Ye Wu, Senin Li & Zhicheng Yu Numerical study of the influence of volumetric block proportion on excavation-induced ground responses in the talus-like rock mass Song Yang, Shaoqiang Zhang, Dong Li, Yinlian Yi, Chuanbin Jiang & Xiaochang Li

766

777 785

794

800

Stability analysis and prevention suggestions for a landslide in Sichuan Province Zongbo Zhang, Chunfeng Yang & Min Yang

811

Simplified analysis of the seismic performance of symmetrical large chassis twin tower structure Wenjie Fu

819

Analysis of accident characteristics of vehicle spillage in a highway tunnel Rui Wang & Jiang Xie

828

Wind tunnel numerical simulation analysis of a super high-rise building near the river Ting Hu, Fang Deng, Zhihao Wen, Mingli Wang & Yan Zhang

836

Study on the deformation characteristics of soft foundation reinforced by vacuum pre-pressure Niu Fei & Qian Wei

842

The applications of superelastic NiTinol material in bridge seismic resistance Tao Xiong

851

Air purification test of enclosed interchange tunnel with covered Chun Liu, Jun Peng, Zaiheng Zhou, Zhengmao Cao, Liting Xiong & Guanghui Li

858

Analysis of metro station ridership based on the process of access to metro and multi-scale geographic weighted regression: A case study of Zhengzhou Metro Jin Zhang, Xu Wang, Yunhong Lin & Junchen Dai

865

Research on the evaluation of anti-liquefaction effect of stone column composite foundation in strong earthquake area Mingxing Zhu, Zhijun Liu, Xiaocong Liang & Jing Wang

876

xi

Construction workers detection using a deep convolutional neural network Yunfan Zhao, Binjin Chen, Meng Wang & Yi Wang Strength prediction of rubber concrete based on particle swarm optimization Pengliang Zhang, Chunfeng Yang & Chaoqun Chu

889 897

Numerical analysis of the effect of bridge pile construction on the disturbance of adjacent oil pipelines Jian Wu, Qi Shi, Jingxian Zhang & Wanmao Zhang

905

Analysis of the influence law of waterproof curtain setting on seepage flow in deep foundation pit Binlei Wang, Yu Liang, Yelin Chen & Kesheng Zhang

916

Simulation study on flowability and flatness of reclamation area under different pipeline layouts Hanghang Lyu, Ping Zhu, Yuchi Hao & Runli Tao

926

Analysis of dewatering effect of deep foundation pit excavation on deformation of surrounding structures Binlei Wang, Yelin Chen, Yu Liang & Kesheng Zhang

935

Monitoring and analysis of temperature and strain development of long-scale concrete in cut-and-cover tunnels Qingsong Xue, Bing Liu, Mengxi Zhang & Xiaonan Yao

945

Strengthening and restoration of wooden roof frames of ancient buildings Yongxin Zhou, Jiaxin Wu & Zhiyong Cheng

952

Leakage risk assessment of cofferdam tunnels based on combined weighting-cloud model Jianping Fang, Xiaoyi Shi & Mengxi Zhang

959

Seismic behavior of the rigid frame bridge with tuning-fork-shaped thin-walled high pier under rare earthquake Shan Chang, Bo Dong, Jianxiang Ling, Ling Yuan & Wei Zhang

967

Analysis of subgrade effect of collapsible loess mixed with high plastic clay Weiqing Liu, Zhen Yao, Dongwei Cao & Chenke Sun

975

Study on vertical stiffness influence of three-tower cable-stayed bridge Pan Wu, Junhao Tong, Yaoyu Zhu & Yongtao Wang

981

Development characteristics and stability analysis of a dangerous rock zone in Sichuan Zongbo Zhang, Chunfeng Yang & Min Yang

989

Selection design of rectangular pipe jacking machine for large section and long distance utility tunnel Qingsong Xue, Yuan Zhang, Yinhao Sun & Zhanping Song

998

Stress-strain relationship of recycled concrete under uniaxial cyclic compression Dongsheng Chen, Bo Li, Tao Gao & Yani Hui

xii

1005

Finite element analysis of seismic behavior of reinforced concrete columns strengthened with engineered cementitious composite Shoushuo Zhang

1017

Bathymetry estimation using ensemble adjustment Kalman filter in the numerical simulation of M2 tide Haowen Wu, Guijun Han, Wei Li, Bin Li & Zhichao Dong

1022

Author index

1031

xiii

Advances in Traffic Transportation and Civil Architecture – Liu, Qi & Law (eds) © 2023 the Editor(s), ISBN 978-1-032-51438-3

Preface It is a great pleasure for the editorial committee to introduce the proceedings of the 2022 5th International Symposium on Traffic Transportation and Civil Architecture (ISTTCA 2022), hosted by Tianjin Port Engineering Institute Co., Ltd. of CCCC First Harbor Engineering Co., Ltd., Tianjin University, Water Transport Engineering Committee of the China Institute of Navigation, Tianjin Water Transport Engineering Association, and Huaiyin Institute of Technology. The conference took place in Suzhou, China during November 19th-20th, 2022 (virtual form), with the attendance of about 150 scholars, experts, researchers and other practitioners in related fields. ISTTCA 2022 established an academic forum where current research results and technical advances can be exchanged and the development of related areas in traffic transportation and civil architecture can be promoted and existing partnerships strengthened while new collaborations fostered. For this reason, specialists and researchers in related fields have come from all over the world to present their research development, share insights and experiences, and explore future directions of the state-of-the-art fields. To make this conference on schedule, we had a three-part plenary conference including a keynote speech part delivered by eight speakers. Among them, Prof. Xiang Ping from Central South University, China performed a keynote speech on the title: Seismic Performances and Running Safety Analysis of Train-Bridge Coupled System. The safety prevention and control of high-speed railway only focuses on the safety of bridge structure, but fails to consider the safety of train-bridge system as a comprehensive whole. His study established a simulation platform of train-track-bridge system under earthquake, carried out the running safety shaking table test, and put forward the running safety suggestions of highspeed railway under near fault earthquakes, providing important theoretical basis and technical support for the construction of high-speed railway in earthquake areas. We are very optimistic that all participants will acquire new ideas and knowledge from this conference to enhance the capability toward traffic transportation and civil architecture. We have received tremendous paper submissions from all over China and abroad. Through a rigorous peer-review process, all submissions were performed double blind review to check their quality of content, level of innovation, significance, originality and legibility. Based on the expertise review comments, some excellent papers were accepted and the corresponding authors were invited to submit their papers for final publication. The accepted papers included topics on: Transportation Safety, Metropolitan Transportation, Rail and Transit Systems, Structural Engineering, Modern Building Technology, etc. We would like to thank the Organizing Committee, Technical Committee and other Committee members for the procedures that lead to the organization of this conference, to the members of the CRC Press LLC for their patience while waiting for the final versions of the papers and to every author who has contributed with an article to the proceedings. We hope all of you will benefit from all the manuscripts of the proceedings and learn some new and novel ideas. The Committee of ISTTCA 2022

xv

Advances in Traffic Transportation and Civil Architecture – Liu, Qi & Law (eds) © 2023 the Editor(s), ISBN 978-1-032-51438-3

Committee Members General Conference Chairs Professor, Run Liu, Tianjin University, China Professor, Said Easa, Ryerson University, Fellow of the Canadian Academy of Engineering, Canada Professor-level Senior Engineer, Jinfang Hou, Tianjin Port Engineering Institute Co., Ltd. of CCCC First Harbor Engineering Co., Ltd., China Technical Committee Chairs Professor, Run Liu, Tianjin University, China Professor, Yanlong Li, Xi’an University of Technology, China Proceedings Chair Senior Engineer, Bin Li, Tianjin Port Engineering Institute Co., Ltd. of CCCC First Harbor Engineering Co., Ltd., China Organizing Committee Chairs Senior Engineer, Guangsi Chen, Tianjin University, China Senior Engineer, Bin Li, Tianjin Port Engineering Institute Co., Ltd. of CCCC First Harbor Engineering Co., Ltd., China Technical Committee Professor-level Senior Engineer, Aimin Liu, Tianjin Port Engineering Institute Co., Ltd. of CCCC First Harbor Engineering Co., Ltd., China Engineer, Binbin Xu, Tianjin Port Engineering Institute Co., Ltd. of CCCC First Harbor Engineering Co., Ltd., China Engineer, Zhifa Yu, Tianjin Port Engineering Institute Co., Ltd. of CCCC First Harbor Engineering Co., Ltd., China Engineer, Wenzhong Zhang, Tianjin Port Engineering Institute Co., Ltd. of CCCC First Harbor Engineering Co., Ltd., China Professor, Lei Wang, Changsha University of Science & Technology, China Professor, Li Ma, Southwest University of Science and Technology, China Professor, Xu Zhang, Henan University of Technology, China Professor, Shuitao Gu, Chongqing University, China Professor, Wei Qin, Chongqing University, China Assistant Professor, Anupoju Rajeev, National Institute of Technology Andhra Pradesh, India D.C. Haran Pragalath, British Applied College, Umm Al Quwain, UAE Assistant Professor, Houssam Khelalfa, Assoc. Professor Selinus University of Sciences and Literature (SUSL), Italy Branch Associate Professor tenured, Young-Jin Cha, University of Manitoba, Winnipeg, MB, Canada Doctor, Subhan Ahmad, Aligarh Muslim University, India Associate Professor, Saeed Ghaffarpour Jahromi, Shahid Rajaee Teacher Training University, Iran Engineer, Muhammad Awais Hussain, National Skills University, Pakistan Assoc. Prof., Bon-Gang Hwang, National University of Singapore, Singapore Dr. Salman Riazi Mehdi Riazi, Universiti Sains Malaysia, Malaysia Dr. Kim Hung Mo, Faculty of Engineering, University of Malaya Malaysia

xvii

Organizing Committee Engineer, Jianbao Fu, Tianjin Port Engineering Institute Co., Ltd. of CCCC First Harbor Engineering Co., Ltd., China Engineer, Zhonglu Cao, Tianjin Port Engineering Institute Co., Ltd. of CCCC First Harbor Engineering Co., Ltd., China Lecturer, Chengfeng Li, Tianjin University, China Engineer, Yingxue Lv, Tianjin Port Engineering Institute Co., Ltd. of CCCC First Harbor Engineering Co., Ltd., China Engineer, Jingshuang Li, Tianjin Port Engineering Institute Co., Ltd. of CCCC First Harbor Engineering Co., Ltd., China Professor, Ruiqing Lang, Tianjin Chengjian University, China Engineer, Changyi Yu, Tianjin Port Engineering Institute Co., Ltd. of CCCC First Harbor Engineering Co., Ltd., China Engineer, Aijun Zhuge, Tianjin Port Engineering Institute Co., Ltd. of CCCC First Harbor Engineering Co., Ltd., China Engineer, Jingjing Zhang, Tianjin Port Engineering Institute Co., Ltd. of CCCC First Harbor Engineering Co., Ltd., China Professor, Xiaodong Zheng, Xi’an University of Technology, China Engineer, Xiaoqiang Kou, Tianjin Port Engineering Institute Co., Ltd. of CCCC First Harbor Engineering Co., Ltd., China Engineer, Yiteng Xu, Tianjin Port Engineering Institute Co., Ltd. of CCCC First Harbor Engineering Co., Ltd., China Professor, Lifeng Wen, Xi’an University of Technology, China Lecturer, Yang Xu, Tianjin University, China Lecturer, Chao Liang, Tianjin University, China

xviii

Traffic safety and transport management planning

Advances in Traffic Transportation and Civil Architecture – Liu, Qi & Law (eds) © 2023 copyright the Author(s), ISBN 978-1-032-51438-3

Airport group flight optimization in the context of air-rail combined transportation Jiajia Jiang Nanjing University of Aeronautics and Astronautics, Nanjing, China

Baoyin Li Jiangsu Provincial Railway Office, Nanjing University of Aeronautics and Astronautics, Nanjing, China

Tianxiang Huang & Shaolan Lv Nanjing University of Aeronautics and Astronautics, Nanjing, China

Qixin Liu China Eastern Airlines Jiangsu Limited, Nanjing, China

Jianhong Sun* Nanjing University of Aeronautics and Astronautics, Nanjing, China

ABSTRACT: In this paper, air-rail intermodal transport is used to evacuate some international aviation hub flights with poor operating efficiency to neighboring airports. The optimal allocation of long-haul flights in airport clusters is studied. The evaluation system of hub airport flights is constructed from four perspectives: hub development, operation efficiency, flight competitiveness, and consumer benefits. The improved entropy weight TOPSIS method is used to evaluate and sort international aviation hub flights and screen the flights to be optimized. Considering the three parties of the airport, airline, and passenger, a multiobjective flight time optimization model is established to maximize the efficiency of airport punctuality, airline market share, and passenger travel cost. Taking the airports in the Yangtze River Delta as an example, a fast non-dominated sorting genetic algorithm with an elite retention strategy is used to solve the problem. The example results showed that the model can effectively evacuate inefficient flights from international aviation hubs to surrounding airports to alleviate congestion at hub airports and promote the collaborative development of airport clusters.

1 INTRODUCTION Although the demand for civil aviation transportation has been increasing in recent decades, there are still problems, such as unbalanced development, unclear functional positioning, and a high degree of route homogeneity among multi-airport systems in China. These problems are reflected in the excessive saturation of hub airports and insufficient passenger flow at surrounding small and medium-sized airports. Facing the shortage of time and resources in the hub airport is the key to solving the contradiction between passenger demand and capacity supply. Most airports choose to expand terminals, build new runways, and other

*

Corresponding Author: [email protected]

DOI: 10.1201/9781003402220-1

3

measures to increase operational capacity, but this will lead to heavy civil aviation infrastructure construction tasks, industry development capital pressure, and will be limited by airspace resources. In this context, using a rich high-speed rail network combined with routes of the combined transport mode becomes a more realistic choice. The development of highspeed rail is a challenge and an opportunity for the civil aviation industry. Airport and railway systems are connected, increasing the accessibility of airports, and providing strong support for the construction of our integrated, comprehensive transportation system. Therefore, reasonable evaluation of the flight time of hub airports, the use of rich high-speed rail resources to evacuate inefficient flights from first-class international hub airports in the multi-airport system, and the creation of air-rail combined transportation products can improve the current situation of unbalanced development of airports in the multi-airport system. There are many research achievements on flight time resource optimization at home and abroad. Zografos et al. (2012), Jacquillat et al. (2013), and Qi Li (2016) studied the allocation of flight time resources at a single airport from different perspectives, such as congestion, delay, and flight wave. Through the establishment of the optimization model for flight deployment, a single airport’s flight time optimization model is closer to the practical application. In addition, many scholars optimized airlines and flights from the air traffic control perspective. For example, Chen Bin et al. (2021), Xiang Zheng et al. (2021), and Jiang Hao et al. (2022) respectively optimized the flight time of a single airport by focusing on the problems of traffic limitation, waypoint conflict, and departure sequencing. In the research field of airport group as a whole, Wang Xinglong (2022), Wang Lili (2021), Li Ang 2020), and Shui Xiaoyu (2023) optimized the flight time of multi-airport system from the perspective of reducing flight delay and optimizing the utilization of airspace resources. From the perspective of collaborative development, De Neufville R. (1995), Wei Wei (2014), Xia Yuzhong (2014), Chen Xin (2014) et al. proposed that the collaborative development of the multi-airport system in China is still in the initial stage of exploration. Attention should be paid to the cooperation mode and development path, and the batch adjustment of airline flights should be made, which put forward new ideas for optimizing airline flights in China. The above studies mainly optimized flight time to reduce delay and improve performance (stability and reliability) without considering the optimization of flight resources from the perspective of functional positioning and multi-airport system collaboration. In addition, the existing studies on flight optimization of airport groups rarely consider the impact of air-rail intermodal travel on the resource allocation of multi-airport systems and among airports. Xia (2019) believed that air-rail intermodal travel can effectively redistribute traffic volume and relieve airport congestion in the new generation of the multiairport system. Zhang et al. (2018) emphasized that air-rail intermodal travel can improve not only the accessibility of airports but also relieve the pressure of hub airports. It is proved that the competitive advantage of air-rail combined travel is mainly reflected in the long-distance routes over 1000 km. Therefore, this paper constructs a comprehensive evaluation index system for long-haul flights at first-level hub airports. It takes the hub development index, operation efficiency index, flight competitiveness index, and consumer benefit index as the criterion layer. It adopts the improved entropy weight TOPSIS method (Sun & Shao 2017) to evaluate and sort flights and screen out flights to optimize. Then, from the perspective of airports, airlines, and passengers, a flight time optimization model is constructed to maximize the benefit of the airport group. It is solved by a fast nondominated multi-objective optimization genetic algorithm (NSGA-II) with an elite retention strategy (Wang et al. 2021). Finally, the Yangtze River Delta multi-airport system is an example of verification.

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2 FLIGHT EVALUATION INDEX SYSTEM Starting from the evacuation of flights inconsistent with their functional positioning and low operating efficiency in the first-class aviation hub airport, in order to improve the passenger flow of regional airports, promote the coordinated development of airport groups, and take into account the competitiveness of flights and the convenience of passengers, the flight evaluation index system criteria are divided into four categories: hub development index, flight operation efficiency index, consumer interest index, and flight competitiveness index. Each category of indicators is divided into several detailed specific indicators. This paper selects long-haul flights as the research object to construct the evaluation index system of long-haul flights at hub airports, as shown in Table 1. The improved entropy weight TOPSIS is used to evaluate and sort the long-haul flights of the first-class hub airport and select the flights to be optimized. Table 1.

Evaluation index system of long-haul flights at first-class hub airports.

Rule layer

Index layer

Definition of Indicator

Hub Development Indicators

Type of navigable point x1 Navigable point cohesion x2

Operational efficiency index

Available seat km x3 Average load factor during the season x4 Airline market share x5 Degree of homogeneity x6 Flight frequency x7 Punctuality of flights x8

Type I: 1.0 for international aviation hubs; Type II: 0.8 for regional aviation hubs Type III: 0.6 at other airports The total number of navigable points at navigable airports Flight
number of aircraft seats Average load factor during the season

Flight competitiveness index

Consumer benefit indicator

The ratio of the number of flights on this airline to all flights on the line If the route is available at all major airports, the value is 1; otherwise, the value is 0 Weekly flight frequency Season average on-time rate

The hub development index mainly examines whether the flights conform to the functional positioning of international aviation hubs. The operational efficiency index selects two sub-indexes, available seat kilometers and flight load factor, which reflect the capacity input of the flight and the full utilization of time resources of the flight, respectively. The flight competitiveness index is mainly considered from two perspectives: airline market share and flight homogenization degree. Generally speaking, airline market share is positively correlated with flight competitiveness, and the larger the market share, the stronger the competitiveness. However, the degree of homogeneity reflects the route duplication of major airports in the airport group (referring to the total market share of more than 80%). If homogeneity is too high, it easily leads to vicious competition. This paper selects two classical indicators, flight frequency, and punctuality, as consumer interest indicators. The higher the flight frequency, the more convenient it is for passengers to travel. The higher the flight punctuality rate, the smaller the delay cost of passenger travel. The above indicators are all positive benefit indicators except the degree of homogeneity.

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3 FLIGHT OPTIMIZATION MODEL The improved entropy weight TOPSIS method is used to evaluate and rank the flights, and the flights in the last order are taken as the flight objects to be optimized. Then the take-off and landing airports and take-off and landing times are redistributed. Considering the interests of airports, airlines, and passengers, a multi-objective flight optimization model with the highest on-time rate of airports, the highest market share of airlines, and the lowest travel cost of passengers is established. Among them, to reduce the travel cost of passengers in different places, the high-speed railway schedule is introduced into the model to shorten air and rail transfer time and create a convenient air-rail intermodal travel product. First, an airport’s on-time performance is related to weather and traffic control flow, whether the airport is saturated or not, and whether the flight time resources are tight. Second, because of airlines’ market share, considering that air-rail intermodal travel mainly applied to long-haul destinations. The selected flight objects for evaluation are domestic flights over 1000 km from first-class international hub airports, ignoring the competition of high-speed railways within this distance. At the same time, the optimal allocation of flights in the multi-airport system must also consider the sum of the economic cost and time cost of passengers in different places. Only on the basis that the cost of the optimized travel mode is not greater than that of the original, the possibility of passengers taking flights to different places can be realized. 3.1

Model assumes

In order to specify the function orientation of airports, avoid the vicious competition between the airport and ease the hub airport congestion, promote the development of the multi-airport system synergy, along with the development of the “public transport” highspeed network, the government, for the promotion of the comprehensive transportation system, this paper adopts air-rail intermodal travel to transfer some traffic volume from the hub airport to the surrounding airports. Based on the existing planned flight schedule, this paper reallocates some flights with poor flight quality and low operation efficiency in the hub airport to meet the requirements of airport group strategic planning. The modeling is based on the following assumptions: 1. Each airport in the multi-airport system is operated and managed by a unified management organization, and airport resources and transportation tasks can be coordinated and optimized. 2. Do not consider the transfer situation of other passengers to first-class international hub airports. 3. Ignoring the waiting time of air and rail intermodal transport. 4. Ignoring the difference in flight time of aircraft taking off from each airport in the multiairport system. 5. Keep the flight arrival airport and the operating airline the same. 3.2

Objective function

According to the above flight optimization principles, the optimal flight allocation model of the multi-airport system is established with the optimization objectives of the maximum ontime rate of airports, the largest market share of airlines, and the minimum travel cost of passengers. max f ðxÞ ¼ ½f1 ðxÞ; f2 ðxÞ; f3 ðxÞT

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(1)

XX

f1 ðxÞ ¼

xuv ij Pj

(2)

xuv ij Auj

(3)

jJ vV

f2 ðxÞ ¼

XX jJ vV

f3 ðxÞ ¼ 

XX

xuv ij ðCjd þ ct Tjd Þ

(4)

jJ vV

Cjd ¼ ca Djd

(5)

ct ¼ M=ðP  TÞ

(6)

Tjd ¼ Tgj0 þ Twj0 þ zj ðTgh0 þ Twh0 þ Th0 h þ Thj þ Tv Þ

(7)

Equation (1) represents the maximization of objective function f(x). xuv ij , zj indicate variable of 0-1, xuv indicating that the flight i operated by airline u is assigned to take off and ij land at time v at airport j. zj indicates that the flight to be optimized is allocated to airport j other than j0. j0 indicates the airport where the flight to be optimized is located. j 2 J; v 2 V Pj is the average punctuality rate of airport j. Auj is the market share of airline u in airport j. Cjd is the economic cost (fare) from airport j to destination d. ca is the airline base price, averaging 0.75 yuan per passenger kilometer. Djd is the distance from airport j to destination d. ct is the average per capita time value of the airport city where the flight to be optimized is located. M and P are the regional GDP and population of the flight to be optimized in the current year, respectively, and T is the legal working time of 250 days a year. Tjd is the travel time from airport j to destination d, Tgh0 and Twh0 the ground traffic time, waiting time, and security check time at the airport j0, respectively, all are set to 1.5 hours. Tgh0 Moreover, Twh0 respectively arrive at the high-speed rail station h0 for ground transportation and waiting time. Security check time, set to 0.5 hours, indicating the highspeed rail running time from the high-speed rail station h0 to the high-speed rail station h (excluding h0). Thj is the transfer time from high-speed railway station h to airport j and Tv is the adjustment amount of flight time, that is, the planned delay time of passengers.

3.3

Constraints

3.3.1 Flight uniqueness constraint The flight uniqueness constraint means that a flight can only have one take-off and landing airport and one take-off and landing time, like equation (10) and equation (11): X

xuv ij ¼ 1

(8)

xuv ij ¼ 1

(9)

j2J

X v2V

3.3.2 Peak hour capacity limits There is a capacity limit on the number of inbound and outbound flights at the airport, which is prone to pile-up and congestion. Therefore, the capacity constraints of landing and take-off airports should be considered when adjusting flights. In this paper, the airport capacity of a 5-minute time slice is taken as the unit for constraint. Cjv represents the airport

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take-off and landing capacity of j airport at time v, as shown in equations (12): XX xuv ij  Cjv

(10)

v2V j2J

3.3.3 Constraints in connecting high-speed rail and flights A successful H-A connection or A-H connection must meet the lower transfer time limit, greater than the minimum transfer time. Thj  tlow hj

(11)

4 THE EXAMPLE ANALYSIS Take the Yangtze River Delta airport cluster as an example. In 2019, the Yangtze River Delta airport group handled 248 million passengers, ranking first among China’s four major airport groups. There are 23 airports in the Yangtze River Delta, and the two airports in Shanghai accounted for 46% of the passenger throughput in 2019. Among the navigation points of Pudong International Airport, 37% overlap with those of surrounding airports. It can be seen that the development of the Yangtze River Delta airport group is extremely uneven, and the degree of airline homogeneity is relatively high. First, the improved entropy TOPSIS method was adopted at Shanghai Pudong International Airport from August 4, 2019 to August 10, 2019. This week’s peak hour flight to evaluate and sort according to the evaluation index is shown in Table 1. Screening the flight of not tally with the function orientation of Shanghai Pudong International Airport, high degree of homogeneity, and lower operational efficiency as an optimization object. Then, considering the distance from Shanghai Pudong International Airport, high-speed rail frequency, air rail transfer time, and other factors, Shanghai Pudong International Airport, Hangzhou Xiaoshan International Airport, Nanjing. Lukou International Airport, Changzhou Benniu International Airport, Nantong Xingdong International Airport, and Sunan Shuofang International Airport were selected as the research objects. Finally, the average remaining capacity flights at each airport during the week from August 4 to August 10, 2019 were taken as the remaining capacity flights at each airport during the week for flight optimization. Figure 1 shows the evaluation results of flights at Shanghai Pudong International Airport from August 4, 2019, to August 10, 2019, using the improved entropy weight TOPSIS method. Flights are divided into five classes according to the average distribution of the distance from the positive ideal solution, and the number of each color bar represents the number of flights in the current class. A total of 237 peak-hour flights of Grade 4 and Grade 5 were selected as the objects to be optimized. Among them, 89.74% were flights with inconsistent functional positioning, 82.82% were flights with a high degree of homogeneity, 108 took off and 129 landed. Use Python to call the Geatpy library NSGA-II algorithm programming solution. According to the calculation results, there were 237 low-efficiency flights in the peak hours of Shanghai Pudong International Airport from August 4 to August 10, 2019, and a total of 73 flights were adjusted. The optimization results are shown in Table 2. Two evaluation indexes of comprehensive quality value F and optimization effect IR are introduced, and the pre-optimization result is set as fij(i = 1,2,3, . . . ,m) and the postoptimization result as fij0 ði ¼ 1; 2; 3; . . .; mÞ then the comprehensive quality value and

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Figure 1. Table 2.

Flight evaluation grade distribution map. Partial flight optimization results. Before optimization

The optimized

Flight time to be Flight optimized no.

Airport Airline on-time market performance share

Direct Airport flight cost/ on-time yuan perfo

Airline market share

Air-rail intermodal travel cost/ yuan

800 815 850 900 900 935 1010 1040 1705 1730 1800 1840 1845 2105 2110 2155

79.81% 79.81% 76.42% 79.81% 76.42% 76.42% 76.42% 79.81% 79.81% 76.42% 79.81% 76.42% 76.42% 76.42% 76.42% 79.81%

1228.80 1475.55 1072.05 1159.05 1498.05 1072.05 1287.30 1222.05 1336.80 1068.30 1228.80 1159.05 1228.80 1003.80 1117.80 1228.80

9.01% 13.79% 33.20% 13.79% 5.34% 33.20% 10.00% 33.20% 20.01% 10.04% 9.01% 10.98% 9.01% 0.15% 9.01% 20.01%

1118.66 1431.32 881.63 1031.00 1441.35 902.59 1212.63 1139.83 1319.35 1092.80 1114.73 1029.69 1106.87 943.54 1188.90 1114.66

HO1153 CA1258 MU9694 CA9651 HU7319 MU255 FM9168 MU5263 CZ6544 FM9198 HO1153 CZ8518 HO1148 KN2315 HO1286 CZ3252

11.49% 8.10% 33.00% 8.10% 2.06% 33.00% 8.74% 33.00% 10.94% 8.74% 11.49% 10.94% 11.49% 0.78% 11.49% 10.94%

78.52% 78.16% 73.13% 78.16% 76.50% 73.13% 76.50% 75.04% 75.04% 76.50% 78.52% 75.12% 76.50% 76.50% 75.12% 75.04%

optimization effect of the ith flight are respectively. Fi ¼

3 X

fbij

(12)

j¼1

fij0  fij IRij ¼



fij

9

(13)

Where fij represents the jth objective function value of the ith flight. fbij represents the standardization of fij, and the standardization formula is fbij ¼

fij  minfij i

i

i

Table 3.

(14)

max fij  min fij

Sorting out the optimization results of some flights.

Flight time to be Flight optimized no.

Comprehensive Optimization Optimization Optimization quality before effect IR of f1 effect IR of f2 effect IR of f3 optimization F

Comprehensive quality after optimization F

800 HO1153 815 CA1258 850 MU9694 900 CA9651 900 HU7319 935 MU255 1010 FM9168 1040 MU5263 ... ... 1705 CZ6544 1730 FM9198 1800 HO1153 1840 CZ8518 1845 HO1148 2105 KN2315 2110 HO1286 2155 CZ3252 The average

0.016 0.021 0.043 0.021 0.001 0.043 0.001 0.060 ... 0.060 0.001 0.016 0.017 0.001 0.001 0.017 0.060 /

0.967 0.526 1.398 1.169 0.255 1.792 0.796 1.579 ... 0.616 2.000 0.945 1.083 0.903 1.066 0.854 0.479 0.632

0.216 0.702 0.006 0.702 1.592 0.006 0.144 0.006 ... 0.829 0.149 0.216 0.004 0.216 0.207 0.216 0.829 /

0.090 0.030 0.178 0.110 0.038 0.158 0.058 0.067 ... 0.013 0.023 0.093 0.112 0.099 0.060 0.064 0.093 /

0.496 0.021 0.994 0.995 0.200 1.398 0.363 0.673 ... 0.317 1.000 0.496 0.087 0.496 0.500 0.662 0.222 0.541

Take flight time 850 and flight number MU9694 as an example. The original flight took off from Shanghai Pudong International Airport, but now it takes off from Nanjing Lukou International Airport. The general aviation airport is Taiyuan Wusu Airport, a regional hub and more in line with the functional positioning of Nanjing Lukou International Airport. The homogeneity degree is higher, and the flight to Shanghai Pudong international airport, Shanghai Hongqiao international airport. Hangzhou Xiaoshan international airport and Nanjing Lukou international airport have opened the route. Moreover, the optimization of transfers passenger needs to Nanjing Lukou international airport, making Shanghai spare time resource arrangement for international flights and improving international market share. The punctuality rate and airline market share of the original flight at Shanghai Pudong Airport were 76.42% and 33.00%, respectively. The punctuality rate and airline market share of the optimized flight in Nanjing Lukou International Airport were 73.13% and 33.20%, and the IR values of the first two indicators were 0.043 and 0.006, respectively. However, the passenger travel cost is 1072.05 yuan from the original direct flight cost, 881.63 yuan from the adjusted air-rail combined transportation route cost, and the optimization effect value IR is 0.178, which significantly reduces the travel cost of passengers, and the optimized comprehensive quality F is 0.404 higher than that before optimization. Regarding the overall optimization effect, the

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average comprehensive quality F increased by 16.82% from 0.541 to 0.632, indicating a good optimization effect. Table 4 describes the changes in other indicators before and after optimization. Combined with the adjustment of flight frequencies, a total of 73 flights in peak hours were optimized, 34 flights with a high degree of homogeneity were reduced, and the total travel cost of passengers was reduced from 1,793,526 yuan to 1,787,950 yuan. The optimized flights are all flights with a high degree of homogeneity and inconsistent functional positioning, and the optimized travel costs of passengers are also reduced to a certain extent, making it a reality for passengers to take flights to different places. Figure 2 shows the schematic diagram of some flights before and after optimization. The optimization results are in line with expectations. In conclusion, the model can effectively allocate the flights that are not in line with the functional positioning of first-class international hub airports, with a high degree of homogeneity and low operating efficiency, to the surrounding airports, alleviating the congestion

Table 4.

Sorting out the optimization results of some flights.

Indicators

Before optimization

After optimization

Peak hour flight volume (flights) Flights with a higher degree of homogeneity (flights) Flights with inconsistent functional positioning (flights) Total passenger travel costs (yuan)

1505 1106 825 1793526

1432 1072 803 1787950

Figure 2.

Schematic diagram before and after optimization of some flight.

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situation of Shanghai Pudong Airport during peak hours. It increases the passenger flow of the surrounding airports and promotes the coordinated development of airport groups.

5 CONCLUSIONS Aiming at the airport in the local airport group development uncoordinated and route problem such as high degree of homogeneity, function orientation needs to be clarified, this paper constructs the comprehensive evaluation index system. The primary hub airport flight put forward under the background of empty rail transport airport flight optimization model of the NSGA–II algorithm, airport group of Yangtze River Delta as example proved the model’s validity. 1. Function orientation from the airport and the airport group synergy development point of view, selection of flight hub development, operational efficiency, and competitiveness, such as consumer interest as the criterion layer on the International hub airport to establish a comprehensive evaluation index system, with the improved entropy TOPSIS method effectively filter out do not tally with the hub airport function, high degree of homogeneity, and low operating efficiency. 2. In a comprehensive consideration of the interests of airports, airlines, and passengers, a high-speed railway schedule is introduced to build an optimization model of flight with the largest punctuality rate of airports, the largest market share of airlines, and the lowest travel cost of passengers and air-rail combined transportation products are created. Taking the Yangtze River Delta Airport cluster as an example, a total of 73 peak-hour flights were optimized from Shanghai Pudong International Airport to the surrounding airports within a week, The flights with a high degree of homogeneity were reduced by 34, the flights with inconsistent functional positioning were reduced by 22, the total travel cost of passengers was reduced by 5,576 yuan, and the average comprehensive quality F was increased from 0.541 to 0.632. The practical application can promote the coordinated development of the airport group and provide a basis for the integrated construction of regional transportation.

REFERENCES Ang L. and Wenpeng Z., An Qi. Flight Time Optimization of North China Airport Cluster for Punctuality Improvement. Computer Simulation, 2020, 37(02):38–40. Bin C., and Wenpeng Z. Study on Flight Time Optimization based on Non-cooperative Game. Science Technology and Engineering, 2021, 21(22):9615–9619. De Neufville R. Management of Multi-Airport Systems: A Development Strategy. Journal of Air Transport Management, 1995, 2(2): 99–110. Hao J., Jixin L., Xinfang D. Dynamic, Collaborative Scheduling Method for Departing Flights based on Traffic State [J/OL]. Journal of Beijing University of Aeronautics and Astronautics:1–18 [2022-09-07]. DOI:10.13700/ j.B.1001-5965.2021.0066. Jacquillat A. and Odoni A.R. Congestion Mitigation through Schedule Coordination at JFK: An Integrated Approach. 2013. Jiancheng S., and Xiaoming S. Applying Topsis Model Based on Improved entropy Weight in Route Evaluation. China and Foreign Highways, 2017, 37(5): 6–9. Li Q. Research on Flight Time Optimization of Hub Airport Based on Flight Wave Operation [D]. Tianjin: Civil Aviation University of China, 2016. Lili W. and Yongya L. Traffic Information and Safety, 2021, 39(05):93–99 + 136. Shui X.Y., Wang Y.J. and Wang Z.M. et al. Slot Allocation of the Multi-airport System Considering Airport Fairness. Acta Aeronautica et Astronautica Sini-ca, 2023, 44(): 327212(In Chinese). DOI: 10.7527/S10006893.2022.27212

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Wan-qiu W., Ling-yun X., Ming-hui M. and Yu-Bin Q. Driver Model of Two-lane Highway Curve based on Nsga-ii. Journal of Highway and Transportation Science and Technology, 2021, 38(12):131–138 + 146. Wei W. Research on Key Theories and Methods of Coordinated Operation Management of the Yangtze River Delta Airport Cluster based on Hub Airport [D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2014. Xia W., Jiang C., Wang K et al. Air-rail Revenue Sharing in a Multi-airport System: Effects on Traffic and Social Welfare. Transportation Research Part B: Methodological, 2019, 121: 304–319. Xin C., Haiyan W., Junhui L., et al. Evaluation of Coupling and Coordinated Development Degree of a Multi-airport Complex System in Yangtze River Delta Region. Transportation System Engineering and Information, 2014,14 (3): 214–220. Xinglong W., Yanfeng X., Yichen X. Evaluation and Optimization of Airport Group Departure Flight Time stability [J/OL]. Journal of Beijing University of Aeronautics and Astronautics:1-16[2022-09-06]. DOI:10.13700/ j.B.1001-5965.2021.0462. Yuzhong X. Research on Multi-airport Cooperative Development Model in Beijing [D]. Beijing: Beijing Jiaotong University, 2014. Zhang F., Graham D.J., Wong M.S.C. Quantifying the Substitutability and Complementarity between Highspeed Rail and Air Transport. Transportation Research Part A: Policy and Practice, 2018, 118: 191–215. Zheng X., Yi-peng Z., Wen-jun Z. and Cheng-Xiang L. Optimization of Departure Flight Time in the Multiairport Terminal Area based on Control Handover. Science Technology and Engineering, 2021, 21 (22):9628–9633. Zografos K.G., Salouras Y., Madas M.A. Dealing with Efficiently Allocating Scarce Resources at Congested Airports. Transportation Research Part C, 2012, 21(1):244–256.

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Advances in Traffic Transportation and Civil Architecture – Liu, Qi & Law (eds) © 2023 copyright the Author(s), ISBN 978-1-032-51438-3

Simulation research on urban distribution capacity allocation based on spatial right of way Mengke Zhang* & Danhua Fu Transportation Development Research Center. Zhejiang Scientific Research Institute of Transport Zhejiang Scientific Research Institute of Transport, Hangzhou, China

ABSTRACT: With the green development of the logistics industry, urban distribution, as an essential end link in the logistics system, need to promote the use of new energy logistics vehicles. The concept of multi-agent is introduced, and the interaction logic among different subjects is clarified to evaluate the effect of the right-of-way priority policy on the promotion of new energy delivery vehicles quantitatively. The distribution capacity allocation model is built in Anylogic software, and different distribution strategies for new and traditional energy delivery vehicles are obtained through simulation. At the same time, the right-of-way classification strategy is proposed, and an example verifies the effectiveness of the right-ofway priority policy. The study shows that implementing right-of-way differentiation will increase the utilization rate of new energy distribution vehicles.

1 INTRODUCTION China attaches great importance to addressing climate change in the face of serious environmental pollution, worsening traffic congestion, and energy shortages (Xin 2018). The state has proposed relevant policies on constructing low-carbon and green transport systems to promote the distribution’s green development. Policy documents such as the Guidelines on Building a Low-Carbon Transport System and the Guidelines on Accelerating the Development of Green, Transport were released. Running new energy vehicles has been regarded as one of the effective means to reduce carbon emissions in urban distribution. Therefore, finding a reasonable method to promote the use frequency of new energy vehicles is of great research value. Relying on fiscal and tax subsidy policies such as purchase subsidy and tax exemption; Chinese new energy vehicles have rapidly developed in recent years. However, with the slowdown of the government’s fiscal subsidy policy, the practical problems of short driving range and long charging time are gradually exposed. The popularity of new energy distribution vehicles is greatly restricted (Xiong & Liu 2022). Therefore, it is necessary to effectively play the positive role of “non-subsidy” policies in promoting new energy distribution vehicles. “Right of way priority” is a major “non-subsidy” policy in promoting and applying new energy in China at the present stage. That is to promote the use of new energy delivery vehicles by giving new energy vehicles the priority of driving. Domestic and foreign scholars have researched the road space right of way. Starting from the concept of the right of way distribution, some domestic scholars study the legal guarantee of travelers’ right of way distribution and improve the current right of way distribution with legal regulations. Some literature examines operational research and queuing theory to build a road network model, simulate the setting of bus lanes, and propose the setting conditions *

Corresponding Author: [email protected]

14

DOI: 10.1201/9781003402220-2

and specific schemes for bus lanes (Chen & Liu 2020; Ma & Zhou 2014; Shao et al. 2015; Wu 2018; Zhang 2019). There is no independent “right of way” concept in foreign countries. As a result, the research on the right of way could be more comprehensive, mainly from the perspective of fairness and efficiency (Tian 2019). Few works of literature conducted quantitative research on the effect of right-of-way priority implementation. This paper established a simulation model of distribution capacity allocation involving both new energy vehicles and fuel vehicles, proposed a right-of-way classification strategy, explored different distribution strategies for new energy distribution vehicles and traditional fuel distribution vehicles in different situations, and quantitatively evaluated the effect of “right-ofway priority” measures on improving the utilization rate of new energy distribution vehicles.

2 THE LOGIC OF DISTRIBUTION CAPACITY RESOURCE ALLOCATION The distribution capacity resource allocation problem considering new energy vehicles is described in the whole distribution vehicle system, including different elements, logistics parks, distribution nodes, charging piles, traditional fuel vehicles, new energy distribution vehicles, and orders. Different numbers of vehicles need to be arranged for different distribution nodes, and different departure times need to be determined to meet the order requirements of distribution nodes. At the same time, new energy delivery vehicles must find the nearest charging pile to charge when the battery runs out. 2.1

Distribution capacity allocation logic considering new energy vehicle charging

The distribution capacity allocation flow chart of new energy vehicle charging is designed to solve this problem, as shown in Figure 1. After the order demand is generated, first judge whether idle vehicles are in the nearest logistics park. If both types of vehicles are idle, judge whether the remaining electricity of the new energy distribution vehicles is sufficient. If the remaining power of the new energy distribution vehicle is not enough to support the

Figure 1.

Logical flow chart.

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round-trip, traditional fuel vehicle transportation will be arranged. If both types of vehicles meet the conditions, the existing vehicles are randomly selected. 2.2

Distribution capacity allocation logic considering space right of way

A link is added based on the original logic to evaluate the right-of-way differentiation scheme. That is to judge whether the traditional fuel vehicle has the right of way at the distribution node. If not, the new energy distribution vehicle is selected for urban distribution. Figure 2 shows the flow chart of distribution capacity allocation considering the spatial right-of-way.

Figure 2.

Logical flow chart considering the spatial right-of-way.

3 DISTRIBUTION CAPACITY ALLOCATION METHOD BASED ON MULTI-AGENT 3.1

Overview of the multi-agent simulation model

The modeling in this study is mainly based on the following assumptions:(1) All vehicles start from and eventually return to the logistics park. (2) There is plenty of goods in the logistics park. (3) The cargo-carrying capacity of different models of vehicles is the same, which can and can only meet the needs of one order. (4) Avoid traffic jams, accidents, and other emergencies. 3.2

Agent establishment

A hybrid multi-agent system structure is established. The working process of vehicles is completed between the logistics park and the distribution nodes. The number of working vehicles is determined by determining the location information between the two, the number of vehicles owned by the logistics park or distribution center, and the order demand of the end nodes. At the same time, the distribution of charging piles and the charging time required by new energy distribution vehicles are considered. The model sets up six agents: distribution node, logistics park, the quantity of order, traditional fuel distribution vehicle, new energy distribution vehicle, and charging station.

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3.3

Agent interaction logic

3.3.1 Distribution node agent There are two tasks in the end-node agent, generating order requirements and finding a distribution center. After the order is generated, the logic of finding a distribution center is as follows: First, consider whether there are idle vehicles in the distribution center that can reach the end node within a limited time. If the distribution center meets the conditions, the nearest distribution center is selected to send the order requirement. 3.3.2 Logistics park agent The distribution center agent performs two main tasks, taking orders and finding vehicles. If an order is generated, it starts looking for vehicles. The logic of new energy delivery vehicles looking for orders is when the order demand is issued, the model is randomly selected. If the new energy delivery vehicle is selected, the logic of Figure 1 will determine whether there are idle new energy vehicles that meet the time limit. If there is a vehicle, a vehicle will be sent at random to complete the transport task. The logic of fuel vehicles is similar to that of new energy distribution vehicles, which will not be repeated here. 3.3.3 Charging station agent When all ports of the charging station are working, the station is in busy mode. Other vehicles will not be charged in this mode. 3.3.4 Traditional fuel distribution vehicle agent The traditional fuel vehicle Agent is built in the distribution center Agent subordinate. The state transfer process is as follows: traditional fuel vehicles stay in different logistics parks in the initial state, set off for distribution nodes after receiving the order message, unload the cargo at the destination, and return to the destination by the feasible route after the unloading is completed. Finally, state selection is carried out. If there is still an order, it goes directly to the next terminal node. It will be stopped and returned to the logistics park if there is no order. 3.3.5 New energy distribution vehicle agent The state transfer of the new energy distribution vehicle is similar to that of the traditional fuel distribution vehicle, except for one more choice state. The new energy distribution vehicle needs to judge whether the power is sufficient after the cargo is unloaded. If not, it needs to find a charging pile to charge. The charging state will last for some time. When the new energy delivery vehicle needs to be charged, it should find the charging pile closest to the delivery vehicle, and the charging port should be idle to charge. The realization of the power consumption of the new energy distribution vehicle is as follows: the initial full power of the new energy distribution vehicle can run continuously for 6h. It does not consume power when in the distribution center and consumes power when leaving the distribution center. 3.3.6 Order agent Order agent is the basis of city distribution behavior generation. Before assigning orders, traditional cars and new energy vehicles will be selected, and preference will be given to vehicles that can complete the distribution activities faster.

4 CASE STUDY 4.1

Simulation parameter setting

This study’s research area is set in downtown Suzhou, and traditional and new energy logistics vehicles are used for urban distribution services. In this simulation, representative 17

Table 1.

Parameter settings.

Parameter

Value

Note

Time of Discharge Charge Time Endurance of New Energy Vehicles Vehicle Operating Speed

8min 30min 6h

Time required for unloading the vehicle at the end node New energy vehicle charging time The running time of the vehicle after a full charge

50km/h

The running speed of the delivery vehicle on the road section

charging stations are selected from the actual construction stations, including 4 logistics parks, 10 terminal nodes, and 6 charging stations. The normal speed of each vehicle in the city is set at 50 km/h, and the charging time of new energy delivery vehicles is set at 0.5h. In the simulation, the unit of measurement is minutes. 4.2

Right-of-way scheme setting

In the distribution capacity optimization scheme of the simulation model, the differential setting of right-of-way is reflected by setting the latitude and longitude range that fuel vehicles cannot pass through, as shown in Table 2. In scheme one, there is no difference in the right of way between the two types of vehicles. In scheme two, fuel vehicles cannot pass in a small part of the area, while in scheme three, the impassable range of fuel vehicles is further expanded. Table 2. Scheme 1

Description of the scheme. Scheme 2

Scheme 3

Both types of vehicles New energy vehicles can pass through can pass through the the whole region, while traditional fuel whole area. vehicles cannot pass within the scope of Longitude (120.55–120.7), Latitude (31.27–31.4).

New energy vehicles can pass through the whole region, while traditional fuel vehicles cannot pass within the range of Longitude (120.5–120.8), Latitude (31–31.7)

5 RESULT ANALYSIS The simulation model was run according to the parameters set in Section 4. The proportions of traditional fuel vehicles and new energy delivery vehicles in the orders received by different distribution nodes in different periods within 12h were counted. Table 3 lists the simulation results. As can be seen from Table 3, in the 0h-4h stage, when the new energy distribution vehicles do not need to go to the charging pile for secondary charging, the distribution nodes will give the traditional fuel vehicles and new energy distribution vehicles equal opportunities to transport goods after receiving the demand for transporting goods. At the 4h-8h and 8h-12h, the new energy delivery vehicles run out of power and need to charge for half an hour at the charging pile. It can be seen from the results that the proportion of new energy delivery vehicles in the total delivery vehicles is reduced. The chance of getting transportation orders is reduced, and the attractiveness of new energy delivery vehicles in the market is reduced compared with traditional energy vehicles.

18

Table 3.

Simulation results without right-of-way restrictions. 0–4h

4–8h Models

8–12h

Models

Models

End Quantity Node of order Number required.

Traditional fuel vehicle

New energy vehicle

Quantity of order required

Traditional fuel vehicle

New energy vehicle

Quantity of order required

Traditional fuel vehicle

New energy vehicle

1 2 3 4 5 6 7 8 9 10

5 8 11 7 4 8 2 7 6 3

3 8 9 9 4 12 2 5 10 5

16 32 40 32 16 40 8 24 32 16

10 20 25 21 10 24 6 15 19 11

6 12 15 11 6 14 2 9 13 5

24 48 60 48 24 60 12 36 48 24

15 30 41 30 16 42 7 21 32 16

9 18 19 18 8 18 5 15 16 8

8 16 20 16 8 20 4 12 16 8

According to the Settings of operation schemes 2 and 3 mentioned in Section 4, the model is run to calculate The times of different types of vehicles participating in transportation orders per hour within 12h. The simulation results are shown in Table 4. As seen from Table 4, when traditional fuel delivery vehicles are restricted to a small range, the opportunity for new energy delivery vehicles to get orders is higher than when there is no right-of-way difference. When traditional fuel delivery vehicles are restricted to a large extent, the opportunity of new energy delivery vehicles to get orders even exceeds that of traditional fuel vehicles in reverse.

Table 4.

Comparison of operation results under different rights of way. Quantity of Transport Orders Involving Different Types of Vehicles No right-of-way difference

Fuel vehicles are restricted to a small area

Fuel vehicles have been banned in a large area

Run Time

Traditional fuel vehicle

New energy vehicles

Traditional fuel vehicle

New energy vehicles

Traditional fuel vehicle

New energy vehicles

01:00:00 02:00:00 03:00:00 04:00:00 05:00:00 06:00:00 07:00:00 08:00:00 09:00:00 10:00:00 11:00:00 12:00:00

15 18 16 15 23 15 17 18 24 21 19 16

17 14 16 17 10 8 14 14 12 8 13 10

14 10 10 10 11 10 17 12 13 14 16 10

18 20 21 19 20 15 17 19 20 18 13 17

7 9 12 10 10 8 9 10 8 11 9 6

25 22 21 24 23 15 18 20 23 20 15 15

19

Combined with Table 3 and Table 4, it can be seen that the right of way of traditional fuel vehicles is gradually reduced. The proportion of new energy delivery vehicles in the total delivery vehicles is gradually increased, and the opportunity to get transportation orders is also increased, indicating that their competitiveness in the transport capacity market is enhanced. In conclusion, the right-of-way differentiation scheme will increase the proportion of new energy vehicles in the transport capacity system and promote the improvement of vehicle structure in the current transport capacity system.

6 CONCLUSIONS Based on the multi-agent simulation modeling technology in the Anylogic platform, a simulation model of distribution vehicle allocation was established to simulate the distribution scheme of different types of vehicles in logistics parks. The right-of-way optimization scheme was proposed, and the scheme was brought into the model for simulation. According to the analysis of the simulation results, it was confirmed that the optimization scheme could improve the utilization rate of new energy distribution vehicles. It is helpful to promote the green development of the urban distribution network capacity system and provides a reference for the government to formulate new energy distribution vehicle promotion policies. Future research can be carried out from two aspects. The first is to expand the calculation example and simulate the order quantity under the real situation. Secondly, the carbon emissions of vehicles delivered under different right-of-way policies should be calculated to analyze the impact of the policies on carbon emissions.

REFERENCES Chen L. and Liu X.(2020). Discussion on the Method of Bus Lane Setting at Home and Abroad. (03),35–40. Ma X. and Zhou. Z. (2014). Urban Optimal Right of Way Allocation Method Model Analysis. Road Traffic and Safety (05), 44–48. Shao Y., Wang X., Li J., Ma Y. and Liu Z. (2015). Urban Road Cross Section Planning Under the Concept of Complete Street. Urban Transportation, (01),25–33. Tian. X. (2019). The Connotation and Structure of Right of Way Are Analyzed from the Perspective of Jurisprudence. Legal Expo. (07), 15–17. Wu .R. (2018). Research on Fine Design of Urban Regular Bus Lanes. Dalian University of Technology Xin L. (2018). New Energy Vehicles and Logistics Mutually Empower Each Other, Opening a New Industrial Era. Logistics Technology and Application, 07: 92–94. Xiong Y. and Liu. H. (2022). The Role and Difference of “Non-subsidy” Policies in the Promotion and Application of New Energy Vehicles. Science Research Management. (09),83–90. Zhang M. (2019) Jurisprudential Study of Right of Way. East China University of Political Science and Law.

20

Advances in Traffic Transportation and Civil Architecture – Liu, Qi & Law (eds) © 2023 copyright the Author(s), ISBN 978-1-032-51438-3

Evaluation of public transport service quality based on improved SERVQUAL model Liping Wang* & Junjun Bian School of Intelligent Manufacturing, Panzhihua University, Panzhihua, China

ABSTRACT: The quality of bus service is the most direct standard to measure the quality of bus passengers. Taking public transportation as the research object, based on the improved SERVQUAL gap model, a public transportation service quality evaluation system with 7 dimensions and 20 indicators was constructed. AHP was used to determine the weight of each index, and a fuzzy comprehensive evaluation model combined with the SERVQUAL gap model was used to evaluate the quality of public transportation services. Empirical research is conducted through a questionnaire survey, showing that the overall perceived value of urban bus service quality is 1.2579. The dimension of the difference between expectation and reality of bus service quality is comfort, the gap is a more prominent inconvenience, and the difference between expectation and reality is assurance. The scientific and practical research method can provide a reference for improving the urban bus service quality.

1 INTRODUCTION Faced with the increasingly serious problems of urban traffic congestion and environmental pollution, the priority development strategy of public transport has become a consensus of various countries. Scientific and reasonable evaluation of urban public transport service quality (Peng, Zhou, Zhang, Lin, Song, 2013) and analysis of the deficiencies in the public transport service system are the fundamental ways to improve the quality of public transport service and enhance the attraction of public transport. Public transport service quality has become a hot issue concerned by scholars. Wu Huirong (Wu, Cui, Zhang, 2012) and others used AHP and the improved SERVQUAL model to evaluate the service quality of urban public transport from six dimensions empathy, assurance, and reliability. Huang Houbo (Huang 2014) used AHP-fuzzy comprehensive evaluation method to study the satisfaction of bus passengers. Li Linbo et al. (Li, Lv, Ji, Wu, 2017) used the service quality gap model to explore the influencing factors of public transport service quality. They combined it with Logistic binomial regression to reflect passenger demand. Liu Ying (Liu, Yu, Liu, 2019) et al. used the AHP-BP neural network model to study the service quality of bus lines. However, the evaluation effect of current research methods has certain limitations. Through the adaptive improvement of the traditional SERVQUAL model, the fuzzy comprehensive evaluation combined with the SERVQUAL gap model is used to evaluate the quality of bus service, which can more accurately reflect the quality of bus service and analyze the factors affecting the quality of bus service, to provide auxiliary support for improving the quality of urban bus service.

*

Corresponding Author: [email protected]

DOI: 10.1201/9781003402220-3

21

2 IMPROVEMENT OF THE MEASUREMENT MODEL Because the traditional SERVQUAL model regards the importance of each dimension as the same, it is unreasonable. Passengers are subjective and different passengers attach different importance to each dimension, so it is particularly important to seek a more reasonable evaluation model. Based on the traditional SERVQUAL model, the weight of each dimension and each index is determined. The calculation formula of the improved SERVQUAL model is as follows: 0

SQi ¼ Wij

m  X

PSij  ESij



(1)

j¼1 0

In Equation (1), SQi is the weighted value of bus service quality of dimension i by passengers; Wij is the weight value of j index in dimension i; PSij is the actual bus service quality value of index j in dimension i perceived by passengers. ESij is passengers’ expected bus service quality value to question j in dimension i (the value of bus service quality that passengers are satisfied with); m is the total number of indicators contained in dimension i. When the service quality of bus dimension i is obtained, the overall perceived value SQ0 of bus service quality is further obtained, and the calculation formula is as follows: 0

SQ ¼ ai

n X

0

SQi

(2)

i¼1

In Equation (2), SQ0 is the overall perceived value of bus service quality; ai is the weight of 0 dimension i; SQi is the weighted value of bus service quality of dimension i by passengers; n is the total number of dimensions.

3 EVALUATION OF PUBLIC TRANSPORTATION SERVICE QUALITY 3.1

Constructing the evaluation index system of public transport service quality

Based on fully considering the travel needs of passengers, combined with a large number of research data, public transport service quality is divided into seven dimensions, such as convenience and comfort. The evaluation indexes are selected according to the dimensions, the weight of each index is determined by the analytic hierarchy process (AHP), and the evaluation index system of bus service quality is constructed with 20 weighted 7 dimensions, as shown in Table 1. 3.2

Fuzzy comprehensive evaluation model based on analytic hierarchy process

3.2.1 Building factor set The set of evaluation indicators at a certain level is called a factor set, denoted by the letter U, U ¼ fu1 ; u2 ; . . . ; un g, The sub-factor set is U i ¼ fui1 ; ui2 ; . . . ; uim g 3.2.2

Construct the evaluation set and numerical set V ¼ fV1 ; V2 ; V3 ; V4 ; V5 g ¼ fvery good; good; average; poor; very poorg Value set N ¼ fN1 ; N2 ; N3 ; N4 ; N5 g

The score is shown in Table 2. 22

Table 1.

Evaluation index system of bus service quality.

Serial Dimension number names

Weight ai

Index number 1

1 Convenience Q1

0.3528

2 3 4

2 Comfort Q2

0.0385

5 6 7

3 Reliability Q3

0.0600

8 9 10

4 Guarantee Q4

0.2247

11 12

5 Reactivity Q5

0.0245

13 14 15

6 Tangibles Q6

0.0747

16 17 18

7 Empathy Q7

0.2247

19 20

Table 2.

Evaluation indicators Time required to walk to the waiting point A1 The convenience of transferring to other routes A2 The method of bus payment A3 The air quality and temperature inside the bus B1 Congestion degree inside the bus B2 Stability of bus operation B3 Frequency and time of departure C1 Punctuality of bus arrival at destination C2 Bus stop announcement C3 Safety degree during bus operation D1 Clarity and accuracy of travel information D2 Service attitude of drivers and passengers D3 The smoothness of passengers’ feedback E1 Satisfaction with complaint results E2 Station service facilities complete degree F1 Waiting environment F2 Sanitation in the vehicle F3 Vehicle safety, service facilities complete degree F4 Attention to special passengers G1 Concern for passengers getting on and off the train G2

Absolute weight

Relative weights Wij

0.2594

0.7352

0.0704

0.1994

0.0231 0.0138

0.0654 0.3583

0.0226

0.5869

0.0021 0.0480

0.0548 0.8004

0.0045

0.0753

0.0075 0.1652

0.1244 0.7352

0.0147

0.0654

0.0448

0.1994

0.0220

0.9000

0.0024

0.1000

0.0034

0.0462

0.0143 0.0094 0.0475

0.1910 0.1265 0.6363

0.1498

0.6667

0.0749

0.3333

Score table.

V (Degree)

very good

good

average

poor

very poor

N (Number)

5

4

3

2

1

3.2.3 Build the weight vector The weight coefficients are combined to form weight vectors. Then, the weight vector corresponding to Ui is K i ¼ ðWi1 ; Wi2 ; . . . ; Wim Þ, the weight vector corresponding to U is K ¼ ða1 ; a2 ; . . . ; an Þ

23

3.2.4 Constructing the membership subset and membership matrix Construct the membership subset Ri ; Ri ¼ fri1 ; ri2 ; . . . ; rim g, Ri refers to the ith index in the evaluation factor corresponding to each membership degree of V1 ; V 2 ; . . . Vm , namely: rij ¼

ðNumber of the ith indicator Vi levelÞ ; The total number of participants in the evaluation

among them j ¼ ð1; 2; . . . ; mÞ

0

Ri1 B Ri2 B Ri ¼ B .. @ .

1

0

r11 C B C B r21 C¼@ r31 A rm1 Rim

3.2.5

r22 ri1 ri1 rm2

   

(3)

1 r1s r2s C C r3s A rms

A first-level and second-level fuzzy comprehensive evaluation

a) First-level fuzzy comprehensive evaluation. According to the weight vector Ki of Ui, the composite calculation of the matrix is obtained Bi ¼ Ki  Ri ¼ ðbi1 ; bi2    bis Þ Where * is the fuzzy operator and s = 5. For this Bi Press bij 0 Bi ¼ P5 j¼1

bij

(4)

The first level fuzzy vector is normalized. b) Second-level fuzzy comprehensive evaluation. The second-level fuzzy matrix can be obtained from the above first-level comprehensive evaluation results: 0 1 0 1 B1 b11 b22    b1s B B2 C B b21 bi1    b2s C B C C B ¼ B .. C ¼ B @ . A @ b31 bi1    b3s A bm1 bm2    bms Bn The matrix composition is Z = K * B. After normalizing it to get the second-order fuzzy vector Z0

4 EMPIRICAL RESEARCH 4.1

Questionnaire design

From the main part of the questionnaire, according to the contents in Table 1, the Likert five-level scale was used to evaluate the perceived value and expected value of bus service quality. 4.2

Reliability and validity analysis of the questionnaire

Using statistical analysis software SPSS Reliability and validity of 23 pairs of 224 valid questionnaires, the Cronbach a coefficient is 0.894 > 0.7, the KMO value is 0.907 > 0.8, and the explained value of the total variance is 0.70058 > 0.7, indicating that the reliability and validity are high, and the measurement results are reliable.

24

A total of 253 questionnaires were distributed, and 224 valid questionnaires were screened out. The sorting and analysis of the survey results are shown in Table 3. Table 3.

Actual and expected evaluation results of passengers on bus service quality. Rating level Very good

Level 1 indicators

The secondary indicators

Convenience Q1

Time required to walk to the waiting point A1 The convenience of transferring to other routes A2 The method of bus payment A3 The air quality and temperature inside the bus B1 Congestion degree inside the bus B2 Stability of bus operation B3 Frequency and time of departure C1 Punctuality of bus arrival at destination C2 Bus stop announcement C3 Safety degree during bus operation D1 Clarity and accuracy of travel information D2 Service attitude of drivers and passengers D3 The smoothness of passengers’ feedback E1 Satisfaction with complaint results E2 Station service facilities complete degree F1 Waiting environment F2 Sanitation in the vehicle F3 Vehicle safety, service facilities complete degree F4 Attention to special passengers G1 Concern for passengers getting on and off the train G2

Comfort Q2

Reliability Q3

Guarantee Q4

Reactivity Q5

Tangibles Q6

Empathy Q7

Good

Average

Poor

Very poor

Actual Expect Actual Expect Actual Expect Actual Expect Actual Expect 20

133

34

67

60

18

56

7

54

3

15

114

41

96

93

12

58

2

17

0

49

143

83

80

58

8

27

1

7

0

10

109

36

103

117

11

48

1

13

0

0

65

8

93

81

63

99

2

36

1

4

87

52

121

107

15

54

1

7

0

5

96

40

115

113

13

54

0

12

0

3

89

55

120

126

15

34

0

6

0

14

99

89

114

92

10

25

1

4

0

12

118

109

101

84

5

16

0

3

0

16

120

106

98

77

6

23

0

2

0

10

101

71

111

114

12

25

0

4

0

6

102

56

104

117

17

38

1

7

0

6

91

65

117

110

15

40

1

3

0

5

94

73

121

103

9

39

0

4

0

5

101

51

109

116

14

47

0

5

0

8

107

62

106

116

11

32

0

6

0

15

107

100

109

87

8

21

0

1

0

17

112

69

102

102

9

33

0

3

1

12

118

78

96

96

10

34

0

4

0

25

4.3

A fuzzy comprehensive evaluation of bus service quality

4.3.1 Fuzzy comprehensive evaluation calculation According to Table 3, we can get the following factors U1 ¼ fA1 ; A2 ; A3 g; U2 ¼ fB1 ; B2 ; B3 g; U3 ¼ fC1 ; C2 ; C3 g; U4 ¼ fD1 ; D2 ; D3 g; U5 ¼ fE1 ; E2 g; U6 ¼ fF1 ; F2 ; F3 ; F4 g; U7 ¼ fG1 ; G2 g According to Table 4, the actual membership matrix is formed as follows, taking “convenience” as an example: 2

RQ1

Actual

0:0893 ¼ 4 0:0670 0:2188

0:1518 0:1830 0:3705

0:2678 0:4152 0:2589

0:25 0:0:2589 0:1205

3 0:2411 0:0759 5 0:0313

Next, the composite calculation of the fuzzy matrix is carried out to obtain the comprehensive value of “convenience” evaluation. ; SQ1Actual

¼ KQ1  RQ1Actual ¼ ð0:7352， 0:1994， 0:0654Þ 2 0:0893 0:1518 0:2678 0:25  4 0:0670 0:1830 0:4152 0:0:2589 0:2188 0:3705 0:2589 0:1205 ¼ ð0:0933; 0:1732; 0:2966; 0:1944Þ

3 0:2411 0:0759 5 0:0313

The actual first-level fuzzy evaluation vector obtained by normalization is: SQ1Actual ¼ ð0:0933;

0:1732;

0:2966;

0:1944Þ

The actual first-level fuzzy evaluation vectors of other dimensions can be obtained similarly. The fuzzy matrix SActual for constructing the first-level index of bus service quality is actually: 3 2 SQ1Actual 0:0933 6 SQ2Actual 7 6 0:0170 6 7 6 6 SQ3Actual 7 6 0:0266 6 7 6 7 6 ¼6 6 SQ4Actual 7 ¼ 6 0:0530 6 SQ5Actual 7 6 0:0244 6 7 6 4 SQ6Actual 5 4 0:0524 SQ7Actual 0:0685 2

SActual

0:1723 0:0913 0:2109 0:4519 0:2546 0:3776 0:3214

0:2966 0:4255 0:4972 0:3997 0:5205 0:4328 0:4465

0:2433 0:3494 0:2183 0:0815 0:1709 0:1258 0:1488

3 0:1944 0:1168 7 7 0:0471 7 7 0:0140 7 7 0:0295 7 7 0:0113 5 0:0149

ZActual ¼ K  SActual ¼ f0:0670，0:2852，0:3861，0:1777，0:0840g It follows that 6.70% of the respondents are satisfied with the current bus service quality, 28.52% of the respondents are satisfied with the current bus service quality, 38.61% of the respondents think the current bus service quality is general, 17.77% of the respondents are not satisfied with the current bus service quality. Moreover, 8.40 percent of the respondents wanted more than the current quality of bus service.

26

According to Table 4 and the calculation formula, the overall score of actual public transport service quality is 0 1 5 B4C B C C PS ¼ ZActual  N ¼ f0:0670，02852，0:3861，0:1777，0:0840g  B B 3 C ¼ 3:0735 @2A 1 4.3.2

According to the above calculation process, the expected comprehensive evaluation results can also be calculated, and the results are sorted out in Table 4

Table 4.

Comprehensive scores of each dimension of bus service quality. Comprehensive score

Dimension

PS;i

ES;i

SQ;i

Convenience Q1 Comfort Q2 Reliability Q3 Guarantee Q4 Reactivity Q5 Tangibles Q6 Empathy Q7

2.7265 2.5423 2.9519 3.4487 3.0737 3.3337 3.2801

4.1479 4.1577 4.3709 4.4843 4.3664 4.4272 4.4582

1.4214 1.6154 1.4190 1.0456 1.2927 1.0935 1.1781

According to the above table and the weight value of each dimension in Table 4, the overall perceived value of bus service quality can be obtained as follows: SQ’ ¼ ai

7 X

SQ’i

i¼1

¼ 0:3528  ð1:4214Þ þ 0:0385  ð1:5154Þ þ 0:06  ð1:4190Þ þ 0:2247  ð1:0456Þ þ 0:0245  ð1:2927Þ þ 0:0747  ð1:0935Þ þ 0:2247  ð1:1781Þ ¼ 1:2579

(5)

According to the overall perceived value SQ’ ¼ 1:2579 < 0, it can be seen that the current bus service quality generally cannot meet the expectations of passengers, that is, bus passengers are not satisfied with the current bus service. In addition, the actual overall score of bus service quality was 3.0735, which was consistent with the calculated overall perceived value S.Q. of bus service quality.

5 CONCLUSION Based on the SERVQUAL gap model, this paper constructs a weighted evaluation index system of bus service quality with 7 dimensions and 20 measurement indexes. The fuzzy comprehensive evaluation model is used to collect data by questionnaire, and the evaluation results of bus service quality are obtained. The results show the following points: The dimension with the largest difference in expectation of public transport service quality is comfort (1.6154), and the actual score of “comfort” (2.5952) is the lowest, which 27

indicates that the current public transport service is lacking in comfort. Passengers’ satisfaction in this aspect could be much higher. The dimension with a prominent gap is convenience (1.5721), and the convenience score (2.7414) is also low. It reflects the current bus service still needs better public transportation convenience. The main is necessary for the passengers to walk to wait for a long time. This dimension was weighted (0.3528). The largest impact on passenger perception of bus service quality is relatively obvious, so the bus service improvement in meeting the demand for bus passenger convenience. The dimension with the smallest expectation actual gap is the assurance (0.9659), and the actual score of assurance is on the high side, which indicates that the public transport service has done a good job in assurance. It still needs to meet the needs of passengers. The public transport service can provide basic services and ensure the personal safety of passengers, but it does not bring enough “sense of security” to passengers. Public transport service providers and managers should pay more attention to passengers’ travel experience in terms of safety, bring passengers a sense of “safety,” and make public transport more attractive. The measurement model provides valuable diagnostic information for managers. Through questionnaire survey and model measurement, the main factors to be improved are of great significance for improving the quality of urban bus service.

REFERENCES Huang H.B. (2014) Research on Evaluating Bus Passenger Satisfaction in Changsha Based on AHP Fuzzy Comprehensive Evaluation Method. D. Central South University of Forestry and Technology Li L.B., Lv Y.Z., Ji K. and Wu B. (2017) Evaluation of Public Transport Service Quality Based on Gap Model. J. Traffic Engineering, 17 (01): 22–26 Liu Y., Yu M. and Liu Y. (2019) Evaluation Model of Bus Line Service Quality Based on Passenger Perception. Journal of Northeast University (Natural Science Edition).,4005: 750–755 Peng C.S., Zhou X.M., Zhang D.Z., Lin N.N. and Song X.H. (2013) Analysis of Factors Affecting Public Transport Service Quality Based on Passenger Perception. J. Traffic Information and Safety, 43 (1): 40–44 Wu H.R., Cui S.H. and Zhang H.S. (2012) Research on Evaluating Urban Public Transport Service Quality Based on Passenger Perception. Journal of Chongqing Jiaotong University (Natural Science Edition), 31 (05): 1027–1030

28

Advances in Traffic Transportation and Civil Architecture – Liu, Qi & Law (eds) © 2023 copyright the Author(s), ISBN 978-1-032-51438-3

Research on the optimization of high-speed rail express operation process—taking Nanning East Station as an example Xiaxi Li* School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China

ABSTRACT: In 2021, China’s express delivery business volume will reach 108.3 billion pieces, a year-on-year increase of 29.9%. In 2021, the number of parcels in China will account for more than half of the world’s total, and the market scale has ranked first in the world for seven consecutive years. In 2021, the total mileage of high-speed railways in China will exceed 40,000 kilometers, and the coverage of high-speed railway networks will be wider. The rapid growth of express business volume and the wider high-speed railway network have provided good opportunities for developing high-speed railway express. To realize the high-quality development of high-speed rail, express, optimizing the operation process of high-speed rail express is an important aspect to improve the operation efficiency. In this paper, taking Nanning East Station as an example, combined with the data collection, the high-speed rail express operation flow chart is drawn, and the high-speed rail express operation process is simulated with Flexsim to find out the problematic links in the existing operation process. Finally, suggestions for optimizing the high-speed rail express operation process are put forward.

1 INTRODUCTION With the deepening of reform and opening up, China’s economic development method and economic structure have also changed. In recent years, the rapid development of the ecommerce industry has driven the rapid development of China’s express delivery industry. In 2021, China’s express delivery business volume will reach 108.3 billion pieces. From a policy perspective, the “14th Five-Year” Postal Industry Development Plan jointly issued by the State Post Bureau, the National Development and Reform Commission, and the Ministry of Transport in 2021 points out the need to develop air express and high-speed rail express. Improve the service capabilities of express delivery, express service stations, and warehouse-delivery integration. The “14th Five-Year Plan for the Development of Express Industry” issued by the State Post Bureau pointed out that the development of high-speed rail express should be encouraged, and the construction of supporting facilities, process connection, and information sharing should be promoted. From an industry perspective, with the continuous development of e-commerce such as Taobao, JD.com, and Tmall, online shopping has become the mainstream of the times. Therefore, relevant experts predict that China’s express delivery industry will maintain rapid growth in the next 10 years, and the development of the express delivery industry will be welcome. Here comes the opportunity of the ages.

*

Corresponding Author: [email protected]

DOI: 10.1201/9781003402220-4

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From the railway level, by the end of 2021, China’s railway operating mileage has reached 150,000 kilometers, including 40,000 kilometers of high-speed rail. China’s railways have covered 81% of the country’s counties, and high-speed rail has reached 93% of cities with a population of more than 500,000. A railway network with a reasonable layout, extensive coverage, distinct layers, safety, and efficiency has been formed.

2 LITERATURE REVIEW In terms of the operating organization process of high-speed rail express and freight, Wang Minghui & Ling Feixiang (2017) pointed out the product positioning of high-speed rail freight. They put forward specific suggestions for integrating modern logistics concepts into each high-speed rail freight operation process. Qin Zhipeng & Qin Baolai (2019) combined the existing problems in developing high-speed rail express in China. They learned from the development experience of high-speed rail express in foreign countries. In terms of railway freight process optimization, Di Junhui (2015) used Petri net to establish an e-commerce railway station freight operation process, found out the existing problems, and proposed corresponding optimization measures for these problems. Chen Jiayi et al. (2018) used Petri net to discover the existing problems in the operation process of white goods sending end, and combined with the results of customer demand analysis, put forward a reasonable optimization plan; finally, the author used Petri net, and Flexsim simulations demonstrate the remarkable effect of the optimization. Yang Kaili (2019) established a stochastic Petri net model after analyzing the problems existing in the operation process of China-Europe trains and using Flexsim for simulation. During the simulation, the accumulation points of the operation process of the China-Europe freight train station were found, and corresponding optimization suggestions were put forward. There are few studies on the high-speed rail express operation process, and no scholars have used the simulation method to find the problems existing in the high-speed rail express operation process. This paper will draw on the experience of railway freight process optimization, use flexsim simulation to find the problems existing in the high-speed rail express operation process, and then suggest suggestions for improvement. 3 ANALYSIS OF THE OPERATION PROCESS OF HIGH-SPEED RAIL EXPRESS AT NANNING EAST STATION 3.1

Introduction to Nanning East Railway Station

Nanning East Railway Station is located in Nanning, Guangxi Zhuang Autonomous Region, China. It is a passenger station under the jurisdiction of China Railway Nanning Bureau Group Co., Ltd., and the largest comprehensive railway transportation hub in Southwest China (as of October 2016). It is a super-large comprehensive transportation hub station integrating subway, bus, and long-distance passenger transportation (Pang 2016). 3.2

The operation process of high-speed rail express at Nanning East Station

Based on referring to many documents related to high-speed rail express, combined with the relevant materials of high-speed rail express in Nanning East Station, this paper sorts out the functions of each link of the operation process of Nanning high-speed rail express and draws its operation flow chart, which lays the foundation for the subsequent Flexsim simulation.

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The high-speed rail express operation process is as follows: 3.2.1 Customers fill in orders At present, the customers of Nanning East Railway Station fill in orders mainly into two types: business hall filling and online filling, but no corresponding APP has been developed, so currently, about two-thirds of customers fill in orders in the business hall. 3.2.2 Express arrival Express arrival is mainly divided into customer self-delivery and door-to-door delivery in business halls. Customers themselves deliver about two-thirds of the express. Such customers bring the expresses to the business hall before filling in the order, and most of these expresses have the characteristics of high added value and require high timeliness. The remaining onethird of the expresses are for regular customers, picked up by the business hall. 3.2.3 Inspection and weighing After the expresses arrive at the business hall, the staff will transport the expresses to the inspection area for inspection. The express that does not meet the transportation requirements will be returned to the customer. After the inspection is qualified, they will be weighed and combined with the type of express product required by the customer, the fee that customers need to pay will be determined and then shipped to the distribution center. 3.2.4 Sorting and packaging According to the variety and transportation order of expresses, they should be sorted, stacked, and packaged. Afterward, staff put the packaged expresses into standard cargo boxes destined for different directions and go through security inspection again. 3.2.5 Loading operation To avoid crossing the passenger flow, 20 minutes before the start of the high-speed EMU, staff will transport the express cargo boxes to be loaded onto the platform through a small trailer. After the EMU arrives at the platform, staff load and stow the express cargo boxes. 3.2.6 Transportation to the destination After the EMU arrives at the destination station, staffs organize the loading and unloading vehicles, transports the express cargo boxes to the station distribution center warehouse through small trailers, and implements distribution or waits for customers to pick up the express according to the actual situation. Flow chart of high-speed rail express operation at Nanning East Station, as shown in Figure 1.

4 SIMULATION OF HIGH-SPEED RAIL EXPRESS OPERATION PROCESS AT NANNING EAST STATION Based on the high-speed rail express operation process of Nanning East Station, use Flexsim to simulate it and analyze the problems existing in the current process by discovering the accumulation points in the simulation process. In the high-speed rail express operation process, there are two operation modes in the same operation link, and different

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Figure 1.

Flow chart of high-speed rail express operation at Nanning East Station.

operation modes consume different times. Therefore, based on the data of Nanning East Station, this paper sets the output ratio of different operation mode libraries, as shown in Table 1 below. Table 1.

The output ratio of different operation mode libraries.

Category

Output content

Proportion

Output content

Proportion

1 2 3

Fill in the business hall Customer express arrives Pass the security check

67% 67% 99%

Fill in the online platform Car Pickup Security check failed

33% 33% 1%

According to the data collected from Nanning East Railway Station, the time is set for each link of the high-speed rail express operation process. The link time mainly obeys a uniform distribution or a normal distribution. Among them, the number of orders per day in Nanning East Station is about 100, and the daily working time is 8 hours.

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Table 2.

Job process transition time.

Transition

Meaning

Consumption time/hour

T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 T12 T13 T14 T15 T16 T18 T19

fill in the business hall review and enter orders fill in the online platform review customer express arrives car pickup inspection weigh pay the fee sorting package labeling divide the direction to the small trailer security check carry expresses to the platform EMU assembly car EMU departure ship to warehouse

evenly distributed (0.15, 0.4) evenly distributed (0.1, 0.2) evenly distributed (0.1, 0.2) evenly distributed (0.05, 0.1) normal distribution (0, 0) normal distribution (1, 0.1) normal distribution (0.1, 0.02) normal distribution (0.2, 0.03) evenly distributed (0.15, 0.3) normal distribution (0.3, 0.05) evenly distributed (0.2, 0.5) normal distribution (0.05, 0.01) normal distribution (0.1, 0.02) evenly distributed (0.1, 0.2) evenly distributed (0.2, 0.3) evenly distributed (0.04, 0.1) evenly distributed (0.04, 0.1) normal distribution (0.2, 0.05)

Therefore, it is assumed that the generator (number of orders) production follows a normal distribution, with a mean value of 0.08 and a standard deviation of 0.01; The customer first brings the express to Nanning East Station and then fills in the information, so the delivery time is 0. Table 2 shows the transition schedule of the high-speed rail express operation process. Input the time distribution of each link in the above table into each simulation link, reset and run. Pay close attention to whether there is any operating failure during the operation and modify it in time if there is a failure. After Flexsim runs for 240 hours (8 hours * 30 days), the 3D diagram at the end of the model simulation is shown in Figure 2.

Figure 2.

Simulation running diagram of high-speed rail express operation process.

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The states of each transition’s input and output entities after the simulation is completed are shown in Table 3 below. Table 3.

Simulation results of high-speed rail express operation process.

Transition Meaning

Number of input flow items

Number of output flow items

Number of flow items at the end time

Number of flow items accumulated in the corresponding storage area

T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 T12 T13

1590 1065 953 953 587 1430 2013 2012 2012 2010 2010 2007 2006

1589 1064 953 953 586 1427 2012 2012 2010 2010 2007 2006 2006

1 1 0 0 1 3 1 0 2 0 3 1 0

457 524 0 0 0 0 0 0 0 0 0 0 0

2006 2006

2006 2006

0 0

0 0

2006 2006 2006

2006 2006 2006

0 0 0

0 0 0

T14 T15 T16 T18 T19

fill in the business hall review and enter orders fill in the online platform review customer express arrives car pickup inspection weigh pay the fee sorting package labeling divide the direction to a small trailer security check carry expresses to the platform EMU assembly car EMU departure ship to warehouse

According to the simulation results in Table 3 above, it can be seen that the model has been run for a total of 2006 times. In Table 3, “Number of Items Accumulated in Corresponding Storage Area” represents the number of orders waiting to be processed by the corresponding transition. Among them, the number of accumulated temporary entities in the corresponding storage areas of T1 (fill in the business hall) and T2 (review and enter orders) are 457 and 524, respectively, indicating a serious accumulation in this link. However, there is a small amount of accumulation in T5 (customer express arrives), T6 (car pickup, T7 (inspection), T9 (pay the fee), T11 (package), and T12 (labeling). The specific reasons are as follows: l

l

T1 (fill in the business hall) and T2 (review and enter orders): Compared with the online order filling of the express company, the current level of information of many high-speed rail express business halls could be higher. In the process, the business hall fills to fill in the paper order on site, which takes a long time. The filled order needs to be reviewed by the staff, and the order information is entered. In the relevant information, it is found that Nanning East stands in this regard particularly obvious. Except for 30% of fixed customers who can send filled orders to the staff through WeChat, most customers fill in paper orders on the spot. Therefore, this link seriously hinders the efficient operation of highspeed rail express. We will focus on optimizing this link in the subsequent process optimization process. T5 (customer express arrives), T6 (car pickup: 30% of the orders from regular customers, China Railway Express car will pick up express. The China Railway Express vehicles often pick up the parcels at 6-7 to catch up with the EMUs at 8-9 in the morning. At this time, it

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l

is rush hour, and urban roads are easily congested, hindering this link’s smooth progress. Similarly, many customers will encounter morning rush hour to deliver goods early. T7 (inspection), T9 (pay the fee), T11 (package), T12 (labeling): inspection is a preliminary check to see whether expresses are contraband, and this link is done manually. After investigation, it was found that the payment fee is that customers need to wait in the business hall for staff to weigh the express and then verbally inform customers to pay, so the link brings inconvenience to customers. Packaging and labeling are all done manually. When there are many expresses, it is easy to accumulate, making the high-speed rail express operation not smooth enough.

5 SUGGESTIONS FOR OPTIMIZING THE OPERATION PROCESS OF HIGHSPEED RAIL EXPRESS AT NANNING EAST STATION Based on the above Flexsim simulation analysis of the high-speed rail express operation process of Nanning East Station, the optimization suggestions for the high-speed rail express operation process of Nanning East Station are put forward as follows: 5.1

Strengthen informatization construction

develop the corresponding high-speed rail express APP, gradually cancel the paper order filling in the business hall, and promote the APP to fill in the order, which improves the efficiency of customer filling and reduces the process of staff entering paper orders into the computer. And improve the work efficiency of the review and feedback link. In terms of payment, online payment can also be used. Staff will initiate a payment request to the corresponding customers after weighing, and customers must complete the order payment within a certain period. This way, customers can leave the business hall after the express has been delivered without waiting to complete the weighing. 5.2

Reasonable planning of the delivery time

The sales department can consider arranging the door-to-door pickup and delivery of express to customers at 8–10 pm the day before the transportation to avoid the time of traffic congestion and improve the delivery efficiency of China Railway Express. Reduce customer’s delivery time for expresses. 5.3

Cancel inspections and strengthen security inspections

Unpacking inspections will reduce efficiency. Establishing a flexible inspection system and strengthening security inspections is more feasible. Strengthening security inspections mainly starts from two aspects. One is to clarify the purpose and content of security inspections. Through certain training, staff can be familiar with the inspection content, clarify what to be inspected, what purpose to achieve through inspection, and what kind of effect will be produced. The second is that staff should find out the problems of the security inspection in time in their daily work and improve the security inspection effect. 5.4

Realize the mechanization of some links

manual packaging and labeling are inefficient and increase labor costs by purchasing some corresponding equipment, such as packaging equipment and automatic labeling machines. Packaging equipment is divided into four types according to functions: sealing machinery,

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wrapping machinery, packing machinery, and strapping machinery. The express of highspeed rail express has the characteristics of small batches, so it is a good choice to use wrapping machinery and packing machinery.

REFERENCES Jiayi C., Ling X., Siwen L. Railway Freight Business Process Reengineering based on Customer Demand Analysis and Simulation Verification [J]. China Railway, 2018(09):34–43. Junhui D. Research on the Freight Operation Process of Railway Stations based on E-Commerce [D]. Lanzhou Jiaotong University, 2015. Kaili Y. Research on Operation Process Optimization of China-Europe Train Station [D]. Beijing Jiaotong University, 2019. Minghui W., Feixiang L. Research on High-speed Railway Freight Operation Process based on Modern Logistics [J]. Journal of Transportation Engineering and Information, 2017, 15(01): 9–15. Pang Lina China. (2016). Nanning East Railway Station - the Largest Comprehensive Railway Transportation Hub in Southwest China. https://baike.baidu.com/item/%E5%8D%97%E5%AE%81%E4% B8%9C%E7%AB%99/5262669?fr=aladdin Zhipeng Q., Baolai Q. Research on the Logistics Operation Organization of High-speed Rail Express [J]. Logistics Engineering and Management, 2019, 41(12): 1–4.

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Advances in Traffic Transportation and Civil Architecture – Liu, Qi & Law (eds) © 2023 copyright the Author(s), ISBN 978-1-032-51438-3

Study on the environmental benefits of developing Sea-River through transportation in the Shandong Bohai Bay region Shuming Liu & Yuhua Zhu* School of Transportation, Shanghai Maritime University, Shanghai, China

ABSTRACT: How to reduce the environmental pollution generated by the transportation process to achieve low-carbon transportation is an important issue in the development of China’s transportation industry. This paper proposes three transport corridors in the Bohai Bay region that can be used for Sea-River through transportation and analyzes the environmental benefits of each route using carbon emission models for automobiles and ships. It is concluded that the development of the Sea-River through transportation routes in the Bohai Bay area of Shandong Province can reduce CO2 emissions by 76.65% per 1 container of cargo compared to road transportation. The development of the Sea-River through transportation aligns with the development concept of green and environmental protection.

1 INTRODUCTION Waterway transport has a significant capacity, low energy consumption, and much lower freight cost than road transport and railroad transport. The development of the Sea-River through transportation can effectively reduce transport costs and environmental pollution. With the development of the shipping industry, domestic research on the Sea-River through transportation is gradually enriched, involving Sea-River through transportation development planning, Inland-River transportation network, and Sea-River through transportation line planning, etc. However, the research mainly focuses on inland river ports in the southern Yangtze River Delta region, and there needs to be more research on the Sea-River through transportation in Shandong Province. The inland ports and seaports in Shandong Province have been in a state independent of each other. Inland cargoes are mostly transported by road and rail, which has a long transportation cycle, large transportation costs, and will cause pollution to the environment. The development of ports in the Bohai Bay area is significantly weaker than other seaports. Still, it has the advantage of a natural river that can be connected to rich inland shipping resources. The development of sea-river intermodal transport requires connecting the seaports in the Bohai Bay region with high-quality river ports in the province and exploring green development paths. Therefore, this paper addresses the following issues: 1The possibility of developing Sea-River through transportation in the Bohai Bay region of Shandong Province and the layout of Sea-River through transportation; 2The measurement of the degree of impact on the environmental benefits of developing Sea-River through transportation in the region; 3The policy countermeasures for developing Sea-River through transportation in the region.

*

Corresponding Author: [email protected]

DOI: 10.1201/9781003402220-5

37

2 BASIC CONDITIONS FOR THE DEVELOPMENT OF SEA-RIVER THROUGH TRANSPORTATION IN THE BOHAI BAY REGION 2.1

Development status

Shandong Bohai Bay seaports include Binzhou Port, Dongying Port, and Weifang Port. Bohai Bay Port is a first-class port under the Shandong Province Port Group. Shandong Province is being operated inland river ports mainly located in the Beijing-Hangzhou Canal and its main tributaries along the waterways of Jining, Zaozhuang, Heze, and other cities. The planned inland ports along the Xiaoqing River waterway under construction are located in Jinan, Binzhou, Zibo, and Dongying. An exception to the under-construction Xiaoqing River route, Shandong Province still needs to develop other Sea-River through transportation routes. As can be seen from Table 1, the development of Binzhou, Dongying, and Weifang ports in the Bohai Bay region is significantly weaker than Qingdao, Rizhao, and Yantai ports. The inland river port of Jining Port and Zaozhuang Port are robust, and Jining Port is even stronger than the two seaports of Weihai Port and Binzhou Port. Although the current strength of the Bohai Bay Port could be better, the location and transportation are superior, and the economic hinterland is vast, with great potential for development and good prospects for development.

Table 1.

Port cargo and container throughput in Shandong Province.

Port Throughput Cargo

2022 2021 2020 Foreign 2022 trade 2021 cargo 2020 Container 2022 2021 2020

Seaport

Inland Port

Bin Dong Qingdao Rizhao Yantai Weihai zhou Ying

Zhao Weifang Jining Zhuang other

32791 63029 60459 23875 45881 44458 1247 2371 2201

2466 4515 5324 371 745 631 31 58 52

27912 54117 49615 16119 34568 31909 273 517 486

22954 42337 39935 7857 16484 14414 204 365 330

2090 4273 3863 585 1239 1185 65 134 122

2424 4007 3665 8 22 3 0 0 0

3127 5880 6022 334 567 648 0 0 0

2832 4600 4180 0 0 0 1 0 0

1062 1713 1234 0 0 0 0 0 0

155 272 311 0 0 0 0 0 0

Data source: Ministry of Transport of the People’s Republic of China, 2022 data as of June

2.2

Bohai Bay area Sea-River through transportation development opportunities

2.2.1 Waterway advantage Shandong Province is abundant in river water resources, with the sea conditions of the waterway Xiaoqing River, the Tuhai River, the Yellow River, and the Beijing-Hangzhou Canal. Compared with other seaports, the Bohai Bay area ports have the advantage of natural inland waterways, which can be connected with the waterways in Shandong Province with the conditions of the Sea-River through transportation. In addition to the Xiaoqing River route being developed (via Weifang Port, Jinan Port, and Yangkou Port), the BeijingHangzhou Canal + Tuhai River route (via Binzhou Port, Dezhou Port, and Liaocheng Port) and the Beijing-Hangzhou Canal + Yellow River route (via Dongying Port, Taian Port, Heze Port, Jining Port, and Zaozhuang Port) can also be developed.

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2.2.2 Resource advantages 2.2.2.1 Advantages of inland river ports In addition to the channel advantage, Bohai Bay Port can also be connected to the province’s two major inland river ports-Jining Port and Zaozhuang Port. Jining Port is the largest inland river port in Shandong Province, which is a large-scale distribution center for shipping materials, with an annual throughput capacity of 5 million tons. It is responsible for the “transportation of coal from the north to the south, cargo from the south to the north, and container transportation.” Zaozhuang Port, an important port planned for the construction of the renewal project of the Beijing-Hangzhou Canal, has a designed annual throughput of 1.33 million tons and a distance term of 4 million tons. The two ports have advanced loading and unloading technology, a high degree of mechanization, and strong development momentum, which can help the Bohai Bay seaport to develop faster if combined with the seaport. 2.2.2.2 Hinterland city advantage The seaport hinterland cities of Binzhou, Dongying, and Weifang are well located with convenient transportation and rich marine and mineral resources. The inland cities along the sea route are Jinan, Dezhou, Liaocheng, Zaozhuang, Jining, and Taian, all of which are major mineral cities in Shandong, with abundant mineral resources and a sufficient supply of goods. Among them, Jining is the first mineral city in Shandong. 2.2.3 Policy support In recent years, the country has formulated and introduced relevant documents for the shipping industry, bringing important development opportunities for Sea-River through transportation. In 2019, the Central Committee of the Communist Party of China (CPC) and the State Council issued “the Outline for the Construction of a Strong Transportation Country.” The outline proposes that a strong transportation country with people’s satisfaction, strong protection, and world forefront will be fully built by the middle of this century. The outline requires the promotion of the orderly transfer of bulk cargo and mediumand long-distance cargo transportation to railroads and water transport. In September 2020, China put forward the goal of a “carbon peak” by 2030 and “carbon neutral” by 2060, which is called the “double carbon” goal. As an important industry in national economic development, the shipping industry shoulders the important mission of energy saving and carbon reduction, which should vigorously develop green shipping and accelerate the green and low-carbon transformation of the shipping industry. Shandong Province also listed the Sea River through transportation as an important planning project. Shandong released the “Shandong Province New Old Dynamic Energy Conversion Major Project Implementation Plan.” The plan proposes to grow and develop the marine service industry, actively develop ocean shipping, promote water-land intermodal transport, Sea-River through transportation, and build a regional aquatic products trading center and cold chain logistics center. At present, Shandong is vigorously promoting the resumption of navigation of the Xiaoqing River. According to the schedule plan, the Xiaoqing River resumption project will be completed in June 2023 to open to navigation. The project will become a major inland waterway through the central industrial corridor of Shandong Province, and it is also the core area of Jinan, straight to the sea to open up the channel.

3 BOHAI BAY AREA SEA-RIVER THROUGH TRANSPORTATION DEVELOPMENT LAYOUT 3.1

Sea-River through transportation channel

As shown in Figure 1 below, the following three Sea-River through transportation corridors can be planned in the Bohai Bay area. 39

Figure 1.

Bohai Bay area Sea-River through the transportation route map.

Xiaoqing River route: The port of departure is Jinan port, along the Xiaoqing River to Weifang Yangkou port into the sea. Beijing-Hangzhou Canal + Tuhai River route: The Beijing-Hangzhou Canal is divided into two sections from the Yellow River to the north and south, with the port of departure at the port of Dezhou, along the northern section of the Beijing-Hangzhou Canal to the port of Liaocheng. At the head of the Four rivers of Liaocheng into the Tuhai River to the port of Binzhou into the sea. Beijing-Hangzhou Canal + Yellow River route: The port of departure is Zaozhuang port, along the southern section of the Beijing-Hangzhou Canal, through the port of Jining, Taian port. Then into the Yellow River basin, inward link Heze port, outward to Dongying port into the sea. 3.2

Sea-River through transportation core area

According to the transport demand and existing port and navigation conditions, Jinan Port, Jining Port, and Zaozhuang Port will be developed as the core inland river ports, and other inland river ports will be developed under the linkage program led by the three core ports. The program uses Jinan Port, Jining Port, and Zaozhuang Port, three major river ports, to drive the development of Taian Port, Liaocheng Port, Dezhou Port, and Heze Port, four river ports. These inland ports and the Bohai Bay region seaport division of labor optimize the layout of the province’s main cargo categories, such as containers, coal, and mineral resources. Expand the existing advantages of inland ports while looking for new development opportunities for other inland ports. Jinan Port is an important port of the Xiaoqing River Re-navigational Project, which is being promoted, with a 6170m planned port shoreline, 30 productive berths, a total port capacity of 18 million tons, and a total land area of 2.46 million square meters. The development goal is to become a comprehensive, modern inland river port with basic functions such as loading and unloading, warehousing, transit, transportation organization, port development, modern logistics, production, and living. Zaozhuang Port and Jining Port are important inland ports of the Beijing-Hangzhou Canal waterway. Jining has a navigable mileage of about 1,100 km, accounting for about

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70% of Shandong’s inland waterway mileage. Its cargo throughput is equal to that of seaports. Jining Port and Zaozhuang Port will be the core area of the inland river transportation business, taking advantage of the development of its access to the river and the sea. The two ports are planned to become a regional shipping center integrating warehouse storage and loading, shipping agency, warehousing, and logistics, shipping logistics support, and other functions, which focus on the Shandong hinterland and radiating Jiangsu, Zhejiang, and Shanghai. 4 ANALYSIS OF ENVIRONMENTAL BENEFITS OF SEA-RIVER THROUGH TRANSPORTATION BASED ON CARBON EMISSION MODEL 4.1

Carbon emission models for automobiles and ships

The Bohai Bay region is rich in mineral resources in the Sea River through transportation layout area, with abundant metal ores, coal, crude oil, and other resources. The use of automobile transport mostly self-unloading semi-trailers. 4.1.1 Automotive carbon emissions model Intergovernmental Panel on Climate Change (IPCC) has proposed two options for calculating GHG emissions. Method 1: Rough estimate. GHG emissions calculation method based on fuel supply and consumption consultation: total emissions = energy consumption
emission factor. Method 2: Detailed algorithm for calculating. GHG emissions based on the supply and consumption of fuels and information on technology patterns for each emission source: total emissions = dynamic activity level
emission factor. IPCC recommends Method 1 to calculate the GHG emissions of ships since it is difficult to collect data related to Method 2. This paper adopts Method 1 to establish a carbon emission model for diesel engines to estimate CO2 emissions. The engines of semi-trailers are mostly diesel engines, and the fuels used in the main engines and ships’ generators are also mainly diesel. Whether light diesel or heavy diesel, the carbon content is fixed, and the amount of carbon dioxide produced is proportional to the fuel consumption. The following formula can calculate the carbon dioxide emissions of diesel engines. X CO2 emission ¼ ðnet calorific value of diesel fuel
CO2 emission factorÞ (1) CO2 emission factor ¼ carbon content of diesel fuel
oxidation rate
conversion factor

(2)

the net calorific value of diesel fuel – is 43 TJ/Gg the carbon content of diesel oil – 20.2 kg/GJ oxidation rate – 99% conversion factor – 44/12 It is known that semi-trailers mostly use No. 0 diesel, and the density of No. 0 diesel is 0.84g/cm3, combined with (1) (2) formula calculation: CO2 emissions per liter of No. 0 diesel = 43/1000
20.2
99%
44/12
1000
0.84 = 2.649727kg. 4.1.2 Ship carbon emission model IMO defines the ship CO2 emission index in document 471, adopted by the 53rd ECC. X  X  FC
C m
D = (3) Indexco2 ¼ carbon i cargo;i i i i

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Ship CO2 emissions also depend on fuel oil consumption. Still, a ship uses many kinds of fuel oil, which has many influencing factors and is complicated to calculate, so it is common to derive CO2 conversion rate according to the ship and fuel type. In this paper, we use the number of CO2 emissions from small cargo ships, which is 3186 kg/T, calculated by Japanese scholars. 4.2

Analysis of environmental benefits of Sea-River through transportation in the Bohai Bay region

A 36 TEU inland container ship is selected as an example for calculation. It is known that a 36 TEU container ship with a range of 140 km is equivalent to the capacity of 36 20-ton trucks driving 140 km. 1 TEU by ship requires 9 L of fuel, while 1 TEU by truck requires 30 L of fuel (Lin 2014). 4.2.1 Xiaoqing river route Xiaoqing River originated in Jinan city, injected into Bohai Bay in the east of Yangkou, across Jinan, Binzhou, Zibo, Dongying, and Weifang 5 areas, which has been planned to resume navigation 169.2 km in length—planning for a three-stage channel. The road transportation distance is about 249 km. Therefore, the CO2 emissions for transporting 1 TEU of cargo from Jinan to Weifang port by road for 249 km are approx. ð2:649727
30Þ=140
249 ¼ 141:382kg

(4)

The CO2 emissions for the inland transportation of 1 TEU of cargo along the Xiaoqing River waterway to Weifang port for a 169.2 km voyage are approx. ð3:186
0:84
9Þ=200
169:2 ¼ 20:377kg

(5)

According to the calculation, the inland river transportation of 1 TEU cargo by the Xiaoqing River route can reduce CO2 emission by 121.005kg compared with road transportation. The carbon reduction rate is 85.59%. Four ports, eight port areas, and eleven operation areas are planned to be built along the Xiaoqing River Re-navigational Project, with an estimated comprehensive annual throughput capacity of 79.6 million tons. After the Xiaoqing River resumes navigation, the annual CO2 emission reduction from just one Sea-River through the transportation route of the Xiaoqing River is about 48.15999 million tons, which is equivalent to the CO2 emission absorbed by 96.32 million trees in a year. 4.2.2 Beijing-hangzhou canal + tuhai river route / yellow river route The length of the Shandong section of the Beijing-Hangzhou Canal is 643 kilometers, connecting Dezhou, Liaocheng, Jining, and Zaozhuang. The road transportation distance of the four cities is about 512 kilometers. The length of the Tuhai River is 436 kilometers, connecting Liaocheng and Binzhou, and the road transportation distance between the two cities is about 387 kilometers. The Yellow River Shandong section is 628 kilometers, connecting Heze, Taian, Jinan, Binzhou, Dongying, and five cities road transport distance is about 556 kilometers. The total length of inland river transportation of the Beijing-Hangzhou Canal + Tuhai River route and the Beijing-Hangzhou Canal + Yellow River route is 1,707 km, and the total length of road transportation is 1,455 km. The CO2 emissions for 1TEU of cargo transported by road for 1,455 km are approx. ð2:649727
30Þ=140
1455 ¼ 826:149kg

42

(6)

The CO2 emissions for 1 TEU of cargo transported by inland waterways over a distance of 1707 km are approx. ð3:186
0:84
9Þ=200
1707 ¼ 205:574kg

(7)

According to the calculation, the inland transportation of 1 TEU of cargo by the BeijingHangzhou Canal + Tuhai River route and the Beijing-Hangzhou Canal + Yellow River route can reduce CO2 emissions by 620.575 kg compared with road transportation. The carbon reduction rate is 75.12%. In summary, the inland river transportation of 1 TEU of cargo on the three Sea-River through transportation routes can reduce CO2 emissions by 741.58 kg compared to road transportation. The carbon reduction rate of the three routes can reach 76.65%.

5 SUGGESTIONS ON THE GREEN DEVELOPMENT PATH OF SEA-RIVER THROUGH TRANSPORTATION IN THE BOHAI BAY REGION 5.1

The government provides policy and financial support

Policy support can play a guiding role in the process of green development of the Sea-River through transportation. The government can formulate preferential support policies to encourage the development of the Sea-River through transportation. The government supports inland waterway shipping enterprises with financial policies, reduces taxes and fees, eases the difficulty of loans, and gives financing support. The government reduces channel usage fees and lock passage fees and simplifies the customs clearance process for sea and river vessels, which can divert road and rail transport to inland waterway shipping. The construction of the Sea-River through transportation requires a large amount of capital, which the government can bear for the construction and maintenance costs of inland waterways, port terminals, and other infrastructure. The government provides funds for scientific research and technology and establishes specialized agencies to research and develop innovative technologies for Sea-River through transportation. The agency will address technical bottlenecks such as green waterways, green ports, green ships, and information platform construction for Sea-River through transportation. The government should improve support for scientific and technological innovation, encourage enterprises to carry out independent research and development, assist them in landing research and development results and provide financial incentives. The government should improve the talent introduction incentive system to attract high-quality talents to settle in the city, etc.

5.2

Establish a specialized agency for coordinated planning

Through transportation, the Bohai Bay region Sea-River involves a wide area, large construction scale, and long cycle. The region needs a dedicated agency to coordinate planning, construction, and management to implement an integrated development strategy for the harbor, river, and port. The agency unifies the planning of the Bohai Bay Sea River through the transportation collection and distribution network, port layout, and channel layout. The agency coordinates the relationship between the ports and improves the mechanism to guarantee the industry of Sea-River through transportation. And it is responsible for the Sea River through transportation routes of investment and publicity work. The agency should also use special measures to incentivize enterprises with the ability to organize freight across modes of transport to take the lead in developing the combined Sea River through the transportation market.

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5.3

Improve the layout of the port

The construction process reasonably sets the layout of the terminal, the construction of sea and river intermodal transport special berths, simplify the operation process of ships in port so that public and railway transport and waterway transport are seamlessly connected to improve transport efficiency. The project strengthens the use of the land area and coordinates the construction of integrated logistics parks and industrial parks behind the port. The government guides the layout of manufacturing and energy bases along the river to promote the development of port-side industrial clusters and inject new vitality into enhancing the strength of inland river ports and seaports. 5.4

Establishing an integrated information platform

Establishing an integrated information platform for Sea-River through transportation can realize the sharing of real-time status information of terminals, ports, waterways, ships, and cargo. Using the same platform by all regions, industries, and departments can improve port responsiveness and service levels. The Sea River can simplify the transaction process and facilitate enterprises through a transportation service platform that integrates cargo handling, storage and transportation, trade, and logistics, to provide customized full-process “end-to-end” transport services for customers. Road-Water through transportation and Rail-Water through transportation information resource sharing platforms can better combine road and rail transportation with waterway transportation, opening up the last kilometer of rail-water through inland ports. So that inland goods do not have to be transported to the seaport by road and rail and improve the port’s multimodal transportation collection and distribution system. 5.5

Building a green, low-carbon port

At the early stage of inland port construction, we pay attention to the ecological protection and restoration of the land waters during construction. The subsequent operation process of the port should pay more attention to reducing pollution emissions, clean and low-carbon operations, on time for port maintenance, and environmental protection facilities upgrade. In the process of inland waterway navigation to strengthen the application of clean energy, the port area promotes the use of solar energy and wind energy. The use of electric boats will be promoted on short-distance inland waterway routes of the Sea-River through transportation. The port undergoes shore power retrofitting, constructs low-emission terminals, and uses electricity to support the operation of the port and terminal equipment.

6 CONCLUSIONS This paper analyzes the geographic resources eligible for Sea-River through transportation in the Shandong Province Bohai Bay area. Propose three Sea-River through transportation channels that can be developed: the Xiaoqing River route, the Beijing-Hangzhou Canal + Tuhai River route, and the Beijing-Hangzhou Canal + Yellow River route. And this paper uses the carbon emission models of automobiles and ships to analyze the environmental benefits of the Bohai Bay region. It is pointed out that the development of the Sea River through transportation in the region can reduce CO2 emissions by 76.65% per 1 container of cargo compared to road transportation, which is in line with the low carbon policy of China’s transportation industry. The construction process of the Sea River through transportation needs to be combined with the concept of green and low-carbon shipping and use the advantages of the inland river to accelerate the development of the Bohai Bay seaports, which is a win-win initiative for the inland river ports and hinterland cities. In the future, the

44

development of sea-river through transport in Shandong Province can be studied considering the comprehensive factors such as time, cost, and environmental impact of Sea-River through transportation.

REFERENCES Bu D. and Ke E., “Suggestions on Green and Low-carbon Development of Inland Waterway Shipping,” J. China Water Transport, vol. 3, pp. 16–17, 2022. Chen P., “Zhejiang Inland River Container Sea-River through Transportation Research,” J. China Ports, vol. 3, pp. 49–52, 2022. Cheng Y. and Dong H.J. “A Study on River-Sea through Transportation of the Lower Yangtze River and Its Future Development of Water Transportation,” J. Remote Sensing, Environment and Transportation Engineering (RSETE), 2012 2nd International Conference on, 2012. Jianchun H. “Research on Countermeasures for the Development of s Sea-River through Transportation in Jiaxing Under the Background of the Four Major Constructions in Zhejiang Province,” J. China Water Transport, vol. 6, pp. 62–64, 2019. Liu H, Chengyi W and Xinbo H “Analysis of Coal Sea-River through Transportation at Jiaxing Port for Constructing Low-carbon Economic Development,” J. China Maritime, vol. 9, pp. 51–53, 2010. Sun Y., Zhang Z., Li G. and Ma J., “Research on the Planning and Design of the Sea-River through Transportation Shipping Routes in Shandong Province,” J. Journal of Qingdao Ocean Shipping Crew Vocational College, vol. 40, pp. 47–50, January 2019. Sun G. “Carbon Emission Optimization Scheme for Inland Waterway Vessels,” J. China Water Transport, vol. 11, pp. 56–57, 2014. Tu S. “Analysis and Inspiration of the Current Situation of the Sea – River through Transportation Development in Europe and America,” J. Port Technology, vol. 1, pp. 50–52, 2019. Wu J, Liu W, Li Y and Wu J, “The Development Path and Suggestions of the Sea-River through Transportation in Quzhou Port,” J. Water Transport Management, vol.42Z1, pp. 7–10+38, 2020. Xue L, “How to Extend the Green Corridor to the “Inside,” J. China Ship Inspection, vol. 7, pp. 19–22, 2022. Yang Q., Yan J., Zhang M and Liu Y, “Inspiration of Green Development of German Inland Waterway Shipping to China’s Shipping Development,” J. Water Transport Engineering, vol. 43, pp. 106–110+191, July 2021. Zhang J, Zhang D and Li G “Exploration on the Path of Green Development of Inland Waterway Navigation–Take the Xiaoqing River as an Example,” J. China Water Transport, vol. 7, pp. 130–132, 2021. Zheng L., “Research on the Development of Direct Sea – River through Transportation in Ningbo-Zhoushan Port,” D. Zhejiang Ocean College, 2014.

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Advances in Traffic Transportation and Civil Architecture – Liu, Qi & Law (eds) © 2023 copyright the Author(s), ISBN 978-1-032-51438-3

Research on the influence of exit obstacles on crowd evacuation efficiency based on Pathfinder Lu Wang, Lihong Yue*, Menghua Yang & Haijing Zhang School of Mechanical & Automotive Engineering, Qingdao University of Technology, Qingdao, China

ABSTRACT: Safety evacuation is an important factor that needs to be considered in building safety design. The setting of evacuation outlet obstacles greatly impacts the evacuation of people. In order to more accurately understand the influence of obstacles on the evacuation efficiency of evacuation exits, this paper considers the influence of the shape and size of obstacles on evacuation efficiency. Firstly, the Pathfinder software is used to simulate the obstacle at 1.0 m from the dispersion outlet. The influence of obstacle shape and size on evacuation efficiency under different conditions is simulated by constructing different evacuation scenarios. It is concluded that when the evacuation number is the same, the evacuation time changes with the change in the size and shape of the obstacle; when the number of evacuations is different, the evacuation time increases with the number of evacuations under the same obstacle condition. The results show that the shape and size of obstacles have a significant indigenous effect on evacuation efficiency when the number of people is different. The motion curve and extreme value of evacuation results provide a reference for the design of building exits and further study of evacuation.

1 INTRODUCTION The level of urbanization is getting higher, resulting in a linear increase in population density in buildings, and the evacuation pressure of evacuation channels in buildings is increasing. Once an accident occurs, it is easy to cause secondary disasters such as congestion and stampede due to untimely evacuation. This kind of accident has the characteristics of sudden. The accident precursor needs to be more obvious. It takes time to start the emergency plan. From 1900 to 2019, there were 267 crowd stampede accidents, with 43112 casualties (Zhang et al. 2019). Therefore, it is of great significance to study how to improve the ability of crowd evacuation for public safety. Zuriguel found that placing obstacles in a suitable position at the silo outlet can effectively increase the flow rate of particles (Zuriguel et al. 2011). Alonso-Marroquin studied the influence of obstacles in the funnel on the particle flow rate and found that a reasonable set of obstacles can increase the flow rate by 5% (Alonso-Marroquin et al. 2012). There are many similarities between gravity-driven particle flows and self-propelled pedestrian flows. Therefore, based on this study, researchers have launched a study of evacuation. Liu Tianyang used mice to do a cluster evacuation experiment, and the results showed that the exit’s form impacted evacuation time (Liu 2017). Helbing simulated with a social force model and found that setting obstacles in special locations facilitated evacuation in normal and panic conditions (Helbing et al. 2000). Based on the extended field model of the CA model, Nishinari found that the location of obstacles in buildings would affect the evacuation results *

Corresponding Author: [email protected]

46

DOI: 10.1201/9781003402220-6

(Nishinari et al. 2003). Tian explored the influence of different placement shapes of classroom seats on crowd evacuation time (Tian & Xu 2010). Yi Peng used Pathfinder to explore that in the case of the same area, the square obstacle in front of the exit of the shopping center has the greatest impact on the emergency evacuation, and the length direction of the obstacle along the depth direction of the exit is more reasonable (Yi et al. 2020). Lv Hui and others obtained other conditions unchanged by Pathfinder simulation. The distance between the obstacle and the exit changes from near to far, and the evacuation efficiency decreases first, then increases, and then decreases. The obstacle’s size also significantly impacts evacuation efficiency (Lv et al. 2020). Shen conducted an experimental study on pedestrian dynamics in the presence of crossing obstacles and concluded that in the pedestrian evacuation experiment in the presence of crossing obstacles, the evacuation time of pedestrians decreased with the increase in the distance between obstacles and exits (Shen 2020). Chen studied the pedestrian evacuation simulation under the influence of space obstacles and concluded that the evacuation time of the system would decrease with the increase of the safety exit width and the distance between the obstacle and the safety exit (Chen 2019). Through the analysis of existing research, setting obstacles at the exit may positively impact evacuation. However, the research on the influence of obstacles on evacuation efficiency under multivariate needs to be more specific and in-depth. Pathfinder software is an intelligent emergency evacuation simulation software based on Agent. It can simulate the model’s evacuation time and path and describe crowd movement characteristics in Steering mode (Nelson & Mowrer 2002). Therefore, in the case of determining the location of obstacles, this paper studies the influence of the shape and size of obstacles on evacuation efficiency under different evacuation conditions by controlling variables and analyzing the reasons.

2 ESTABLISHMENT OF THE MODEL 2.1

Parameter setting

The model parameters mainly include model obstacle parameters and personnel data. The combination of the shape and size of obstacles is complex. In order to reduce the influence factors of model simulation and better use the control variable method to analyze the simulation results, this paper makes some assumptions about the model. l

l

The obstacle is a left-right symmetrical figure, and the symmetry axis of the obstacle coincides with the left-right symmetry axis of the evacuation exit. Obstacle shape Settings graphics with different sides, select triangle, square, rhombohedral, pentagon, hexagon, and circle. The size of the square obstacle is 0.2 m
0.2 m, 0.4 m
0.4 m, 0.6 m
0.6 m, 0.8 m
0.8 m, and 1.0 m
1.0 m. The size of other shapes is based on the square obstacle, and the comparison with a square obstacle is shown in Figure 1.

Figure 1.

Size diagram of each shape obstacle.

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l

In order to reduce the influence of evacuation crowds on evacuation efficiency and improve the universality of simulation results, this paper mainly studies the influence of obstacle conditions on evacuation efficiency. In order to reduce the influence of evacuation crowds on evacuation efficiency and improve the universality of simulation results, the personnel are unified without considering the individual differences within the crowd. The specific parameters of evacuees are set in Table 1. Table 1.

2.2

Evacuation of individual characteristic parameters.

Parameter Name

Parameter Size

Height / m Shoulder Width/cm Turn to Update Time / s Collision Response Time / s Comfortable Distance / m Maximum Individual Moving Speed / (ms-1)

1.8288 45.58 0.1 1.5 0.08 1.19

Determination of optimal location

In order to avoid the influence of location factors on evacuation efficiency and make the shape and size of obstacles have a better evacuation effect, it is necessary to determine the best location to simulate the shape and size of obstacles. In this paper, the setting of obstacle size is based on the square size as a reference. Therefore, the square obstacle is selected for simulation to determine the optimal position with the highest evacuation efficiency. Considering the influence of the position and size of the obstacle on the evacuation time, the model conditions are as follows: the size of the rectangular room is 10 m
8 m, the evacuation exit is located in the center of a long side, the width is 1.4 m, and the distance between the front end of the square obstacle and the exit is 0.6 m, 0.8 m, 1.0 m, 1.2 m, 1.4 m. The model diagram is shown in Figure 2. The number of people evacuated is 100, 150, and 200. Different crowd distribution will lead to different evacuation times (Nishinari et al. 2003), so the personnel distribution is the same at the beginning of the simulation. Through simulation, it is concluded that in the case of 100 people, 150 people, and 200 people, the evacuation time without obstacles is 56.5 s, 80.0 s, and 113.8 s. The specific evacuation results are shown in Figure 3.

Figure 2.

Square obstacle model diagram.

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Figure 3.

Square obstacle evacuation time.

It can be seen from Figure 3. that the evacuation time fluctuates with the increase in the size of the square obstacle, except that the distance between the obstacle and the exit is 0.6 m. The evacuation effect of the green curve (the square obstacle is 1.0 m away from the exit) is better than that of other curves. In most cases, the evacuation effect of the square obstacle is 1.0 m away from the evacuation exit, and the shortest evacuation time is 1.0 m away from the evacuation exit. When 100 people, the evacuation time is the shortest when the obstacle is 1.0 m away from the evacuation exit, and the size is 0.8 m
0.8 m, which is 8.3% higher than the evacuation efficiency without obstacles. When 150 people, the evacuation time is the shortest when the obstacle is 1.0 m away from the evacuation exit, and the size is 0.6 m
0.6 m. For 200 people, the evacuation time is the shortest when the obstacle is 1.0 m from the evacuation exit and the size is 0.4 m
0.4 m. Therefore, this paper selected 1.0 m away from

49

the evacuation exit as the best location to set obstacles. On this basis, it simulated the evacuation law of obstacle shape and size. 3 OBSTACLE SIMULATION 3.1

Design of work condition

Considering the influence of the shape and size of the obstacle on the evacuation efficiency, the working conditions of the model are set as follows: the size of the rectangular room is 10 m
8 m, the width of the door is 1.4 m, and is located in the center of the long side of the room, and the triangle, square, rhombohedral, pentagon, hexagon and circular obstacles are set. The obstacles of each shape are located in the center of the exit, the distance from the exit is 1.0 m, and the specifications increase from 0.2 m to 1.0 m with the law of 0.2 m. The number of evacuees is set to 100 people, 150 people, and 200 people, and the position of people in the model with the same number of people is kept unchanged at the beginning of the evacuation. The model is shown in Figures 4 and 5.

Figure 4.

Figure 5.

Obstacle-free evacuation model.

Comparison of 200 evacuation models at 50 s.

50

3.2

Analysis of effect

Using Pathfinder to simulate the above model, the evacuation time-obstacle shape curve of triangle, square, rhombohedral, pentagon, hexagon, and circular obstacles at a distance of 1.0 m from the evacuation exit can be obtained. That is, the influence curve of different shape obstacles on evacuation time, as shown in Figure 6. The evacuation time-obstacle

Figure 6.

The curve of evacuation time changes with the obstacle shape.

51

shape curve under the same size of the obstacle can also be obtained, that is, the evacuation time changes with the size of the obstacle, as shown in Figure 7. Figure 6 shows that the evacuation time of 100 people without obstacles is 56.5 s, and the evacuation time fluctuates with the shape of obstacles. In most cases, the evacuation time is shorter than that without obstacles. When the obstacle is a regular pentagon with a size of 1.0 m, the evacuation time is the shortest, about 11.0% shorter than 56.5 s without obstacles. Set the size of 0.6 m circular obstacle evacuation time is the longest. The evacuation time without obstacles for 150 people is 80.0 s, and the evacuation time fluctuates with the change of obstacle shape. When the obstacle is a regular triangle with a

Figure 7.

Evacuation time change curve with obstacle size.

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size of 0.8 m and a regular pentagon with a size of 1.2 m, the evacuation time is the longest; when the obstacle is an inverted pentagon with a size of 1.2 m, the evacuation time is the shortest, which is about 7.5% shorter than 80.0 s without obstacles. The evacuation time of the rhombus and pentagonal obstacles is above 80.0 s, which has no positive effect on the evacuation effect. The obstacle-free evacuation time for 200 people is 113.8 s. The evacuation time changes with the shape of the obstacle but generally fluctuates below 113.8 s. The evacuation time in only three cases is longer than that without obstacles, and the evacuation time is the longest when the obstacle is an equilateral triangle with a size of 0.6 m. The evacuation time of hexagonal obstacles with a size of 1.2 m and circular obstacles with a size of 1.0 m is the shortest, 101.0 s, which is about 11.2% shorter than that without obstacles. Under a different number of evacuees, evacuation time changes with the size of the same shape of the obstacle roughly the same trend. Under the premise that the obstacle is set to be 1.0 m away from the evacuation exit, the evacuation time varies with the shape of the obstacle, and there is no obvious rule with the shape of the obstacle. Under the obstacle model of different numbers of people, the influence of obstacles on evacuation time is different. When the number of evacuees is 200, the evacuation effect of setting obstacles at the evacuation exit is the best, followed by 100 people, and 150 people are the worst. The reasons for this result may be: 1) The number of evacuees affects the evacuation effect of the shape and size of obstacles; 2) The software randomly distributes the evacuation crowd, and the different crowd distribution has an impact on the evacuation time. Under the premise of the same number of people, the different angles of the same shape have different effects on evacuation time. For example, the evacuation effect of the inverted triangle obstacle is better than that of the equilateral triangle obstacle. This phenomenon may be due to the shape characteristics of the inverted triangle. The flow of people is diverted and guided earlier than the equilateral triangle obstacle, which delays the formation of the “arch” structure and makes the evacuation of the crowd before the evacuation exit smoother. Figure 7 shows that the evacuation time fluctuates with the increase in obstacle size, and in most cases, the evacuation time is shorter than that without obstacles. Under the premise of the same obstacle shape, with the increase of obstacle size, the fluctuation trend of evacuation time of different people is different. Under the premise of the same number of people but different shapes of obstacles, the changing trend of evacuation time is different with the increase of obstacle size, and the length of evacuation time is not proportional to the size of obstacles. Therefore, under the premise of different obstacle shapes, with the increase in obstacle size, the fluctuation trend of evacuation time is different, and the optimal obstacle size of different obstacles is different. It can be seen from Figures 6 and 7 that the shape and size of the evacuation exit obstacles affect the evacuation time. The influence on the evacuation effect is also related to the number of evacuees. Under different evacuation numbers, the influence of the same obstacle on the evacuation results is different. Therefore, the best position to disperse the exit obstacles should be designed according to the actual situation and needs.

4 CONCLUSIONS In order to further study the influence of obstacles on evacuation efficiency, this paper studies the influence of obstacle shape and size on evacuation efficiency and draws the following conclusions through simulation. 1) Setting obstacles before the evacuation exit will affect the evacuation results. Unlike people’s traditional cognition, obstacles under certain conditions will positively impact evacuation results. Through the control variable method, the influence of the shape and

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size of the obstacles is studied and analyzed. The obstacles may affect the trajectory of the crowd, advance, or delay the formation of the arch structure, thus affecting the evacuation time. 2) For the influence of obstacle shape on evacuation efficiency, the evacuation time fluctuates with the change of obstacle shape. Under a different number of evacuees, the trend of evacuation time changing with the shape of obstacles is different. When the number of evacuees is 200, the positive influence of obstacles on the evacuation effect is the best, followed by 100 people. There is no regularity between the changing trend of evacuation time and the geometric edge number of obstacles. 3) For the influence of obstacle size on evacuation efficiency, similar to the influence of obstacle shape, the evacuation time fluctuates with the increase of obstacle size, and the evacuation time is not proportional to the size of the obstacle. The size of the obstacles significantly affects the evacuation efficiency for different shapes of obstacles, and the optimal size corresponding to different shapes of obstacles is different. 4) Under the premise that the location of the obstacle is determined, the influence of the same shape and the same size of the obstacle on the evacuation effect is different due to the different placement angles. For example, the inverted triangle obstacle of the same size is generally better than the equilateral triangle obstacle. This paper needs to conduct in-depth research on the above phenomena. It does not consider the influence of irregular obstacles, the height of obstacles, and whether it can cross obstacles on evacuation efficiency. In the future, the influence of complex factors such as obstacle placement angle, irregular obstacles, and obstacle height on evacuation efficiency can be considered, or the influence of multi-factor coupling on evacuation efficiency can be further studied.

REFERENCES Alonso-Marroquin F. and Azeezullah S.I., Galindo-Torres S.A. et al. Bottlenecks in Granular Flow: When Does an Obstacle Increase the Flow Rate in an Hourglass?. Physical Review E, 2012, 85(2): 020301. Helbing D., Farkas I.J. and Vicsek T. Simulating Dynamical Features of Escape Panic [J]. Social Science Electronic Publishing, 2000, 407(6803): 487–90. Hui L., Xiangyuan L. and Yinglong W., et al. Study the Influence of Laws of Obstacle in Evacuation Exit on Evacuation Efficiency [J]. Journal of Safety Science and Technology, 2020, 16(01): 141–145. Lingli Z., Baoyun W., Ting W., Maolin J. Statistical Analysis of Stampede Accident for Crowd-Gathered Place [J]. Safety & Security, 2019, 40(10): 9–14. Nelson H. and Mowrer F. Emergency Movement-the SFPE Handbook of Fire Protection Engineering (3rd Ed.) [M]. Bethesda: Society of Fire Protection Engineers. 2002. Nishinari K., Kirchner A. and Namazi A., et al. Extendedfloor Field CA Model for Evacuation Dynamics [J]. IEICE Transactions on Information and Systems, 2003(7): 1–7. Peng Y., Xingrun Z. and Pengkun Y.. Influence of Obstacles in Front of Shopping Center’s Exit on Emergency Evacuation Efficiency [J]. Fire Science and Technology, 2020, 39(01): 62–66. Tian Q. and Xu Y. Cellularautomaton Simulation of Emergent Evacuation Considering the Classroom Seats Arrangement [C]// Third International Joint Conference on Computational Science & Optimization. IEEE, 2010. Tianyang L. The Impact of the Exit Location and Forms on the Escape Efficiency of Mice Under Irrational Conditions [D]. Chengdu: Southwest Jiaotong University, 2017: 75–76. Wenxin C. Simulation Research on Pedestrian Evacuation Under the Influence of Spatial Obstacles [D]. Beijing: Beijing Jiaotong Unversity, 2019: 89–91. Zhiwei S. Experimental Study on Pedestrian Dynamics in the Presence of Obstacles that Can be Stepped over [D]. Hefei: Hefei University of Technology, 2020: 67–68. Zuriguel I., Janda A. and Garcimartín A. et al. Silo Clogging Reduction by an Obstacle [J]. Physical Review Letters, 2011, 107(27): 278001.

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Advances in Traffic Transportation and Civil Architecture – Liu, Qi & Law (eds) © 2023 copyright the Author(s), ISBN 978-1-032-51438-3

Traffic theory research on the attractiveness of subsidy policy for new energy vehicles based on the multinomial logit model Dawei Wu* & Lu Ma School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China

ABSTRACT: With the deepening of sustainable development, environmental protection and clean energy are becoming increasingly important. The advantages of new energy vehicles in terms of efficiency and environmental protection have won the favor of consumers. Various localities have issued many subsidy policies to improve the sales of new energy vehicles, but the relevant transportation theories are lacking. In order to improve the attractiveness of new energy vehicle subsidy policies, promote socially sustainable development and fill the gap in transportation theory research, this paper focuses on the attractiveness of different subsidy policies to consumers. According to the data from 26 provinces in China, the multinomial logit model (MNL) is used to fit and analyze the choices of consumers with different gender, educational backgrounds, age, and other characteristics in subsidy policies. The results show that 47.28%, 22.28%, 8.15%, and 19.02% of respondents chose policies such as purchase subsidiary, purchase tax exemption, free parking, and no wagging. Therefore, it can be concluded that sex will not become a factor in distinguishing the choice of subsidy policies. More and more people are in favor of the lump-sum subsidy policy as the age increases. Highly educated consumers favor the convenient policy of no wagging, and consumers with new energy vehicles look forward to other policies.

1 INTRODUCTION With the deepening of China’s social modernization and the rapid improvement of productivity, the importance of sustainable development of environmental protection and resource utilization has attracted people’s attention. Electric vehicles have gradually come into the public view because of their characteristics of saving resources and effectively curbing air pollution. At the same time, introducing subsidy policies further makes new energy vehicles win the favor of consumers. By the end of 2020, the number of new energy vehicles in China had reached 4.92 million, accounting for 1.75% of the total number of vehicles, an increase of 1.11 million over 2019, an increase of 29.18% (Ministry of Transport of the People’s Republic of China 2021). It can be seen that the new energy vehicle market is far from saturation. Many achievements have also been made in the transportation theory research of subsidy policies at home and abroad: Erdem et al. (2020) found that environmental awareness is the main reason why consumers choose to buy new energy vehicles; The research results of Yang et al. (2020) studied that the subsidy policy objectively promotes the promotion of energy-saving vehicles and new energy vehicles. Tu et al. (2019) discussed the key factors affecting consumer behavior by constructing a theoretical framework. Liu et al. (2020) studied that local finance and innovation ability directly affect their policy-making.

*

Corresponding Author: [email protected]

DOI: 10.1201/9781003402220-7

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Although the transportation theory research of the existing subsidy policy comprehensively analyzes the impact of external factors, such as the subsidy policy on consumers’ purchase of new energy vehicles, it rarely considers the impact of characteristic factors, such as consumers’ age and education, on the choice of new energy vehicle subsidy policy. Based on this, this paper proposes the traffic theory research on the attractiveness of subsidy policy for new energy vehicles based on the multinomial logit model (MNL). It uses the MNL model to analyze the choice behavior of consumers of different ages, genders, education levels, and incomes to the main new energy subsidy policies to enhance the attractiveness of new energy vehicles to consumers.

2 MODEL AND METHOD 2.1

Random utility theory

The emergence of a phenomenon is a combination of individual behaviors. In terms of new energy vehicles, to improve their popularity, the most important thing is to study the impact of subsidy policies on consumers’ decision-making behavior. For decision analysis, the most important thing is to quantify the uncertainty of the research phenomenon and assign a value to the possible results. The phenomenon of quantitative uncertainty can be expressed by its occurrence probability, and the possible results need to use the random utility theory proposed by Manski in 1977. The basic meaning of the theory is as follows: The subsidy policy choice set by the consumer n is An, the utility of choosing j a travel scheme Yjn, then the condition for choosing i a subsidy policy is: Yin  Yjn ; i 6¼ j; j 2 An

(1)

The utility consists of two parts, and there is a linear relationship between the components (Zhang 2012). Assuming that Yin is the utility of consumer n choosing i subsidy scheme, there is: Yin ¼ Xin þ ein

(2)

Where Xin is the fixed term of the utility function, and ein is the probability term of the utility function. According to the utility maximization theory, the selection probability Pin can be expressed as: Pin ¼ PðYin > Yjn ; i 6¼ j; j 2 An Þ ¼ PðXin þ ein > Xjn þ ejn ; ; i 6¼ j; j 2 An Þ ¼ Pðein > Xjn þ ejn  Xin ; ; i 6¼ j; j 2 An Þ Where 0  Pm  1 and

X

(3)

Pin ¼ 1

i2An

One of the most commonly used expressions of the utility function is a linear form (Zong 2008), and its formula is: Xin ¼ wZin ¼

K X

wk Zkin

(4)

k¼1

Where w is the parameter vector, Zin is the feature vector for selecting the subsidy scheme object, wk is the undetermined coefficient, and Zkin is the eigenvalue of the k-th variable corresponding to the feature vector.

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2.2

Multinomial logit model

What is certain is that when the probability term obeys the Gumbel distribution, we can call it the logit model. When there are multiple elements in the set of subsidy schemes for consumers to choose from, we can establish the multinomial logit model (MNL). The specific formula is as follows: expðXin Þ Pin ¼ X ; i 2 An expðXjn Þ

(5)

j2An

Where Pin is the probability that the consumer n chooses the subsidy scheme i, Xin is the fixed item that the consumer n chooses i, An is the subsidy scheme selection set of consumer n, and j is the element in An. It should be noted that if the model uses the maximum likelihood estimation method to calibrate the parameters, the expression is: expðwZin Þ Pin ¼ X expðwZin Þ j2An

¼

1 X j2An

expð

K X

; i 2 An

(6)

wk ðZkjn  Zkin ÞÞ

k¼1

Based on the overall probability density function, the MNL model constructs a likelihood function, including location parameters. When the likelihood function value is the largest, the estimated value of the variable parameters of the object is solved (Bai 2015).

3 INTENTION SURVEY AND DATA ANALYSIS 3.1

Data acquisition

This survey is conducted in the form of a questionnaire composed mainly of personal information and new energy vehicles. Personal information includes gender, age, permanent residence area, and educational background. The relevant information on new energy vehicles includes the number of new vehicles owned by individuals and the choice of subsidy policies for new vehicles. The respondents fill in their personal information according to their actual situation and choose the appropriate subsidy policy for new energy vehicles according to their interests. There is no need to think too much, and the authenticity is strong. 3.2

Data analysis

New energy vehicles are popular all over the country, so this survey is conducted nationwide, and the survey data are from 26 provinces in the country. Most of the respondents are aged from 20 to 40. Consumers in this age group have a high degree of acceptance of new energy vehicles, and the results are highly representative. In addition, more than 84% of the respondents have not purchased new energy vehicles, which further enhances the authenticity of the subsidy policy choice. It is worth noting that 87% of the respondents have a bachelor’s degree or above, which further reveals the true views of highly educated consumers on the subsidy policy for new energy vehicles. The selection of subsidy policies is summarized in Table 1.

57

Table 1.

Summary of subsidy policies selections.

Subsidy Policy

Sample Size

Proportion

Purchase Subsidy Purchase Tax Exemption Free Parking No Wagging Others Total

87 41 15 35 6 184

47.28% 22.28% 8.15% 19.02% 3.26% 100%

In order to better express the education distribution of investigators corresponding to different subsidy policies, a violin map is drawn. The figure combines the advantages of a box line diagram and nuclear density diagram and intuitively shows the position of the median., which is convenient for analysis and judgment. In order to facilitate processing, the educational background is quantified in Figure 1 represents the high school and below, 2 represents a bachelor’s degree, and 3 represents a master’s degree and above. From Figure 1, it is easy to find that under normal circumstances, people with high education tend to choose the policy of no wagging and free parking, and people with bachelor’s degrees tend to choose the policy of car purchase subsidy and purchase tax exemption. In contrast, people with low education tend to choose other policies.

Figure 1.

Violin plot of subsidy policy choice corresponding to different academic qualifications.

In order to visualize the relationship between different gender and age and the selected subsidy policy, this paper gives two spinograms of classification variables and subsidy policy. The spinogram is a deformation of a stacking bar graph, which can show the probability distribution of dependent variables given a certain independent variable. As shown in Figure 2 and Figure 3, males tend to choose the policy of purchase subsidy, followed by purchase tax exemption, no wagging, and finally, free parking. Females’ choices are the

58

Figure 2.

The spinogram of the gender and subsidy policies.

Figure 3.

The spinogram of the age and subsidy policies.

same. Thus, there is not much difference between males and females in the choice of subsidy policy. The selection of different age groups is quite different. With the increase in age, more and more attention will be paid to purchase subsidies and tax exemptions, which further shows that this is also the core way to promote the popularity of new energy vehicles.

59

4 RESULTS The parameters of the MNL model are calibrated, and the results are shown in Table 2. It is worth mentioning that variables that do not appear in each category are reference variables. All model interpretation and derivation are carried out based on variable parameter values. The estimated parameters of each variable are shown in Table 2. Table 2.

Estimation results of the MNL model.

Variable

Purchase Tax Exemption Free Parking No Wagging Others

Intercept –1.262 Sex — Female –0.026 Educate — Bachelor degree 0.423 Master’s degreeor above 0.476 The number of new energy vehicles — One 0.795 Two 0.892 More than two –19.177

–1.959 — 1.275 — –1.302 0.209 — –0.410 –14.966 –18.055

–1.845 — 0.039 — 0.577 1.638 — 0.349 –15.832 –18.813

–1.714 — –0.273 — –2.721 –0.481 — 0.910 2.941 –15.334

According to the calibration results of the model in Table 2, quantitative analysis is carried out based on changing only one of the variables. The following conclusions can be made clear: in the MNL model, the intercept of each policy is larger, indicating that there are other influential policies in addition to the policies proposed in this paper, such as giving supporting services and so on. The policy choices of women are the same as those of men. However, the coefficient of Free Parking is 1.275, indicating that women are 3.5278 times more likely to choose this policy than men, indicating that women pay more attention to the policy of free parking fees than men. Academically, people with higher academic qualifications prefer the non-lottery policy, with a coefficient of 1.638. Compared with consumers who do not buy new energy vehicles, car owners are more expected to introduce other subsidy policies after enjoying the existing policies.

4 CONCLUSIONS This paper analyzes the decision-making research on the subsidy policy of consumers from 26 provinces in China. It uses the MNL model to fit and judge the choice of subsidy policy for new energy vehicles by consumers of different genders, ages, and academic qualifications. The results show that the policy choices between men and women are the same. With the increase in age, consumers will pay more attention to purchase subsidies and purchase tax exemptions. Consumers who own new energy vehicles will pay more attention to other subsidy policies based on existing ones. Highly educated consumers tend to choose policies such as no wagging. The study shows the attractiveness of the main subsidy policies for consumers with different characteristics to buy new energy vehicles, which helps to provide a scientific basis for the government to issue relevant subsidy policies to improve the sales of new energy vehicles and promote sustainable social development, which is of great significance. It should be noted that the MNL model used in this paper has IIA characteristics and will produce certain errors. In subsequent research, other models and independent variables can be considered to reduce the errors and obtain more accurate fitting results.

60

ACKNOWLEDGMENT This research was supported by the National Natural Science Foundation of China (No. 71971023).

REFERENCES Bai X.H., Study on the Influence of Transfer Preference on the Travel Choice Behavior of Urban Residents, Beijing Jiaotong University, 2015. Erdem C., Sentuerk I. and Simsek T., “Identifying the Factors Affecting the Willingness to Pay for FuelEfficient Vehicles in Turkey: a Case of Hybrids,” Energy Policy, vol. 38, pp. 3038–3043, January 2020. Liu W. and Yi H, “What Affects the Diffusion of New Energy Vehicles Financial Subsidy Policy? Evidence from Chinese Cities,” International Journal of Environmental Research and Public Health, vol. 17, pp. 726– 733, October 2020. Ministry of Transport of the People’s Republic of China, “Ministry of Public Security: in 2020, there will be 33.28 Million Newly Registered Motor Vehicles and 4.92 Million New Energy Vehicles,” Commercial Vehicles, vol. 1, pp. 9–10, January 2021. Tu J.C. and Yang C., “Key Factors Influencing Consumers’ Purchase of Electric Vehicles,” Sustainability, vol. 11, pp. 3863–3864, July 2019. Yang T., Xing C. and Li X., “Evaluation and Analysis of New-Energy Vehicle Industry Policies in the Context of Technical Innovation in China,” Journal of Cleaner Production, vol. 281, pp. 125–126, January 2020. Zhang J.N., Passenger Transport Supply Structure Planning Theory of Comprehensive Transport Corridor, Beijing Jiaotong University, 2012. Zong F., Research on Evaluation of Traffic Demand Management Strategy Based on the Disaggregate Model, Jilin University, 2008.

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Advances in Traffic Transportation and Civil Architecture – Liu, Qi & Law (eds) © 2023 copyright the Author(s), ISBN 978-1-032-51438-3

Analysis of the emission factors of new energy vehicles compared to fuel vehicles during the lifecycle Sicheng Bing† Transportation Engineering College, Dalian Maritime University, Dalian, China

Runze Hong† Faculty of Environment and Life, Beijing University of Technology, Beijing, China

Jun Li*† Computer Science in Internet Technology, Hong Kong Metropolitan University, Hong Kong, China

ABSTRACT: This study is based on data collection, analysis, and theoretical analysis. This research first analyses the influencing factors of the emission content. Then it uses the Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation model (GREET) to collect the emission factor content of different electric vehicles under four energy sources. The experiment compared them and found that the fuel vehicles (ICEV-E10) have the highest emission content. However, the experiment also indicates that the electric vehicle (EV300) has higher emissions than others under the battery production process. Besides, comparing electric vehicles with four energy sources shows that hydroelectric power has the lowest emissions and coal-fired power has the highest emissions. Finally, this study gives some theoretical advice to reduce the emission of light vehicles. The innovative contribution of our study is that we use GREET to collect data to compare different types of vehicles, and we found that new energy vehicles are not better than fuel vehicles.

1 INTRODUCTION Currently, the emission of global green gas is increasing and fossil energy is constantly being consumed. Many countries started to carry out the “carbon peaking and carbon neutrality goal” to protect the environment, and China is no exception. In China, transport accounts for a large part of carbon emissions. In 2020, transport had more than 2.3 billion tons of carbon emissions, about 23 percent of total emissions. So, the new energy vehicle becomes popular depending on its clean advantage. However, in recent research, new energy vehicle has more carbon emissions in energy supply and components production. Therefore, this study will determine whether a new energy vehicle is a better choice in the future. Kui Yan’s team (Yan 2021) found that the conversion shows that the emissions of electric vehicles are closely related to the regional energy mix, so we know that electric vehicles are not really “zero emissions.” Ningning Ha analyzed the influence of vehicle weight and energy construction. Then one research showed that every 100 kg increase in vehicle weight would increase the energy consumption demand of electric vehicles by 0.0051 KWH/km (Han 2018), and compared with traditional internal combustion engines, electric vehicles,

*

Corresponding Author: [email protected] These authors contributed equally.

†

62

DOI: 10.1201/9781003402220-8

and hybrid vehicles found that under the current energy structure, the electric vehicle has the highest greenhouse gas emissions in the whole life cycle, which are 1.19 and 1,06 times of traditional internal combustion engines and hybrid vehicles, respectively. Junhua Yan’s team (Yan 2018) found that the energy consumption of electric vehicles is the least, 52.20% and 59.92% of the fuel and hybrid vehicles, respectively. Wu’s team (Geng 2015) used Monte Carlo simulations regarding economic benefits. It was found that the relative cost efficiency of electric vehicles depends on driving distance and vehicle class, mainly due to their lower operating costs per kilometer compared to conventional vehicles. In terms of energy sources, Juan Li (2015) found that increasing the proportion of nuclear energy and water energy in the power generation energy construction can effectively reduce the environmental impact of electric vehicles. In terms of vehicle operation, Zhihong Wang’s team (2022) used an energy consumption experiment to compare the carbon emission of light gasoline vehicles and electric vehicles. Finally, it is concluded that electric vehicles have obvious advantages, and the average CO2 emission in urban sections is only about 50% of the light gasoline vehicle. The carbon emissions between electric vehicles and hybrid vehicles are different. In terms of operation, new energy vehicles have a great advantage, but the carbon emissions in energy supply and other aspects are higher than in fuel vehicles. This study will show the influence factors on emission. Then it will use GREET to collect the emission factor content of different electric vehicles under four energy sources. Finally, it compares them and determines whether a new energy vehicle is the better choice.

2 CARBON EMISSIONS THROUGHOUT THE LIFE CYCLE OF LIGHT VEHICLES After the above discussion, it is essential to analyze the emission factors of fuel vehicles and new energy vehicles in detail and compare each emission factor. Based on this, and supplemented by the analysis of carbon emission factors in the life cycle of light vehicles by other scholars, the final analysis and conclusions are made by comparing and analyzing the differences between them. 2.1

Factors influencing carbon emissions of fuel vehicles

2.1.1 Known conclusions and data First, based on the comparison of Shi Xiaoqing et al. (2015), which is about the carbon emissions of fuel taxis and electric taxis throughout the life cycle, it can be represented that the conclusions reached them using gabi4.4 software and LCA method framework are consistent with the conclusions reached by our team. Among the various environmental impacts, from the comparative value of the characteristics of GWP at each life cycle stage, fuel vehicles are 49% more than electric vehicles. In the use stage of automobiles, fuel vehicles emit 55% more emissions than electric vehicles. In terms of MAETP, the carbon emissions of the production stage of fuel vehicles are significantly higher than those of electric vehicles. In addition, the contribution of the recycling phase of each impact type to the environmental impact of the entire life cycle of both models is negative. According to Tong Xiaobo (2017), taking Beijing as an example, as of 2018, mobile pollution sources in Beijing’s local CO emissions have accounted for as much as 45%, ranking the top among many pollution sources. In London, road traffic also accounts for 49% of NOx emissions and 30% of CO emissions. The London part is not inconsistent with the CO emission dominance of fuel vehicles in operation analyzed by our group because, as of 2018, the penetration rate of new energy vehicles in the London area is higher than that of China, so the proportion of nitrogen oxides in their emissions will be higher than that of China.

63

It is understood that Zhang Lei et al. (2013) et al. found that the main reason for the environmental impact of global warming lies in the emission of greenhouse gases of which, mainly CO2 emissions, in the use stage of electric vehicles, due to the lack of exhaust emissions, electric vehicle power systems do not emit CO2. CO2 is mainly generated in the production process of electric energy. The environmental impact of the internal combustion engine vehicle power system mainly occurs in the use stage, accounting for 86.1% of the environmental impact value of the whole life cycle. In addition to ozone depletion and radioactive radiation, the proportion of other environmental impact values is relatively average. Through the research of the first few parts, it is easy to find that in the whole life cycle of the fuel vehicle, the results of our group are the same as those of other scholars. The carbon emissions of fuel vehicles are mainly concentrated on the carbon emissions of operation, followed by the WTP process. In terms of component production and oil production, the carbon emissions of fuel vehicles account for a relatively low proportion. 2.1.2 Panel conclusion In this group’s study, the same conclusion was reached that the carbon emissions during driving are the largest, mainly because fuel vehicles will continue to burn fuel during driving and release greenhouse gases. This part of the carbon emissions is much more than other cycles. Therefore, it is concluded that the carbon emissions of the whole life cycle of fuel vehicles are mainly affected by the carbon emissions generated when the fuel is burned, that is, the fuel cycle stage. 2.2

Carbon emission influencing factors of new energy vehicles

2.2.1 Known conclusions and data After analyzing the fuel cycle and vehicle cycle, Haninging (Han 2021) concluded that although the energy consumption intensity of the electric vehicle cycle is the highest, the consumption intensity of traditional internal combustion engine vehicles in the whole life cycle is still the highest, which shows that electric vehicles can achieve certain energy-saving effects. The authors focused on the differences in methane, nitrogen oxide, and carbon dioxide emission intensities in this analysis. Another article by Shi Xiaoqing et al. (2013) studied the carbon dioxide emission coefficient and emission reduction potential of different types of batteries in electric vehicles. In iron phosphate batteries, lithium manganese oxide batteries, and lead-acid batteries, iron phosphate batteries have good emission reduction space. Moreover, the carbon emissions of electric vehicles in the life cycle stage of electric vehicles are affected by different factors such as the energy structure of power generation, the type of vehicle fuel, the type of vehicle, and the urban traffic conditions. In the article, Nie Kai et al. (2015) mainly analyzed that according to the calculation of carbon emission reduction rates, new energy vehicles can significantly reduce carbon dioxide emissions. However, the article only analyzed carbon dioxide emissions from the vehicle driving process. However, it quantitatively took several important cities in China as a sample to calculate the emission reduction space after changing from fuel vehicles to new energy vehicles. 2.2.2 Panel conclusion Through the calculation of the GREET model, it can be concluded that the carbon emission factor of new energy vehicles is most affected by upstream energy. The carbon emissions generated are different in different ways of obtaining electricity. However, the carbon emissions in the driving stage are generally lower than the carbon emissions in the driving stage of fuel vehicles.

64

3 EMISSION FACTOR’S DATA COLLECTION AND ANALYZE The life cycle of a car is divided into four stages: production, energy, use, and recycling. Because the data this experiment collected is the same for each vehicle in recycle stage, this study will mainly compare and analyze the data of the other three stages. The research collected “Components,” “Fluids,” and “Battery” for the production stage, then collected “WTP” for the energy stage, and finally, it collected “Operation Only” for the use stage. By the way, this study collected “WTW” because it means one-time carbon emission. During the processing of data collection, the research found that the order of magnitude in total energy consumption (Total Energy) and green gas (GHG-100) are extremely higher than others. In order to analyze them more clearly, this study will separate them from others when it begins to compare. 3.1

Fuel vehicle emission factors

The fuel vehicles use ICEV-E10 as represented. As shown in Table 1, the average energy consumption is 1719125.512J/mi; GHG-100 has the biggest content in emission factors, and its average is 0.12100385kg/mi. Conversely, PM10 has the smallest content in emission factors, which only have 6.49632
106kg/mi.

Table 1.

ICEV’s emission factors summary.

Total Energy (J/mi) GHG-100 (kg/mi) CO (kg/mi) NOx (kg/mi) PM10 (kg/mi) PM2.5 (kg/mi) VOC (kg/mi)

3.2

WTP

WTW

Components Fluid

Battery

Operation only ADR

AVERAGE

1219409

5744642

381267.6716

72481.1815 4550.6166 4525233

86295.1117 1719125.512

0.099

0.4085

0.0254

0.0044

2.24E-04

0.3095

2.94E-06

0.12100385

6.69E-05 1.09E-04 1.22E-05 8.19E-06

0.0028 1.91E-04 1.60E-05 1.16E-05

1.09E-04 2.57E-05 1.19E-05 5.50E-06

1.77E-06 4.82E-06 7.87E-07 4.20E-07

2.19E-07 2.55E-07 2.88E-07 1.42E-07

0.0027 8.24E-05 3.80E-06 3.36E-06

4.49E-06 7.79E-07 4.71E-07 0.0053

0.000811736 5.90797E-05 6.49632E-06 0.00076131

1.26E-04

1.95E-04

2.12E-05

1.59E-04

1.21E-07

6.88E-05

9.97E-06

8.27675E-05

New energy vehicle emission factors

This research uses HEV, PHEV, and EV300 for new energy vehicles. Besides, the study collected data on EV300 under the coal-fired, natural gas-fired, nuclear, and hydroelectric power supply. 3.2.1 Three types of new energy vehicle emission factors As shown in Table 2, the average energy consumption in HEV is 1272019.919J/mi. GHG100 has the biggest content in emission factors, averaging 0.089257563kg/mi, while PM10 has the smallest content in emission factors, at only 5.74965
106kg/mi. For the PHEV, data is shown in Table 3. The average total energy consumption is 1089382.416J/mi, GHG-100 has the most, which is 0.07070042kg/mi, whereas PM10 only has 7.11279
106 kg/mi on average. For the EV300, data is shown in Table 4. Since electric vehicle does not generate emission factors during operation, all the remaining data except the total energy consumption are

65

Table 2.

HEV’s emission factors summary. WTP

WTW

Components Fluid

Battery

Operation only

ADR

AVERAGE

Total Energy (J/mi) GHG-100(kg/ mi) CO (kg/mi) NOx (kg/mi)

885266.2048 4170494 383724.3439 66272.8374 26858.9341 3285228

86295.1117 1272019.919

0.0719

0.2969

0.0254

0.004

0.0016

0.225

2.94E-06

0.089257563

4.86E-05 7.88E-05

1.11E-04 2.59E-05

1.65E-06 4.34E-06

2.47E-06 1.74E-06

0.0027 6.92E-05

4.49E-06 7.79E-07

0.000809726 4.69723E-05

PM10 (kg/mi)

8.86E-06

1.21E-05

7.11E-07

1.60E-06

3.80E-06

4.71E-07

5.74965E-06

PM2.5 (kg/mi)

5.95E-06

5.68E-06

3.77E-07

6.97E-07

3.36E-06

0.0053

0.000760768

VOC (kg/mi)

9.14E-05

0.0028 1.48E04 1.27E05 9.31E06 1.29E04

2.10E-05

1.59E-04

5.19E-07

3.71E-05

9.97E-06

6.38734E-05

ADR

AVERAGE

Table 3.

PHEV’s emission factors summary. WTP

Total Energy (J/ mi) GHG-100(kg/mi) CO (kg/mi) NOx (kg/mi) PM10 (kg/mi) PM2.5 (kg/mi) VOC (kg/mi)

Table 4.

WTW

Components Fluid

Battery

Operation only

1246599 3483978 401431.7044 66272.8374 103721.2609 2237379

86295.1117 1089382.416

0.1337 6.05E05 1.05E04 1.43E05 8.57E06 5.01E05

0.2288 0.0012

0.0266 1.16E-04

0.004 1.65E-06

0.0067 4.45E-06

0.0951 0.0011

2.94E-06 4.49E-06

0.07070042 0.00035526

1.34E04 1.59E05 9.97E06 6.56E05

2.70E-05

4.34E-06

8.07E-06

2.89E-05

7.79E-07

4.39621E-05

1.27E-05

7.11E-07

4.09E-06

1.59E-06

4.71E-07

7.11279E-06

5.95E-06

3.77E-07

1.41E-06

1.40E-06

0.0053

0.000761098

2.17E-05

1.59E-04

1.23E-06

1.55E-05

9.97E-06

4.60898E-05

Battery

Operation only

ADR

AVERAGE

EV300’s emission factors summary. WTP

Total Energy (J/ mi) GHG-100(kg/mi) CO (kg/mi) NOx (kg/mi) PM10 (kg/mi) PM2.5 (kg/mi) VOC (kg/mi)

WTW

Components Fluid

1395736 2749689 357193.2305 16997.0482 495466.3464 1353953

86295.1117 922189.9624

0.1654 6.39E05 1.14E04 1.69E05 9.69E06 1.82E05

0.1654 6.39E05 1.14E04 1.69E05 9.69E06 1.82E05

0.0238 9.94E-05

0.0011 6.99E-07

0.0325 2.16E-05

2.94E-06 4.49E-06

0.06470049 4.23298E-05

2.41E-05

6.34E-07

4.07E-05

7.79E-07

4.91934E-05

1.09E-05

1.23E-07

2.11E-05

4.71E-07

1.10761E-05

5.09E-06

4.66E-08

7.02E-06

0.0053

0.000888589

1.84E-05

1.57E-04

5.85E-06

9.97E-06

3.79995E-05

66

zero. The average total energy consumption is 922189.9624J/mi. As usual, GHG-100 has the biggest content in emission factors, averaging 0.06470049kg/mi, whereas PM10 has the smallest emission factor content, with only 1.10761
105kg/mi. 3.2.2 EV300’s emission factors under different supplies First, the data on coal-fired power is shown in Table 5. Total energy consumption is 1280303.105J/mi on average. GHG-100 has 0.142267157kg/mi on average, and PM10 is 2.55691
105kg/mi on average. Furthermore, the data on natural gas power is shown in Table 6. The total energy consumption is 1026584.534J/mi. GHG-100 has 0.041867157kg/mi on average, and PM10 has 6.96872
106kg/mi. For nuclear power, data is shown in Table 7. The total energy consumption is 533720.6105J/mi. GHG-100 has 0.010333824kg/mi on average, and PM10 has 5.52579
106kg/mi. Table 5.

EV300’s emission factors under coal-fired power summary. WTP

Total Energy (J/ mi) GHG-100(kg/mi) CO (kg/mi) NOx (kg/mi) PM10 (kg/mi) PM2.5 (kg/mi) VOC (kg/mi)

Table 6.

WTW

Components Fluid

Battery

Operation only

ADR

AVERAGE

2649132 4003085 357193.2305 16997.0482 495466.3464 1353953

86295.1117 1280303.105

0.3981 1.18E04 2.87E04 6.04E05 2.68E05 3.24E05

0.3981 1.18E04 2.87E04 6.04E05 2.68E05 3.24E05

0.0238 9.94E-05

0.0011 6.99E-07

0.0325 2.16E-05

2.94E-06 4.49E-06

0.142267157 6.05188E-05

2.41E-05

6.34E-07

4.07E-05

7.79E-07

0.0001066

1.09E-05

1.23E-07

2.11E-05

4.71E-07

2.55691E-05

5.09E-06

4.66E-08

7.02E-06

0.0053

0.0008943

1.84E-05

1.57E-04

5.85E-06

9.97E-06

4.27391E-05

EV300’s emission factors under natural gas power summary. WTP

Total Energy (J/mi) GHG-100(kg/ mi) CO (kg/mi) NOx (kg/mi) PM10 (kg/mi) PM2.5 (kg/mi) VOC (kg/mi)

WTW

Components Fluid

Battery

Operation only

ADR

AVERAGE

1761117 3115070 357193.2305 16997.0482 495466.3464 1353953

86295.1117 1026584.534

0.0023

0.1915

0.0238

0.0011

0.0325

2.94E-06

0.041867157

4.22E06 4.20E06 2.79E07 1.95E07 1.10E06

8.57E05 1.12E04 8.94E06 8.63E06 2.61E05

9.94E-05

6.99E-07

2.16E-05

4.49E-06

3.60149E-05

2.41E-05

6.34E-07

4.07E-05

7.79E-07

3.04279E-05

1.09E-05

1.23E-07

2.11E-05

4.71E-07

6.96872E-06

5.09E-06

4.66E-08

7.02E-06

0.0053

0.000886829

1.84E-05

1.57E-04

5.85E-06

9.97E-06

3.64767E-05

67

Table 7.

EV300’s emission factors under nuclear power summary. WTP

WTW

Components Fluid

Battery

Operation only

ADR

AVERAGE

Total Energy (J/mi) GHG-100(kg/ mi) CO (kg/mi)

36093.5369 1390046 357193.2305 16997.0482 495466.3464 1353953

86295.1117 533720.6105

0.0023

0.0023

0.0238

0.0011

0.0325

2.94E-06

0.010333824

4.22E-06

9.94E-05

6.99E-07

2.16E-05

4.49E-06

2.2429E-05

NOx (kg/mi)

4.20E-06

2.41E-05

6.34E-07

4.07E-05

7.79E-07

1.24291E-05

PM10 (kg/mi)

2.79E-07

1.09E-05

1.23E-07

2.11E-05

4.71E-07

5.52579E-06

PM2.5 (kg/mi)

1.95E-07

5.09E-06

4.66E-08

7.02E-06

0.0053

0.000885424

VOC (kg/mi)

1.10E-06

4.22E06 4.20E06 2.79E07 1.95E07 1.10E06

1.84E-05

1.57E-04

5.85E-06

9.97E-06

9.9673E-06

The data on hydroelectric power has something different from the others. As shown in Table 8, the total energy in WTP is negative. That is because, in the ideal scene, the hydroelectric power supply will proceed with carbon absorption. Thus, in WTP, all the emission factors are zero. The total energy consumption is 523408.2481J/mi. GHG-100 has 0.014350735kg/mi on average, and PM10 has 8.14912
106kg/mi.

Table 8.

EV300’s emission factors under hydroelectric power summary. WTP

Total Energy (J/ 1.83Emi) 10 GHG-100(kg/mi) CO (kg/mi) NOx (kg/mi) PM10 (kg/mi) PM2.5 (kg/mi) VOC (kg/mi)

WTW

Components Fluid

Battery

Operation only

1353953 357193.2305 16997.0482 495466.3464 1353953 0.0238 9.94E-05 2.41E-05 1.09E-05 5.09E-06 1.84E-05

0.0011 6.99E-07 6.34E-07 1.23E-07 4.66E-08 1.57E-04

0.0325 2.16E-05 4.07E-05 2.11E-05 7.02E-06 5.85E-06

ADR

AVERAGE

86295.1117 523408.2481 2.94E-06 4.49E-06 7.79E-07 4.71E-07 0.0053 9.97E-06

0.014350735 3.15332E-05 1.65451E-05 8.14912E-06 0.001328038 4.79112E-05

4 PROPORTION AND COMPARISON OF EMISSION FACTOR DATA OF MULTIPLE VEHICLE MODELS 4.1

The proportion of multi-vehicle emission factor data

From Figures 1 to 8, it can be concluded that ICEV, HEV, and PHEV-E10 mainly generate more emission factors in operation, mainly CO, accounting for 90% of the total, followed by VOC gas, accounting for 7%. PHEV-10 generates about 104 kg/mi of NOx in WTP, while PM10 and PM2.5 are almost absent.

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Figure 1.

The proportion of other emission factors of ICEV.

Figure 2.

The proportion of other HEV emission factors.

VOC produces the most emission factors in EV300 CM, nuclear CM, natural gas CM, and hydrogen CM, and less in other aspects. In EV300, coal, and natural gas, WTW generates more NOx, about 1.2*104, 2.8*104, and 1.1*104kg/mi, respectively. CO is generated more in nuclear power components and natural gas WTW and components than in nuclear power and natural gas batteries. A small amount of VOC is generated in nuclear power components, natural gas WTW, and components. In general, PM10 and PM2.5 generated by the whole life cycle of all energy sources are less.

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Figure 3.

The proportion of other emission factors of PHEV-E10.

Figure 4.

The proportion of other emission factors of EV300 CM.

Figure 5.

The proportion of other emission factors of coal-fired CM.

70

Figure 6.

The proportion of other nuclear CM emission factors.

Figure 7.

The proportion of other emission factors of natural gas CM.

Figure 8.

The proportion of other emission factors of hydrogen CM.

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4.2

Comparison of energy consumption data of four new energy vehicles

Figure 9 shows that in terms of total energy, in the vehicle fuel cycle (WTW), ICEV-E10 CM vehicles consume energy the most, EV300 CM vehicles consume the least, HEV-E10 (Ni MH) CM vehicles and PHEV-E10 CM vehicles consume the second and third most, respectively. The consumption of operation alone accounts for nearly 50% of the WTP. The energy consumption of the four models is almost distributed in an isochromatic decreasing distribution. ICEV-E10 CM is the largest, which is 4500000 J/mi. In the upstream phase of the fuel cycle (WTP), the energy consumption of the four vehicle types is similar, with EV300 CM at the most and HEV-E10 (Ni MH) CM at the least. The difference in the consumption of components is very small, even in the fuel, the EV300 CM is only 4550.6166 J/mi, and the other three types are about 70000 J/mi. However, in terms of batteries, the EV300 CM consumes nearly 500000 J/mi, far more than the 5000 J/mi consumption of the other three vehicles.

Figure 9.

4.3

Total energy comparison results of various vehicles.

Comparison of emission factor data of four new energy vehicles

Because there is a large order of magnitude difference between the emissions of GHG-100 and other emission factors in all vehicle models, the proportion of other emission factors is mainly analyzed. In Figure 10, we also compare the emissions of CO, NOx, PM10, PM2.5, and VOC. From the perspective of WTW, most ICEV-E10 CM vehicles have the first emission compared to these gas emissions, and the emissions of NOx, GHG-100, and VOC gases are far higher than those of the other three models. Only PM10 emissions are slightly lower than EV300 CM vehicles. HEV-E10 (Ni MH) CM vehicle has the lowest emissions compared with PM10 and PM2.5, and other emissions rank second. PHEV-E10 CM is relatively in the middle. Only PM2.5 emissions are the third, and others are the third. The EV300 CM vehicle emits the most PM10, the third PM2.5, and the others emit the least.

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Figure 10. models.

Comparison results of CO, NOx, PM10, PM2.5, GHG-100, and VOC of various vehicle

In the WTW, ICEV-E10 CM generates the most GHG-100, reaching 0.4 kg/mi, and the other three models are 0.3 kg/mi, 0.23 kg/mi, and 0.16 kg/mi, respectively. ICEV-E10 CM and HEV-E10 (Ni MH) CM are mainly embodied in operation, while PHEV-E10 CM and EV300 CM are mainly embodied in WTW. 4.4

Energy consumption data comparison of EV300-CM new energy vehicles

We also compared the energy consumption of EV300-CM with different energy supplies such as coal, natural gas, nuclear energy, and hydrogen energy in Figure 11. The primary energy consumption is the largest, with coal consumption of up to 400000 J/mi, followed by natural gas consumption of 300000 J/mi, and the consumption of nuclear energy and hydrogen energy is the least, with only about 140000 J/mi. Coal and natural gas account for 65% of WTP and 35% of operation. The components, fuel, cell, and operation consumption of the four energy sources are close, respectively, 357193.2305 J/mi, 16997.0482, 4954666.3464 J/mi, and 1353953 J/mi. The WTP of nuclear energy is slightly higher than that of fuel, while hydrogen energy generates energy.

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Figure 11.

4.5

EV300-CM total energy comparison results of different energy supplies.

EV300-CM new energy vehicle emission factor data comparison

Figure 12 shows that the emissions of CO, NOx, PM10, PM2.5, and GHG-100 from coal supply are the largest in the upstream phase of fuel on a year-on-year basis, while the emissions of VOC in fuel are the largest. CO, NOx, and GHG-100 of natural gas are emitted more in WTW. The upstream emissions of nuclear and hydrogen fuels are almost zero. In all stages, the VOC emissions of the four kinds of energy sources are the largest, up to 1.57*104kg/mi, and the CO emissions of components are relatively large, up to 9.9*105kg/ mi. The emissions of other gases are 10% to 25% yearly. CO, NOx, GHG-100, and VOC emissions are generally low in the battery stage. In the component phase, only CO emits more, reaching 9.9367*105kg/mi, while other gases emit less.

5 CONCLUSION According to the analysis presented in the article, the following conclusion can be reached: The carbon emissions of fuel vehicles are mainly concentrated in the operation stage, GHG100 is the largest factor in the emission factors, and the CO emissions in the whole life cycle are significantly more. For new energy vehicles, the total energy consumption is still concentrated in the WTP and operation stages, but the carbon dioxide content emitted by the WTP stage is much lower. Moreover, different battery types will also lead to different emission reduction effects. According to the conditions of the reference, iron phosphate batteries have the lowest energy consumption per 100 kilometers. Compared with traditional fuel vehicles, new energy vehicles can more effectively reduce carbon emissions in the operating stage. There is still much room for new vehicles under existing technology to further reduce carbon emissions throughout the life cycle. Therefore, a series of optimization schemes are needed to optimize the production of energy sources such as electric energy or hydrogen energy for new energy vehicles to achieve carbon reduction goals. An optimization plan can be achieved by optimizing fuel composition and the battery of new energy vehicles.

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Figure 12. Comparison results of CO, NOx, PM10, PM2.5, GHG-100 and VOC under different energy supply of EV300-CM.

Through the group analysis, it can be found that the carbon emissions of the whole life cycle of fuel vehicles are higher than those of new energy vehicles, mainly concentrated in the carbon emissions generated when burning fuel in the driving stage, so since new energy vehicles cannot completely replace the fuel vehicles on the market in a short period, it is necessary to find a way to use clean gasoline to replace traditional oil, clean gasoline is based on unleaded gasoline, and gasoline produced by adding multi-effect gasoline cleaning composite additives. Compared with ordinary unleaded gasoline, adding multi-effect composite additives enhances the ability to remove various deposits, improves the economy, and reduces exhaust pollution. New energy vehicles have reduced many carbon emissions compared to fuel vehicles, but there is still room for further reduction. The type of battery plays a crucial role, as liquid batteries, compared with solid batteries, liquid energy density is also higher, easy to store, and high safety factor. For example, if new energy vehicles can be popularized, the iron phosphate battery mentioned in the fourth part will further reduce the carbon emission problem of new energy vehicles. At this stage, the carbon emissions of new energy vehicles in the driving stage have been very low. The main carbon emissions of the whole life cycle of new energy vehicles are concentrated in generating clean energy, taking trams as an example. The way large-scale power generation can be generated today is still coal power generation, which makes the energy of new energy vehicles still produce many carbon emissions. Therefore, the focus of developing new energy vehicles today is to achieve clean power generation and clean hydrogen production in the true sense, such as photovoltaic and tidal power generation. When upstream energy achieves almost no carbon emissions, it will have a long-term and

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far-reaching positive impact on environmental protection efforts throughout the transportation industry. Only in this way can the goal of carbon peaking and carbon neutrality be truly achieved.

REFERENCES Geng W., “Total Cost of Ownership of Electric Vehicles Compared to Conventional Vehicles: A Probabilistic Analysis and Projection Across Market Segments,” Energy Policy, no. 80, pp. 196–214, 2015. Han N.N., Carbon Emission Assessment and Environmental Impact of Electric Vehicle Lifecycle, Hebei: North China Electric Power University, 2021. Li J., Life Cycle Evaluation and Analysis of Electric Vehicle and Fuel Vehicle Powertrain, Hunan: Hunan University, 2015. Nie K., Xie D.F. and Li W., “Construction and Analysis of Carbon Emission Model of New Energy Vehicle Urban Logistics,” J. Hunan Univ., vol. 42, no. 9, pp. 134–140, 2015. Shi X.Q., Li X.N. and Yang J.X., “Carbon Emission Reduction Potential and Influencing Factors of Low Carbon Transportation Electric Vehicles,” Environ. Sci., vol.34, no. 1, pp. 385–394, 2013. Shi X.Q., Sun Z.X., Li X.N., Li J.X. and Yang J.X., “Comparative Study on Life Cycle Environmental Impacts of Electric Taxis and Fuel Taxis in Beijing,” Environ. Sci., vol. 36, no. 3, pp. 1105–1116, 2015. Tong X.B., “Internal Combustion Engine is still the main Force of Energy Saving and Emission Reduction in Transportation Field. China Energy Journal, 2017-11-20(003). Wang Z.H., “Comparative Study on Carbon Emission of Light Gasoline Vehicle and Pure Electric Vehicle,” J. Auto. Eng., vol. 12, no. 4, pp. 495–505, 2022. Yan J.H., “Energy Consumption and Gas Emission Analysis and Environmental Impact Assessment of Electric Vehicle,” J. South China Univ. Technol., vol. 46, no. 6, pp. 137–144, 2018. Yan K., “Comparative Study on Life Cycle Benefits of Pure Electric Vehicles and Traditional Vehicles,” J. Wuhan Univ. Technol., vol. 45, no. 1, pp. 177–181, 2021. Zhang L., Liu Z.F. and Wang J.J., “Comparative Analysis of Life Cycle Environmental Impacts of Electric and Internal Combustion Engine Vehicles,” Chin. J. Environ. Sci., vol. 33, no. 3, pp. 931–940, 2013.

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Advances in Traffic Transportation and Civil Architecture – Liu, Qi & Law (eds) © 2023 copyright the Author(s), ISBN 978-1-032-51438-3

A model for shared parking optimization for supply and demand matching considering overtime parking Danhua Fu* Zhejiang Scientific Research Institute of Transport, Hangzhou, China

Xiangdi Li Zhejiang Communications Construction Group CO., LTD., Hangzhou, China

Mengke Zhang & Fan Zhang Zhejiang Scientific Research Institute of Transport, Hangzhou, China

ABSTRACT: Shared parking is a way to alleviate the parking conflict. Reservation in advance can reduce travel time and avoid parking space vacancy costs of the operation platform, thereby improving the utilization rate of parking spaces. In practice, demand uncertainty still occurs throughout the reservation, such as overtime parking. The operation platform has to reserve emergency parking spaces for uncertain demand risks. Therefore, this paper proposes a two-stage stochastic programming model aiming to maximize the operation platform’s expected profit. The analysis results show that the model can effectively alleviate the adverse impact of uncertainty of overtime parking. The model gives a theoretical reference for decision management of the operation platform.

1 INTRODUCTION With the increase in car ownership, the parking problem has gradually become a common problem in major cities, seriously affecting people’s daily travel. Shared parking mode can alleviate parking conflict effectively through using spare time of private parking spaces, which has been implemented in cities like Beijing, Guangzhou, and Hangzhou. To play the role of sharing theory inefficient resource allocation, researchers have conducted extensive research about shared parking on the operation mode and resource planning. Shao et al. established a shared parking spaces allocation model that comprehensively considered time window constraints and potential loss of rejecting users’ parking requests, aiming to maximize the operating platform’s long-term profit. The superiority of the model was validated by comparing it with the first-reserve-first-served mode (Shao et al. 2016). Sun et al. established a demand-driven model for parking space renting and allocation, which balanced supply and demand in theory by regulating supply dynamically (Sun et al. 2020). In addition, some researchers considered demand uncertainty (Zhang & Mu 2019). For example, Jiang et al. analyzed the relationship between the vacancy duration of parking spaces and the occurrence probability of uncertainty, including overtime parking (Jiang & Fan 2020). In practice, the shared parking system comprises an operation platform, a provider of shared parking space, and a parking user. Overtime parking of one user may occupy the parking time of the following user, despite the fact that the latter should have had the right

*

Corresponding Author: [email protected]

DOI: 10.1201/9781003402220-9

77

because he had made a reservation in advance. The operation platform should plan to avoid this case and reduce the cost of direct compensation and potential penalty caused by a breach of promise. Reserving extra parking spaces is one of the useful methods (Guo et al. 2016). Therefore, this paper proposes a demand-driven model to decide on renting and allocating parking spaces. The model is practical and robust because extra parking spaces are rented to respond to predicted overtime parking risk when deciding to respond to requests. From the perspective of practical operation, the model can maximize the expected profit of the operation platform and guarantee user benefits.

2 PARKING SPACES PROGRAMMING This paper establishes a model based on the theory of stochastic programming with compensation. In this model, stochastic factors are the occurrence possibility and duration of overtime parking, of which the probability distributions are assumed to be known (Lu et al. 2018). Decision variables belong to two stages, respectively. In the first stage, the operation platform programs, which requests are accepted based on global demand information. The operation platform decides the number of reserved parking spaces in the second stage. The model is based on the following assumptions: A1: Assume the parking space owners only share parking spaces in their spare time, and they will return to park punctually. A2: Assume the time windows of parking spaces share continuously. A3: Assume the users are unbiased for parking spaces. A4: Assume the reservation is not on first-reserve-first-served but global planning. 2.1

Definitions of parameters and variables

Firstly, operating time is divided into T intervals. N denotes the number of shared parking spaces, of which available time equals T. I denotes parking requests set, including the duration timestamps of arrival tstart and departure tend denotes the parking duration, which i i . ti start equals tend  t þ 1. b denotes whether request i parks during t, as the following equation: it i i   t  tend 1; tstart i i ; t 2 T; i 2 I (1) bit ¼ 0; t > tend or t < tstart i i tover denotes the overtime duration of request i, which follows an exponential distribution. T i denotes the operating time of reserved parking spaces, whose open time equals that of shared parking spaces but the close time is later. cit denotes whether the request i parks during t; ðt; 2 T ; Þ. cit; ¼ 1 means that request i parks. Otherwise, cit; ¼ 0. Then, the actual parking time ki : ki ¼

T; X

cit; ; i 2 I ; t; 2 T ;

(2)

t; ¼1

rnt denotes whether provider n returns and parks during t. rnt; ¼ 1 means that provider n parks. Otherwise, rnt ¼ 0. At the first stage, decision variable xi denotes whether accept request i. If accepted, xi ¼ 1. Otherwise, xi ¼ 0. In the second stage, decision variable zs denotes the number of reserved parking spaces in the case of scene sðs 2 S Þ. ws denotes the occurrence possibility of scene s. Other parameters are defined in the following Table 1.

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Table 1.

2.2

Definitions of parameters.

Parameter

Definition

p1 p2 p3 p4 s1

The purchase price per shared parking space The purchase price per reserved parking space On-time parking fee per interval Over-time parking fee per interval Compensation cost of rejecting parking users per interval

Two-stage stochastic programming model

The model aims to minimize the operation platform’s expected cost, comprehensively considering revenue, cost, and user benefit. The decision variables of the first stage are inputted into the second stage. The optimal solution of the second stage feeds back to the first stage. The model at the first stage is shown as follows: minf ¼ N  p1  p3 

I X

xi  tduration þ s1  i

i¼1

I X

tduration  ð1  xi Þ þ Qðxi Þ i

(3)

i¼1

s:t: xi 2 f0; 1g; i ¼ 1; 2    I I X

xi  bit  N; t ¼ 1; 2;    T

(4) (5)

i¼1

In the objective formula (3), the first term represents the cost of purchasing the shared parking spaces; the second term represents the revenue for serving the parking users; the third term represents the compensation cost for rejecting parking users; the fourth term as shown in formula (6). In the constraint set, constraint (4) implies xi is a binary decision variable; constraint (5) guarantees that each shared parking space can accommodate only one car per interval. The model in the second stage is shown as follows: ! S I X X  s  s s duration Qðxi Þ ¼ min w  p2  z  p4  xi  ki  ti (6) s¼1

i¼1

s:t: 0  zs  Z; I X i¼1

xi  csit; þ

N X

s2S

rnt;  N þ zs ; t; 2 T ; ; s 2 S; i 2 I ; n 2 N

(7) (8)

n¼1

In the objective formula (6), the first term represents the cost of reserving parking spaces; the second represents the revenue generated from overtime parking. In the constraint set, constraint (7) implies the number of reserved parking spaces can’t exceed Z; constraint (8) implies the number of parking users and shared parking spaces providers can’t exceed that of shared parking spaces and reserved parking spaces per interval.

79

3 MODEL COMPARISON AND EVALUATION The research focuses on how many emergency parking spaces should be reserved to cope with overtime parking risk. Therefore, a control experiment is designed to compare the proposed model with another model which doesn’t consider reserved parking spaces. In the contrast model, the objective formulas and constraints at the first stage are the same as formulas (3) to (5) but different in the second stage, as follows: Qðxi Þ ¼ min

S X

ws  p4 

s¼1

þ p3

I X

I X

I  X    þ s2 asi  kis  tduration xi  asi þ s3  ys i

i¼1

i¼1

  tduration  xi  asi i

(9)

i¼1

s:t: asi 2 f0; 1g; s 2 S; i 2 I

(10)

asi  xi ; s 2 S; i 2 I

(11)

0  ys ¼

I X

asi  di  N; s 2 S; i 2 I

(12)

asi  csit;  N; t; 2 T ; ; s 2 S; i 2 I

(13)

i¼1 I X i¼1

In the objective formula (9), the first term represents the revenue generated from overtime parking; the second term represents the compensation cost for breach of parking reservation; the third term represents the compensation cost for breach of parking space tenancy. The fourth term represents the revenue generated from on-time parking. The decision variables at the second stage are defined to determine whether the providers of shared parking spaces and the requests accepted at the first stage can be served as planned. Let asi denotes whether request i can be served under scene s. asi ¼ 1 means it can, otherwise asi ¼ 0 and a cost s2 should be compensated. Let ys denotes the number of providers with no space to park under scene s, where a cost s3 should be compensated. Model evaluation indexes of interest to the operation platform include profit, the acceptance rate of parking requests, and the real service rate. The profit P is negative to the objective cost given in function (3). P ¼ minf

(14)

The acceptance rate b1 of parking requests after final parking allocation is shown as follows. PI b1 ¼

i¼1

I

80

xi

(15)

The real service rate b2 represents the proportion of users who can park as planned, including travelers and owners of parking spaces. b2 can more truly evaluate the user benefits than b1 . PI b2 ¼

i¼1

xi 

PS s¼1

P P ws  Ii¼1 asi þ N  Ss¼1 ws  ys PI i¼1 xi þ N

(16)

4 NUMERICAL EXPERIMENTS In MATLAB, a numerical example is designed to simulate and analyze the proposed model. The operating times of shared and reserved parking spaces are 8 hours and 15 hours, respectively. Accordingly, T ¼ 32 and T ; ¼ 60. Suppose p1 ¼ 3, p2 ¼ 100, p3 ¼ 5 and p4 ¼ 10. Let I ¼ 500. Suppose arrival time follows a Poisson distribution and on-time parking duration follows an exponential distribution. Two possibilities of overtime parking occurrence (high and low possibilities are defined as 0.5 and 0.3) and two possibilities of overtime parking duration (long and short average duration are defined as 3h and 1h) are permuted and combined as four overtime scenes where occurrence possibilities are supposed to 0.25 evenly. To conduct sensitivity analysis, N is varied from 0 to 300. Suppose Z ¼ 500. Because the two models both consider s1 , let s1 ¼ 0: Let s2 ¼ 50; s3 ¼ 50 (Guo et al. 2016). As shown in Figures 1 and 2, the profit and user benefit of the proposed model are significantly superior. From the point of profit, more parking spaces supply means more parking demands can be served, while more overtime parking may occur concomitantly. Therefore, there is a turning point in the profit curve where the reserved cost is about to grow faster than the operating profit. Nonetheless, it is better than the potential long-term cost due to losing users’ trust. The proposed model can maximize operating profit while guaranteeing users’ benefits.

Figure 1.

Comparison of profits of the two models.

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Figure 2.

Comparison of user benefits of the two models.

The operation platform’s decision is based on the expectation of the cost of overtime parking. The solution depends on not only demand and supply but also parameter values. Figure 3 shows the relationship between overtime parking fee p4 with objective benefits. With the increase of p4 , the operation platform tends to accept more requests. The increasing direct revenue encourages the operation platform to actively add emergency capacity (reserved parking spaces) for responding to overtime parking risk. However, the fee directly affects the parking positivity of travelers. Therefore, the value should be determined according to price-demand sensitivity analysis and price policy.

Figure 3. Impact analysis of overtime parking fee on profit, parking space reserving rate, and acceptance rate.

In Figure 4, for each combination of demand or supply, the contours of profit are plotted in the two-dimensional space. From the figure, an optimal ratio ofZ=I can be found. This ratio is useful because it shows the number of reserved parking spaces the operation platform should purchase for a given or predicted number of parking requests.

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Figure 4.

Profit contours as shared parking spaces supply and demand change.

5 CONCLUSIONS Shared parking effectively improves the utilization rate of existing parking resources and alleviates parking conflicts. In practice, the uncertainty caused by overtime parking will increase costs and damage user benefits. This paper proposes a two-stage stochastic programming model considering the randomness of overtime parking. Optimizing the number of reserved parking spaces and deciding which requests are accepted minimizes the operation platform’s expected cost. A numerical example is designed and analyzed. The results show that the model can improve profitability while guaranteeing user benefits. The model gives a theoretical reference for decision management of the operation platform.

REFERENCES Guo W., Zhang Y., Xu M. and Zuo L. (2016) Parking Spaces Repurchase Strategy Design via Simulation Optimization. Journal of Intelligent Transportation Systems. Commun., 20(3): 255–269. Jiang B. W. and Fan Z. P. (2020) Optimal Allocation of Shared Parking Slots Considering Parking Unpunctuality Under a Platform-based Management Approach. Transportation Research Part E: Logistics and Transportation Review. Commun., 142: 102062. Lu M. S., Chen Z. H. and Shen S. Q. (2018) Optimizing the Profitability and Quality of Service in Carshare Systems Under Demand Uncertainty. M&Som-Manufacturing & Service Operations Management. Commun., 20(2): 162–180. Shao C., Yang H., Zhang Y., Ke J. (2016). A Simple Reservation and Allocation Model of Shared Parking Lots. Transportation Research Part C: Emerging Technologies. Commun., 71: 303–312. Sun H.J, Fu D.H., Lv Y., Heng Y.M., Guan T.C. (2020) Parking Spaces Renting and Allocation Model for Shared Parking. Journal of Transportation Systems Engineering and Information Technology, Commun., 20(3):7. Zhang L.F., Mu Y.P. (2018) Parking Space Allocation with Uncertain Demand and Supply Consideration. In: 15th International Conference on Service Systems and Service Management (ICSSSM). pp. 1–5.

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Advances in Traffic Transportation and Civil Architecture – Liu, Qi & Law (eds) © 2023 copyright the Author(s), ISBN 978-1-032-51438-3

Application of traffic simulation in urban expressway traffic organization design Cong Liu* College of Civil Engineering, Nanjing Tech University, Nanjing, China

Fangqing Zhang & Wei Chen China Design Group Co., Ltd., Nanjing, China

ABSTRACT: In response to the great impact of road occupation and the long construction period of urban expressway construction, traffic simulation technology was adopted to simulate the traffic organization scheme. The causes of congestion were analyzed according to the simulation results, and optimization suggestions were put forward to support the feasibility of the traffic organization scheme. First, the VISSIM traffic simulation software was used to build the simulation model of the urban expressway traffic organization scheme; second, traffic simulation was carried out according to the simulation model to analyze the causes of poor service levels on some road sections and put forward optimization suggestions; finally, the traffic organization scheme was optimized according to the optimization suggestions, and the schemes before and after optimization were compared to verify the feasibility of the scheme and provides a guarantee for the efficient operation of traffic during urban expressway construction.

1 INTRODUCTION With the substantial rise in motor vehicles in cities, urban roads face the pressure of rapid transformation. As an important traffic infrastructure, large-scale urban expressway construction comprises a large road area, a long construction period, dense traffic flow, and difficult quantitative evaluation. (Zhang 2018). Moreover, it brings great pressure on the traffic system in and around the construction scope. Therefore, during the reconstruction of the urban expressway, the traffic organization design is particularly important (Zhan 2018). However, traditional traffic organization design usually adopts static and qualitative design methods. The design scheme needs to be more effectively verified. In contrast, the traffic simulation technology can dynamically and quantitatively analyze the traffic organization scheme and, at this moment, optimize the traffic organization design scheme and ensure efficient traffic operation during urban road construction (Jia 2020). Based on the reconstruction project of Hongyun Avenue Expressway in Nanjing, this paper used VISSIM to establish a traffic simulation model. It simulated according to the traffic organization scheme to dynamically analyze the traffic operation during the construction period, to provide quantitative analysis results and optimization suggestions for the design scheme.

*

Corresponding Author: [email protected]

84

DOI: 10.1201/9781003402220-10

2 OVERVIEW OF THE HONGYUN AVENUE EXPRESSWAY RECONSTRUCTION PROJECT 2.1

Project overview

Located in front of the South Square of Nanjingnan Railway Station, the Hongyun Avenue Expressway Reconstruction Project is an important section of Nanjing’s 1.5 Ring FastConnection New City Fast Road. The project is mainly in the form of a tunnel, which is arranged along the east-west direction of Hongyun Avenue, under-crossing the ramp bridge of the existing drop-off platform on the west side, over-crossing the running tunnel of Metro Line 3, under-crossing the ramp bridge of the existing drop-off platform on the east side, and then grounding (Figure 1). The total length of the tunnel is 1,400m, including a 965m buried section and 435m open section. The tunnel adopted “six main lanes and four auxiliary lanes” in the design, with the main lanes functioning as an urban express road and first-class highway, with a design speed of 80km/h. The auxiliary lanes are functioned as urban trunk roads, with a design speed of 50km/h.

Figure 1.

2.2

Hongyun avenue expressway reconstruction project.

Traffic organization scheme

Limited by the over-crossing and under-crossing construction, the tunnel of Hongyun Avenue expressway had a long construction period, and the construction was affected by many factors. Traffic-keeping was an important issue during the construction. According to the construction organization plan, the traffic organization during the construction period was divided into three stages. The first stage was to set up construction fences on both sides of the road for pipeline relocation and road reconstruction of each road section, and the existing road in the middle was used for traffic-keeping design. The second stage was mainly to construct tunnels and pedestrian underpasses. Construction fences were set in the middle of the road, and the two sides of the reconstructed ground roads were used for traffic-keeping design. The third stage was recovery construction, including median strips, side dividing strips, and ground auxiliary lanes, and the completed tunnel was used for traffic-keeping design. This paper will discuss the first-stage traffic organization.

3 SETUP OF TRAFFIC SIMULATION 3.1

Contents of traffic simulation

According to the contents of the first-stage traffic organization scheme during the construction of the Hongyun Avenue expressway, the simulation mainly focused on verifying the feasibility of the existing traffic-keeping design under the condition that the construction enclosure limits the road traffic capacity at the initial stage of construction (Figure 2). The key areas concerned in the traffic simulation include the lanes on the east and west sides of

85

Meixiang Road and Mingcheng Avenue. Since the existing road was used to keep the traffic in the first stage, only the simulation models for the traffic-keeping roads were built, and the enclosed area was outside the scope of traffic simulation.

Figure 2.

3.2

First-stage traffic organization scheme.

Traffic simulation process

According to the characteristics of the urban expressway project, the process of traffic simulation consists of four steps: simulation model building, simulation parameter setting, simulation analysis, and scheme optimization verification (Figure 3). 1) Building simulation models, including road, bridge, tunnel, and related channelization models, according to the traffic organization scheme. 2) Adding traffic volume and analysis parameters, including traffic flow, traffic flow ratio, road priority at intersections, traffic flow path direction, information control layout and timing, design speed, and simulation time to the simulation models. 3) Simulating the traffic organization scheme based on the above settings and analyzing the simulation results, including the vehicle direction, maximum queue length, vehicle delay time, and average parking times (Zhang 2019). 4) Optimizing the schemes for congestion sections according to the simulation results and re-simulating the optimized scheme until the traffic needs are met.

Figure 3.

Flow chart of traffic simulation analysis.

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4 SETUP OF TRAFFIC SIMULATION PARAMETERS 4.1

The setting of traffic volume

considering that at the early stage of the first stage of construction, when social vehicles were not fully adapted to the influence of the construction environment, the traffic flow simulation was carried out with the traffic data of the survey on the current situation. The traffic volume data at each intersection is shown in Table 1. Table 1.

Traffic volume parameters at each intersection in the first stage (per hour). East Entrance

West Entrance

Intersection

Straight

Right

Left

Total

Straight

Right

Meixiang

721

89

41

851

924

191

Left 50

411

Road

South Entrance

North Entrance

Total

Straight

Right

Left

Total

Straight

Right

Left

Total

1576

5

10

20

35

20

321

90

431

Uturn

Nonghua

564

188

/

752

1024

/

/

1024

/

/

/

/

/

287

/

287

767

188

/

955

916

/

108

1024

/

/

/

/

/

108

317

525

661

295

/

956

1127

/

108

1233

/

/

/

/

/

294

187

481

610

194

115

919

724

378

212

1254

734

93

216

1043

619

130

173

910

/

/

/

/

/

191

799

990

232

/

105

337

56

814

/

910

Road Lvdu Avenue Jiangnan Road Mingcheng Avenue Minsheng Road

4.2

The setting of intersection parameters

According to the contents of the traffic organization scheme, the traffic signal phases at the intersections of Meixiang Road, Nonghua Road, Lvdu Avenue, Jiangnan Road, Mingcheng Avenue, and Minsheng Road were set, respectively. 4.3

Traffic simulation evaluation standard

Urban expressways usually measure the traffic service level of the road section mainly by the saturation V/C (traffic volume/design capacity of the road section), followed by the speed or delay (Guo 2019). In practice, the service level of the road section is mainly measured by V/ C, and that of the road intersection is mainly evaluated by the delay time. Currently, the following standards are mainly used for traffic simulation evaluation, as shown in Table 2 (Zhao & Zhang 2019). Table 2.

Traffic service level evaluation table.

Service Level

V/C

Delay time (s)

A B C D

 10 0.4–0.6 0.6–0.75 0.75–0.9

 10 11–20 21–35 36–55

E

0.9–1.0

56–80

F

 1.0

 80

Situation Smooth traffic flow, basically without delay Stable traffic flow, with a slight delay Stable traffic flow, with a certain delay Nearly unstable traffic flow, with greater delay, and the driver can tolerate it. Unstable traffic flow, with traffic congestion, great delay, and the driver can not tolerate it. Forced traffic flow, with serious traffic congestion and vehicles stopping occasionally.

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Considering the road’s service level and utilization benefit, this traffic simulation specifies that the simulation results below Level E can meet the traffic requirements.

5 TRAFFIC SIMULATION ANALYSIS AND OPTIMIZATION 5.1

Analysis of traffic simulation results

Based on the above models, all lanes at the intersection of Hongyun Avenue Expressway were simulated. The weighted average value of all intersections was taken as the simulation results. The simulation results from Table 3 show that the intersections of Nonghua Road, Lvdu Avenue, Jiangnan Road, and Minsheng Road traffic-keeping section in the first stage can achieve level B service level, with stable traffic flow, smooth traffic, and good service level; the intersection with the lowest service level is Meixiang Road and Mingcheng Avenue intersection, with Level D traffic service level. The operation condition is close to the unstable traffic flow, the vehicle speed is greatly affected, barely maintaining the required speed, and the driving performance and comfort are poor. In particular, the through lane on the east side of Mingcheng Avenue has a maximum queue length of 233.2 meters.

Table 3.

Data sheet of first-stage simulation results.

Intersection

Average Maximum Queue Length (m)

Average Vehicle Delay (s)

Average Number of Stops

Service Level

Meixiang Road Nonghua Road Lvdu Avenue Jiangnan Road Mingcheng Avenue Minsheng Road

185 65.8 80.3 61.9 233.2 94.8

48.3 12.22 13.01 10.83 39.6 19.5

2.07 0.69 0.58 0.44 0.84 0.88

D B B B D B

The problem data (Table 4) are screened based on the traffic flow data table at the intersection of Mingcheng Avenue. After analyzing the data, it is concluded that the possible reason is that there is no signal control in the north-south right-turn lane of Minsheng Road to the east side of Mingcheng Avenue, resulting in a large number of vehicles from Minsheng Road stopping at the through lane on the east of Mingcheng Avenue. Therefore, it is considered to set up signal control on the right-turn lane on the north side of Minsheng Road to relieve the traffic pressure of the through lane on the east of Mingcheng Avenue.

Table 4.

First-stage vehicle flow analysis data of mingcheng avenue intersection.

S/N

Direction

Maximum queue length (m)

Vehicle delay (s)

Number of stops (average)

1 2 3

Straight west Straight east Turn left to the east

207.7 233.2 53.3

31.4 51.2 49.1

0.79 1.33 1.68

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5.2

Verification of optimized traffic organization scheme

The optimized traffic simulation model was re-stimulated and analyzed according to the above optimization measures. The results show that the optimization measures can effectively alleviate the long queue length at the intersection of the through lane on the east of Mingcheng Avenue, and the impact on the lanes and intersection on the west of Mingcheng Avenue can be ignored. The traffic flow analysis data table of the Mingcheng Avenue intersection after re-stimulation and analysis is shown in Table 5. Table 5.

First-stage vehicle flow analysis data of mingcheng avenue intersection after optimization.

S/N

Direction

Maximum queue length (m)

Vehicle delay (s)

Number of stops (average)

1 2 3

Through the lane on the west Through the lane on the east Left-turn on the east

107.9 58.2 38.5

35.4 19.9 48.7

1 0.57 0.94

According to the optimized analysis results, during the analysis period in the peak traffic flow period, the maximum queue lane at the intersection of Mingcheng Avenue after optimization is the through Lane on the west of Hongyun Avenue, with a queue length of 107.9 meters; the lane with the longest delay is the left-turn lane on the east of Mingcheng Avenue, with a vehicle delay time of 48.7 seconds. The lane with the maximum number of stops is the through lane west of Mingcheng Avenue. Each vehicle stops once on average before passing the intersection; the overall service level of the road has changed from Grade D to Grade C, and the traffic capacity has been improved. The comparison of the analysis results of Mingcheng Avenue and Minsheng Road before and after optimization is shown in Table 6. Table 6. road.

Comparison of results before and after optimization of mingcheng avenue and minsheng

Before/After Maximum Queue Intersection Optimization Length (m)/Lane Mingcheng Avenue

Before After

Minsheng Road

Before After

233.2/Through lane on the east 107.9/Left turn on the east 94.8/Left turn on the west 94.8/Left turn on the west

Vehicle Delay (s)/ Lane

Number of Stops/ Lane

Service Level

51.2/Left turn on the east 48.7/Through lane on the west 48.5/Left turn on the south 28.7/Left turn on the south

1.68/Left turn on the east 1.00/Through lane on the west 1.16/Left turn on the west 0.92/Left turn on the south

D C B B

6 CONCLUSION The traffic organization scheme design during the construction period plays an important role in effectively reducing the impact of expressway construction on urban traffic. This paper creatively applied the traffic simulation technology in the traffic organization design, dynamically and quantitatively analyzed the traffic organization scheme during urban

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expressway construction, and optimized the traffic organization scheme according to the analysis results to ensure the efficient operation of urban traffic during the construction period.

ACKNOWLEDGMENTS This work was supported by Jiangsu Provincial Double-Innovation Doctor Program (JSSCBS20210394).

REFERENCES Guo G.H. “Identification of the Ground Road Traffic Status,” [C] The 24th Cross Straits Symposium on Urban Traffic, pp. 452–464, 2019. Jia J.F. “Discussion on Traffic Organization and Management Measures during Construction of Urban Rail Transit Stations,” [J]. TranspoWorld, vol. 08, No. 530, pp. 167–168, 2020 Zhan Y. “Study on Traffic Organization Method During Urban Road Rapid Transformation,” [D] Southeast University, 2018. Zhang X. “The Study on Traffic Organization Scheme of Reconstruction and Extension Project of Provincial Highway HongYong line,” [D] Chang’an University, 2018. Zhang L.Y. “Research on Waiting Area and Signal Control Method of Roundabout,” [D] Harbin Institute of Technology, 2019. Zhao A.L., Zhang J.X. “Traffic Safety Simulation Evaluation and Safety of Typical Road Sections in Chongqing,” [J]. Highway Engineering, vol. 44, No. 197(04), pp. 124–129, 2019.

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Advances in Traffic Transportation and Civil Architecture – Liu, Qi & Law (eds) © 2023 copyright the Author(s), ISBN 978-1-032-51438-3

Multi-location inventory optimization model in supply chain environment Fei Meng* Faculty of Social Sciences and Law, University of Bristol, Bristol, UK

ABSTRACT: Reflection in the backdrop of global manufacturing and commercial aggregation is the significance of inventory control. It is not only a vital component of supply chain management but also one of how a value-added supply chain can be realized. Consequently, the issue of inventory control optimization is gaining attention, and the issue of inventory control management is undergoing new research and facing new challenges. This paper establishes a centrally controlled multi-location inventory ordering and allocation model based on the inventory control model, combined with the concept and research content of a multi-location inventory system, based on the knowledge of supply chain management theory and the related methods of inventory control optimization. This study employs the genetic annealing evolutionary algorithm with the global search capability of the genetic algorithm and the local search capability of the simulated annealing algorithm to solve the ordering and allocation models in the multi-location inventory model to obtain the optimal ordering quantity and the optimal allocation quantity, thereby achieving the minimum total cost of multi-location inventory. In this study, the model-solving process is implemented through MATLAB programming, and the studied problem is solved and validated by using arithmetic examples to derive the optimal order quantity and optimal allocation quantity under the ordering and allocation models, respectively, to validate the efficacy of the centrally controlled distributed inventory system.

1 INTRODUCTION Supply chain inventory is the key to addressing market volatility, with inventory costs comprising 30% of total supply chain expenses. Consequently, it is of great practical importance to investigate the distributed inventory control problem in a supply chain environment based on supply chain inventory to optimize the management concept and satisfy the variable market demand aspect. Rudi provides a two-inventory model resolution approach for the distributed inventory problem (Rudi et al. 2001), and Robinson extends it to an n-inventory model resolution approach that computes the approximate optimal decision using a large knotted planning problem (Robinson 1990). Kukreja and Miller, 2001, found the optimal inventory holding level at each inventory point in a distributed inventory team model. The environment of the multi-location inventory decision problem with multiple inventory points employs a one-toone ordering strategy, which is more frequently applied to genetic algorithms, forbidden search algorithms, nearest point search, and enhanced TSP algorithms employing hypothesis ranking (Young et al. 2007; Luo & Wang, 2005; Nonas & Johnston 2006). Zeng and Han, 2010, proposed that the core concept of centralized control and unified management was *

Corresponding Author: [email protected]

DOI: 10.1201/9781003402220-11

91

proposed by establishing a virtual coordination center, and the model was simulated using the Analogic modeling tool to determine the optimal inventory strategy for the system. Xia proposed an Intranet-based architecture for managing distributed inventory to achieve effective management, and rapid and accurate retrieval of distributed inventory information, and presented a mathematical model for determining the optimal inventory quantity of a distributed warehouse (Xia 2013). Previous research on distributed inventory is mostly focused on the study of distributed inventory allocation strategy, and the literature rarely focuses on the ordering strategy and allocation strategy in distributed inventory. In this paper, the strategy of a centrally-controlled distributed inventory system with a coordination center is investigated. Under the condition that the warehouse demand is uncertain and the suppliers have lead time, a centrally-controlled distributed inventory ordering and dispatching model is developed, and the ordering and dispatching strategies in distributed inventory are investigated separately.

2 MODEL BUILDING 2.1

Model underlying assumptions

The model assumes a supplier, a centralized inventory coordination center, and an Nwarehouse distributed inventory system. The supplier is responsible for the supply of goods from the warehouses, while the inventory coordination center at the headquarters controls and manages the warehouses centrally, determining orders or allocations. The following assumptions apply to a specific type of product.

2.2

Definition of symbols

The following definitions are given for the symbols that appear in the model as follows, where (k=1, 2, 3, ..., N). Ikt —The opening inventory of this good at warehouse k at time t Vk —The maximum storage capacity of warehouse k (unit: pieces) Gt —Supplier’s maximum supply capacity at time t (in pieces) Lk —Supplier to warehouse k delivery lead time (unit: days) Dkt —The demand for goods at warehouse k in period t, follows a normal distribution of N(tmk ts2k ) Pkt ðxÞ—Warehouse k is demanded in period t as a probability distribution of x. The probability density function is set to f ðxÞ Qkt —Warehouse k’s order quantity to suppliers in period t Qkjt —Transfers from warehouse k to warehouse j at time t (positive for transfers into warehouse k, negative for transfers out) SSk —Safety stock of warehouse k in period t Rkt —The order point of warehouse k in period t Cpk —When ordering centrally from a supplier, the unit cost of purchase of that good Cik —Warehouse k business processing costs for each processing of goods into the warehouse Cok —Warehouse k business processing costs for each processing of goods out of the warehouse Cckt —Transaction costs of goods at warehouse k in period t Cmkt —Warehouse k in t time unit of goods storage costs, including the goods in the warehouse occupancy funds, use costs, storage costs, and the cost of custodians required Cnkt —Out-of-stock loss cost per unit of goods when warehouse k is out of stock in time t Chkt —Labor costs incurred by warehouse k for each purchase in period t

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Cdkt —Unit transportation distance cost of a single piece of cargo at warehouse k in period t dk — Distance from supplier to warehouse k dkj — Distance from warehouse k to warehouse j CWk — Total cost of ordering from supplier at warehouse k CWJk — Total cost of warehouse k redeployment CW — Total cost of warehouse orders from suppliers CWJ— Total cost of transfers between warehouses

2.3

Model building

The distributed model is an optimization model based on inventory costs, to minimize costs and determine the safety stock, order point, and order quantity for each warehouse. The costs involved in the model mainly include ordering costs, inventory holding costs, out-ofstock loss costs, transfer transportation costs, and other costs (outbound and inbound operational costs, labor costs). The two functions that appear in the model are defined: ( dðQÞ ¼

1Q > 0 0Q  0

( ; mðQÞ ¼

1Q < 0

(1)

0Q  0

In this paper, the objective function is defined as a function of supplier orders and warehouse transfers. When ordering from suppliers is centralized, transferring between warehouses is not performed, and no warehouse transfer costs are incurred. When mutual transferring between warehouses is selected, centralized ordering from suppliers is not performed in headquarters inventory coordination, and no ordering costs are incurred. The cost models for the transfer strategy and the ordering strategy are as follows, based on the model assumptions and parameters described above. Objective function: N N N N N X X X Y X MinC ¼ dð SktÞ
CWk þ dð SktÞ
ð1  dðIkt  RktÞÞ
CWJk k¼1

k¼1

k¼1

k¼1

(2)

k¼1

The total cost of ordering strategy: C1 ¼ dðQktÞ
ðCpk
Qkt þ CcktÞ C2 ¼

ð IktþQkt

(3)

Cmkt
ðIkt þ Qkt  DktÞ
PktðDktÞdðDktÞ

(4)

Cnkt
ðDkt  Ikt  QktÞ
PktðDktÞdðDktÞ

(5)

0

C3 ¼

ð þ1 IktþQkt

C4 ¼ dðQktÞ
Qkt
Cdkt
dk

(6)

C5 ¼ dðQktÞ
ðCik þ ChktÞ

(7)

93

ð IktþQkt CWk ¼ dðQktÞ
ðCpk
Qkt þ CcktÞ þ Cmkt
ðIkt þ Qkt  DktÞ
PktðDktÞdðDktÞ 0 ð þ1 þ Cnkt
ðDkt  Ikt  QktÞ
PktðDktÞdðDktÞ þ dðQktÞ
½ðCik þ ChkÞ þ ðQkt
Cdkt
dkÞ IktþQkt

ðx  mktÞ2  1 2ts2k dx ¼ dðQktÞ
ðCpk
Qkt þ CcktÞ þ Cmkt
ðIkt þ Qkt  xÞ
pffiffiffiffiffiffiffi
e 2ptsk 0 ðx  mktÞ2 ð þ1  1 2ts2k dx þ dðQktÞ½ðCik þ ChkÞ þ ðQkt
Cdkt
dkÞ þ Cnkt
ðx  Ikt  QktÞ
pffiffiffiffiffiffiffi
e 2ptsk IktþQkt (8) ð IktþQkt

8 Qkt þ Ikt  Vk > > > > Qkt þ Ikt > Rkt < N X Constraints : Gt  Qkt > > > > k¼1 : Qkt; Ikt; Dkt > 0

(9)

Equation (1) represents the total objective function, which is the expression of the total function of the ordering model and the transfer model under the distributed inventory model, and centralized ordering and transfer are selected with the ordering point in mind. The total ordering expense of warehousePk to suppliers is and the total ordering expense of each warehouse to suppliers is CW ¼ N k¼1 CWk. The total cost of ordering specifically includes the cost of ordering, inventory holding costs, out-of-stock loss costs, order transportation costs, and other costs (inbound operational processing fees, labor costs). The specific costs are shown by Equations (2) (6). Equation (7) will be combined with Equations (2) and (6) to determine the total cost of ordering supplies for warehouse k. Equation (8) is for the centralized ordering model constraints, ordering model constraints in order: the sum of the order quantity and opening inventory of each warehouse cannot exceed its maximum storage level; the sum of the order quantity and opening inventory of each warehouse exceeds its order point; the sum of the order quantity of each warehouse cannot exceed the maximum supply capacity of the supplier. The total cost of the redeployment strategy N X

C6 ¼

½dðQkjtÞ
Qkjt
Cdkt
dkj 

(10)

j¼1j6¼k

C7 ¼

ð IktþQkt

Cmkt
ðIkt þ Qkjt  DktÞ
PktðDktÞdðDktÞ

(11)

Cnkt
ðDkt  Ikt  QkjtÞ
PktðDktÞdðDktÞ

(12)

0

C8 ¼

ð þ1 IktþQkt

C9 ¼

N X j¼1j6¼k

þ

N X

½mðQkjtÞ
Cok  þ

N X

½dðQkjtÞ
Cik  þ

j¼1j6¼k

N X

½dðQkjtÞ
Coj 

j¼1j6¼k

½dðQkjtÞ
Chkt

(13)

j¼1j6¼k

94

CWJk ¼ þ

N X

½dðQkjtÞ
Qkjt
Cdkt
dkj  þ

Cnkt
ðDkt  Ikt  QkjtÞ
PktðDktÞdðDktÞ þ

IktþQkt

þ

N X

Cmkt
ðIkt þ Qkjt  DktÞ
PktðDktÞdðDktÞ

0

j¼1j6¼k

ð þ1

ð IktþQkt

½dðQkjtÞ
Coj  þ

j¼1j6¼k

N X

N X

½mðQkjtÞ
Cok  þ

j¼1j6¼k

N X

½dðQkjtÞ
Cik

j¼1j6¼k

½dðQkjtÞ
Chkt

j¼1j6¼k

ðx  mktÞ2 ð þ1  1 2ts2k dx þ Cnkt
¼ Cmkt
ðIkt þ Qkjt  xÞ
pffiffiffiffiffiffiffi
e ðx  Ikt  QkjtÞ 2ptsk 0 IktþQkt ðx  mktÞ2  N N X X 1 2ts2k dx þ
pffiffiffiffiffiffiffi
e ½mðQkjtÞ
Cok  þ ½dðQkjtÞ
ðCik þ Coj þ ChktÞ 2ptsk j¼1j6¼k j¼1j6¼k ð IktþQkt

(14)

Constraints :

8 Ikt þ Qkjt  Vk > > > > > < 0 < ðRkt  IktÞ < Qkjt > ðIkt  RktÞ < Qkjt < 0 > > > > : Ikt; Dkt > 0

(15)

In Equation (1), the total cost of transfer from warehouse k to warehouse j is N P CWJ ¼ CWJk, and the total cost of transfer between warehouses. The total cost of k¼1

transfer includes specifically the transfer process of transportation costs, inventory holding costs, inventory out-of-stock costs, other costs (warehouse k into the inventory when the goods enter the business processing costs, warehouse k out of the inventory when the goods leave the business processing costs, and labor costs incurred by the booking process), and the four costs in turn by Equations (9) to (12). Equation (13) will be the sum of Equations (9) through (12) for the total cost of transfer between warehouses k and j. Equation (14) is for the transfer model constraints and transfer model constraints in order: when warehouse j to warehouse k transfer of inventory, warehouse k transfer of inventory, and the sum of the beginning of the inventory cannot be greater than the maximum capacity of the warehouse; when warehouse j to warehouse k transfer of inventory, warehouse k transfer of inventory and the sum of the beginning of the inventory is greater than the warehouse’s order point; when warehouse k to warehouse j transfer of inventory, the warehouse transfer of inventory left after the inventory is greater than the ordering point of the warehouse; the opening inventory and demand of each warehouse are greater than zero, and the transfer amount of each warehouse is a real number, of which the transfer to the warehouse is positive and the transfer out is negative.

3 ALGORITHM DESIGN In this paper, by analyzing the advantages and disadvantages of the genetic algorithm and simulated annealing algorithm, and according to the usability of the two algorithms, the genetic annealing algorithm is introduced by combining the two algorithms, and the distributed inventory ordering and dispatching model is solved by using the genetic simulated annealing algorithm.

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4 EXAMPLE ANALYSIS 4.1

Background of the algorithm

This thesis’s example analysis is situated within the context of a domestic automaker with a parts department at its corporate headquarters that handles spare parts for multiple models within the enterprise. The company has four parts warehouses in the four major sales regions of the country that can supply repair and service stations with parts. The parts department at the headquarters is the model’s headquarters inventory coordination center, and the four parts warehouses and the parts department at the head office comprise a distributed inventory system. Using the ordering model and allocation model, respectively, this paper verifies the efficacy of the centralized distributed control ordering and allocation strategy. The maximum supply capacity of the supplier is 500 (pieces) and the distances between the supplier and each warehouse are 300, 100, 200, and 100 kilometers, respectively (unit: km). In this section, the inventory model is solved using the genetically simulated annealing algorithm, and the order quantity when centralized ordering and the transfer quantity when transferring are finally solved based on the model’s expression and the given model data, as depicted in Figure 1.

Figure 1.

Flow chart of model solving.

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4.2

Determine order points and safety stock

According to the model solution process depicted in Figure 1 of this paper, the safety stock of each warehouse SSk and order point Rkt must be determined first. Then, through the order point of each warehouse, the use of an order or transfer strategy must be determined. In the model assumptions, the order lead time Lk of warehouse k is constant, and the demand of each warehouse follows a normal distribution Nðmk s2k Þ; Z represents the service level of each warehouse (PðZÞ standard normal distribution coefficient). pffiffiffiffiffiffi SSk ¼ Zsk Lk (16) pffiffiffiffiffiffi Rkt ¼ SSk þ Lkmk ¼ Zsk Lk þ Lkmk (17) The lead time determines that the demand follows a normal distribution of safety stock as shown by Equation (15), and the ordering point and safety stock relationship as shown by Equation (16). According to the solution equation of safety stock and the parameter setting of each warehouse in Equation (16), the safety stock of each warehouse is SS1=5, SS2=4, SS3=3, SS4=3, and the order point of each warehouse is R1t=17, R2t=10, R3t=12, R4t=16 from Equation (17). 4.3

Solving and analyzing the ordering algorithm

The initial stock of each warehouse is I 1t=10, I 2t=15, I 3t=8, I 4t=10. Since the total order points of the four warehouses are greater than the total warehouse beginning inventory, the centralized ordering strategy is selected. 4.3.1 Concentrate ordering: This paper employs a MATLAB-implemented evolutionary algorithm based on the genetic annealing process. The optimal solution is output after 100 iterations of the objective function, and Table 1 displays the order quantity for the centralized ordering scheme in the ordering model. Table 1.

The number and cost of concentrate order.

Warehouse Warehouse Warehouse Warehouse Warehouse

1 2 3 4

Order Points Rkt (pieces)

Order Volume Qkt (Pieces)

17 10 12 16

20 10 32 18

Total order quantity (pieces): 80 The total cost of centralized ordering (RMB): 2977.6

4.3.2 Disperse ordering: If according to the company’s original ordering strategy, each warehouse independently orders from suppliers according to their own needs and determines the best order quantity through their own needs (the independent ordering strategy is not calculated by genetic algorithm, and the order quantity of each warehouse is calculated by the enumeration method), the total cost of each warehouse is minimized, then, under this scheme, the order quantity and total order cost of each warehouse are optimized. Comparing the order quantity and order cost under the two strategies of centralized ordering and disperse ordering, the total order quantity of centralized ordering is 80 pieces

97

and the total cost of centralized ordering is 2,977.6 RMB; whereas the total order quantity of dispersed ordering strategy is 96 pieces and the total order cost is 3,361.8 RMB; the centralized ordering quantity is 16 pieces less than the disperse ordering quantity, and the centralized ordering cost is also lower than the disperse ordering cost. The centralized ordering strategy is efficient because it reduces surplus inventory and optimizes ordering costs.

Table 2.

The number and cost of disperse order.

Warehouse Warehouse Warehouse Warehouse Warehouse

4.4

Order Points Rkt (pieces) 1 2 3 4

17 10 12 16

Order Volume Qkt (pieces)

24 12 39 21 Total order quantity (pieces): 96 The total cost of Disperse ordering (RMB): 3361.8

Expense Ckt (RMB) 860.3 410.6 1345.8 745.1

Solving and analyzing the transfer case

According to Table 3, the opening inventories of warehouses 1 and 3 are less than their order points, but the total opening inventory of the warehouse is greater than the sum of the order points of each warehouse. Therefore, the transfer strategy is employed, and the transfer Table 3.

The date of each inventory.

Warehouse

Warehouse 1

Warehouse 2

Warehouse 3

Warehouse 4

Beginning Inventory Ikt(pieces) Order Point Rkt(pieces)

11 17

40 10

8 12

35 16

model is solved using the genetic annealing algorithm to calculate the transfer volume between the warehouses. And in this inventory condition, the centralized order quantity from suppliers and the transfer cost is first determined. Table 4 displays the distance between the warehouses and the per-unit transportation cost.

Table 4.

The distance between warehouses.

Warehouse

Warehouse2

Warehouse3

Warehouse1 700kilometers 1200kilometers Warehouse2 700kilometers Warehouse3 Unit cargo transportation costs (Cdkt): 0.01RMB/piece. Kilometers

Warehouse4 1100kilometers 900kilometers 1200kilometers

a) Transshipment After 160 iterations of the objective function, the total cost in the redeployment model is $1592.0, where warehouse 2 redeploys 19 pieces to warehouse 1, warehouse 4 redeploys

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16 pieces to warehouse 3, and warehouse 2 redeploys 5 pieces to warehouse 3, shown in Table 5. b) Concentrate ordering After 100 iterations of the objective function, the total cost of centralized ordering is $2,843.2. At this time, the order quantities of Warehouses 1 and 3 are 15 and 27 respectively, and the order quantities of Warehouses 2 and 4 are 0, as shown in Table 6. Table 5.

The relative date of inventory transshipment.

Warehouse

Warehouse2 transfer quantity

Warehouse1 19 Warehouse2 Warehouse3 Total cost of transfer (RMB): 1592.0

Table 6.

Warehouse3 transfer quantity

Warehouse4 transfer quantity

–5 16

The number and cost of concentrate order.

Order Quantity

Warehouse 1

Warehouse 2

Warehouse 3

Warehouse4

15 Total order cost (RMB):

0 2843.2

27

0

c) Here, the two strategies of centralized ordering and transfer are compared, and it can be concluded from Tables 5 and 6 that the order quantity under the centralized ordering strategy is Q1t = 15, Q2t = 0, Q3t = 27, and Q4t=0, and the total cost is 2843.2 RMB; while the transfer quantity under the transfer strategy is Q12 = 19, Q23 = -5, Q34 = 16, and the transfer strategy’s total cost is 1592.0 RMB. When the total opening inventory does not meet the order point, the transfer strategy can reduce the overall inventory level of the company, avoid the backlog of goods brought to other warehouses, and increase the enterprise’s liquidity, thereby optimizing the overall inventory. 5 CONCLUSION Based on the current status of domestic and international research on distributed inventory and inventory control theory, this paper adopts a distributed inventory control strategy to optimize the overall inventory layout for businesses with multiple inventory distributions. While considering the distributed inventory allocation model, an ordering model is also constructed, and the ordering and allocation strategies are selected based on the ordering point and opening inventory, resulting in an overall optimization model that enables effective inventory control, reduces inventory costs and increases inventory efficiency. In this paper, the centralized multi-location inventory model is studied from a theoretical perspective, but there is still a distance between the model and its actual application operation, such as the collection of large amounts of inventory information and inventory control in the actual operation to achieve cost optimization while also taking into account the optimal control of inventory time. Consequently, it is necessary to apply the model extensively in future practice, to continuously revise the model, to examine the multi-location inventory system from all angles, and to conduct additional research and analysis on the multi-location inventory system. 99

REFERENCES Luo J.W., and Wang H., “A Multi-location Inventory System with Transshipments and its Heuristic Algorithm,” Proceedings of ICSSSM’05, International Conference, Voll, 2005, pp. 278–281 Nonas L.M., Johnston K., “Optimal Solutions in the Multi-location Inventory System with Transshipments,” Springer Science + Business Media B.V,2006, pp. 47–75 Robinson L.W., “Optimal and Approximate Policies in Multi-Period, Multi-location Inventory Models with Transshipments,” Operations Research,1990, pp. 278–295 Rudi N., Kapur S., and Pyke D.F., “A Two-Location Inventory Model with Transshipment and Local Decision Making,” Management Science, 2001, pp. 1668–1680 Schmidt P.K., and Miller D., “Stocking Decisions for Low-usage Items in a Multi-location Inventory System,” Management Science 2001 INFORMS, 2001, pp. 1371–1383 Xia L.L., “Study About Distributed Inventory Management and Inventory Optimization Based on the Intranet,” Microelectronics& Computer, 2013, pp.47–50 Young H. L., Jung W. J., and Young S. J., “An Effective Lateral Transshipment Policy to Improve Service Level in the Supply Chain,” International Journal of Production Economics, 2007, pp. 215–126 Zeng L.M., Han R.Z., “Simulation of the Distributed Inventory Management System,” Computer Integrated Manufacturing Systems, 2010, pp. 1067–1072

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Advances in Traffic Transportation and Civil Architecture – Liu, Qi & Law (eds) © 2023 copyright the Author(s), ISBN 978-1-032-51438-3

Research on collaborative scheduling mechanism and method of automatic guided vehicles in complex warehouse Yingli Liu*, Minghua Hu & Jiaming Su College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, China

ABSTRACT: With the continuous development of the e-commerce industry, the surge in commodity logistics orders raises higher requirements for warehouse management. Intelligent unmanned warehouse gradually replaces traditional warehouses. The automatic guide vehicle (AGV) has become one of the important equipment for the operation of the unmanned warehouse because of its flexible cargo handling capacity, which can greatly meet the needs of cargo handling and improve the automation of unmanned warehouses. This paper studies the optimal scheduling problem of a complex unmanned warehouse carrying robot, combs the whole process of AGV optimal scheduling, considers the dispatching entities such as commodity pallets and AGV, studies the optimal scheduling problem of AGV from two perspectives of “mechanism” and “method”, designs a multi-agent cooperative AGV scheduling mechanism, and establishes a multi-constrained, multi-objective, and integrated dispatching mechanism based on this. A “commodity pallet-AGV” collaborative scheduling model with multistage decision-making is presented, and an improved heuristic algorithm is designed to solve the model.

1 INTRODUCTION Warehouse management is the effective control management of a series of activities such as the receipt, storage, delivery of goods in the warehouse, maintaining the warehouse goods, and ensuring that the daily operational activities are performed properly. The traditional warehouse management model is mostly a manual management model, and this type of management way has many problems, especially because of the “warehouse explosion” phenomenon caused by the factors of low human efficiency. With the rise of e-commerce, the unmanned warehouse has gradually become the development direction and goal of the automated warehousing logistics system. The Automated Guided Vehicle (AGV) is also known as an intelligent mobile robot. This problem is based on goods ordered in unmanned warehouses. AGV carries goods and transforms the traditional “person to person” picking mode into the “goods to person” mode (Ding et al. 2019). The earliest included Amazon’s Kiva robots, and later the AGV carrying robots, SHUTTLE rack shuttles, and DELTA sorting robots, all of which were highly automated and tailored to the unattended warehouse. The dispatch of unattended AGVs is the core issue. Among them, multi-AGV collaborative path planning technology is the key technology to achieving multi-AGV collaborative completion of handling tasks in a complex warehouse environment and maximizing the order release rate (Xiong 2021). Since the adoption of AGV for transportation

*

Corresponding Author: [email protected]

DOI: 10.1201/9781003402220-12

101

can effectively reduce workers’ work intensity, increase productivity, reduce labor costs, enhance safety, and enable orderly and efficient completion of transportation operations, related studies on scheduling issues with AGV in transportation systems have gradually risen in recent years. Typical complex depots were binarized, and the visualized depot two-dimensional planes are shown below. Where path nodes (gray): AGV is freely accessible; Storage node (green): place pallets or common shelves where AGV can reach. Generally, there is only one location into and out, i.e. a location close to a road; Retention node (yellow): retention location column node (black): obstacle, AGV not reachable; Pick station node (blue): where the picking robot exits from the conveyor belt after commodity packing, generally with multiple pallet dock to location replenishment station nodes (pink): Commodity placement sites that are replenished from a high-density area, generally delivered via conveyor belt; Empty pallet recovery node (red): the empty pallet recovery site, with only two sites in the figure. Complex UN bin handling robotic scheduling problem can be described as follows: various kinds of commodity order requirements within a certain period, goods located in individual pallets within a warehouse, pallets located in a storage location in a warehouse with multiple goods of various types stacked on each pallet, combined with the current inventory situation with the goals of maximizing AGV operational efficiency, minimizing the overall cost of AGV operation, and when meeting AGV loading constraints using conditions such as time window constraints, the AGV coordinated handling robots to accommodate order demand from carry pallets at the storage station to a nearby pick station, either by scheduling non-empty pallets at the pick station to an empty reservoir after picking is completed, or by arranging empty pallets at the pick station to pallet recovery.

Figure 1.

Diagram of the warehouse.

The AGV scheduling problem is an extension of the vehicle routing problem (VRP) and belongs to the NP-hard problem, therefore, this AGV scheduling problem on a large scale is difficult to solve using traditional accurate solution algorithms (Sun 2012). The AGV scheduling problem is far more complex than the conventional VRP problem due to the complex scenarios (warehouse storage, sorting, pallet recovery, obstacles, and the coexistence of backup reservoirs), features such as multiple constraints (loading constraints, time window constraints, etc.), multi objectives (transportation cost, number of AGV used, etc.), and uncertainties (AGV collisions, deadlocks). At present, most of the scheduling methods

102

adopted by companies are based on production experience and rules, which have problems such as low on-time delivery rate of orders and low utilization rate of AGV. Therefore, it is urgent to combine the optimization scheduling theory with the features of the shop production link and logistics link to design an efficient optimization mechanism and method for solving an unscheduled AGV scheduling problem. This paper transforms the traditional “person to cargo” picking mode to the “cargo to person” mode by handling goods via AGV based on order goods in an ungroomed area (Kumar & Kumar 2018). In the problem of global coordinating to optimize the AGV schedule, around the optimal path planning problem under multi-subject synergy, the task equilibrium area delineation problem, and the avoidance of obstacles and congestion problem, we focus on the synergistic operation mechanism and method of AGV in complex unoccupied bins and ask for the optimization method and solution algorithm to avoid collisions and jams, so the AGV can perform commodity order tasks efficiently and greatly improve the efficiency and quality of unassisted in unoccupied bins, and improve the operational efficiency as well as the economic benefits of the whole system (Sheng 2019).

2 AGV COLLABORATIVE SCHEDULING MECHANISM The coordination path planning problem of AGV is the most fundamental problem and the basis for solving subsequent problems (Le et al. 2018). The problem of pallet assignment, optimal path planning for the pallet, and AGV assignment is first addressed in the model, where the path of the optimal AGV is required to achieve optimal coordinate scheduling for the optimal policy, provided that the order requirements can be met as soon as possible. When addressing the optimal strategy for collaborative scheduling with AGV, a two-stage optimal scheduling model with multiple constraints, multi-subject cooperation, and multi-resource coupling is developed by considering the coupling of the pallet scheduling problem to achieve multi-objective integration directed portaging robot optimal scheduling to maximize operational efficiency and minimize the walking path. The established two-stage optimization model was solved using neighbor search with a simulated annealing algorithm. The coordination scheduling problem of the AGV can be understood as increasing the efficiency of the use of the AGV as much as possible, reducing free time for the AGV, reducing the total distance walked by the AGV, and completing as quickly and efficiently as possible the matching task of the commodity pallets with the order demand (Wang 2019). Based on this, the AGV co-scheduling mechanism is proposed, i.e., the scheduling by AGV is divided into two stages: In the first stage, to minimize the total distance traveled by the pallet to complete the optimal match of the commodity pallet commodity to the order demand, a pallet scheduling task set is generated; In the second stage, to optimize scheduling AGV runs and assign AGV to complete the pallet scheduling task while optimizing the AGV running Spatio-temporal network to achieve the goal of improving the efficiency of robot operation and minimizing the total length of robot running path, the scheduling mechanism is shown in Figure 2.

2.1

Phase I: Pallet assignment and optimal scheduling model

In Phase I, a pallet optimization scheduling model to minimize the total pallet run path length is established, considering the limitations on the number of goods within the pallet scheduling, and the limitations on the pallet outbound and return storage levels, the model establishment logic is shown in Figure 3.

103

Figure 2.

AGV Collaborative scheduling mechanism.

Figure 3.

Pallet assignment and optimal scheduling model construction logic.

104

Table 1.

Sets, parameters, and variables for model.

Symbol Quantity H I Q M R Ch Qi Oih Jiq Dqm Dmq Dmr M xi yi kih pim giq fir diqm dimq dimr zq

Set of the item types in order Set of the pallets Set of the storage position Set of the picking table Set of the empty pallet recovery position Order requirements for item h(h 2 H) Initial storage position for the pallet i ,Qi 2 Q The number of item h in the pallet i, i 2 I,h 2 H The initial storage position of the pallet i is q The shortest travel distance from the storage position q to the picking table m, q[Q, m 2 M The shortest travel distance from the picking table m to the storage position q,m 2 M, q[Q The shortest travel distance from the picking table m to the recovery position r,m 2 M，r 2 R Infinitely large value xi=1 if the pallet i is picked for a picking table, otherwise, xi=0, i 2 I yi=1 if all items in the pallet i are selected, otherwise, yi=0, i 2 I The number of item h in the pallet i to be picked at the picking table, i 2 I , h 2 H pim=1 if the pallet i selects the picking table m, otherwise pim=1, i 2 I ; m 2 M giq=1 if the pallet i returns to the storage position q, otherwise, giq=0, i 2 I ; q 2 Q fir=1 if the pallet i selects the empty pallet recovery position r, otherwise fir=0 i 2 I ; r 2 R The distance for pallet i travelling from the storage position q to the picking table m, i 2 I; m 2 M; q 2 Q The distance for pallet i travelling from the picking table m to the storage position q, i 2 I; m 2 M; q 2 Q The distance for pallet i travelling from the picking table m to the empty pallet recovery position r, i 2 I ; m 2 M; r 2 R zq=1 if the pallet in the storage position q is assigned, otherwise zq=0

As shown in Table 1, the symbolic variables of the pallet assignment optimal scheduling model are defined. To minimize the total distance traveled on commodity pallets, the objective function is: X min ðdiqm þ dimq þ dimr Þ (1) i2I

Constraints: 1. Constraints in matching the number of items in the pallet to the order requirements. 0  kih  Pih ; 8i 2 I ; h 2 H X kih þ M ð1  xi Þ > 0; 8i 2 I

(2) (3)

h2H

X

kih 

h2H

X

X

Pih
xi  0; 8i 2 I

(4)

h2H

kih ¼ Ch ; 8i 2 I ; h 2 H

i2I

105

(5)

Constraint (2) limits the number of item h selected from the pallet i to less than the number of items held on that pallet. Constraint (3) limits the total number of items selected from the pallet i should be greater than zero if the pallet i is picked out to the picking table. Constraint (4) limits the total number of items selected from the pallet i should be equal to zero if the pallet i is not picked out to the picking table. Constraint (5) limits the number of item h selected from the pallets that should meet the demand in the order. 2. Constraints on scheduling picking tables for each pallet. X pim  1; 8i 2 I (6) m2M

X

pim  xi ¼ 0; 8i 2 I

(7)

m2M

diqm þ Mð3  xi  pim  Jiq Þ  Dqm ; 8i 2 I ; m 2 M; q 2 Q

(8)

diqm  Mð1  xi Þ  0; 8i 2 I ; m 2 M; q 2 Q

(9)

Constraint (6) limits pallet i to a maximum of one picking table. Constraint (7) states that a pallet i must be assigned to a picking table if the pallet i is picked out for the picking table. Constraint (8) states that the length of the route of the pallet i from the storage position q to the picking table m should be greater than the shortest path (Dqm ) from the storage position q to the picking table m in case of the pallet i is picked out from the storage position q and then goes to the picking table m. Constraint (9) states that the length of the travel path of the pallet i from the storage position q to the picking table m should be zero if the pallet i is not picked out to the picking table. 3. Constraints in finding empty storage space for pallets if there are still items in the pallet. X X kih  Oih þ Mð2  xi  yi Þ  0; 8i 2 I (10) h2H

h2H

xi  yi  0; 8i 2 I X giq  1; 8i 2 I

(11) (12)

q2Q

yi þ

X

giq  xi ¼ 0; 8i 2 I

(13)

q2Q

X

X

fir  1; 8i 2 I

(14)

giq  zq  0; 8i 2 I ; q 2 Q

(15)

dimq  Mð1  xi þ yi Þ  0; 8i 2 I ; m 2 M; q 2 Q

(16)

dimq þ Mð4  xi þ yi  pim  giq  zq Þ  Dmq ; 8i 2 I ; m 2 M; q 2 Q

(17)

zq þ Jiq  xi  1; 8i 2 I ; q 2 Q

(18)

q2Q

giq þ

r2R

Constraint (10) limits the total number of items selected from the pallet i should be equal to the total number of items held in the pallet i if all items in the pallet have been selected. Constraint (11) states that if the pallet i is not picked out for the picking station,

106

there must be merchandise remaining in the pallet i. Constraint (12) restricts pallets to a maximum of one storage position after all items have been picked. Constraint (13) states that it should be allocated to a storage position if the pallet i has items remaining after picking. Constraints (14) ensure that after any pallet has been picked, it can only go to either the storage position or the empty pallet recovery position. Constraint (15) ensures that the pallet is allocated to only an empty storage position after being picked. Constraint (16) states that the length of pallet i travel from the picking table m to the empty storage position q should be equal to zero (dimq ¼ 0) if the pallet is not picked out go to the picking table or the pallet is empty. Constraints (17) state that the length of the route of the pallet i from the picking table m to the empty storage position q (dimq ) should be greater than the shortest path from the picking table m to the storage position q (Dmq ) if the pallet is picked out to the picking table and goes to the picking table which is not empty after being picked. Constraint (18) provides that if a pallet is picked, the storage position in which the pallet is located can be considered empty. 4. Constraints in finding recycling nodes for pallets if there are no items in the pallet. X

fir  1; 8i 2 I

(19)

r2R

xi 

X

fir  0; 8i 2 I

(20)

r2R

X

fir  M
yi  0; 8i 2 I

(21)

r2R

dimr þ Mð4  xi  yi  pim  fir Þ  Dmr ; 8i 2 I ; m 2 M; r 2 R

(22)

dimr  Mð2  xi  yi Þ  0; 8i 2 I ; m 2 M; r 2 R

(23)

diqm  0; dimq  0; dimr  08i 2 I ; m 2 M; q 2 Q; r 2 R

(24)

Constraint (19) ensures that the pallet i will go to at most one empty recycling position after the item has been picked. Constraint (20) states that if a pallet is not picked out for the picking table, there is no need to allocate an empty pallet recovery position to it. However, if there are any items left in the pallet after being picked, a storage position is allocated to the pallet. Constraint (21) states that if all items in a pallet are picked out, i.e. there are no items left in the pallet after picking, then an empty recovery position is allocated to it. Constraint (22) states that the length of the route of the pallet i from the picking table m to the empty pallet recovery position r (dimr ) should be greater than the shortest path from the picking table m to the empty pallet recovery position r (Dmr ) when the pallet i is picked out to the picking table m and the pallet is empty after being picked. Constraint (23) states that the length of the travel route of the pallet from the picking table m to the empty pallet recovery position r should meet dimr ¼ 0 if the pallet i is not picked out for the picking table or is not empty after being picked. Constraints (24) limit the length of the pallet travel route to a nonnegative value. 2.2

Phase II: AGV robot operation scheduling model

Based on the results of the first phase of optimization, a pallet scheduling task set e is generated, which contains three tasks related to pallet scheduling, i.e., out-of-warehouse, return, and recovery tasks. The second phase of the robot scheduling problem can be described as follows: each task in the task set e needs to be assigned to a robot, and the same robot must complete two tasks in succession considering a certain time interval. In this 107

context, the task assignment and task time of the robot are optimally scheduled to increase the efficiency of the robot and reduce the distance traveled by the robot. In the task set, as the AGV is not initially at the storage node, the task of travelling the path from the robot’s initial position to the storage node of the pallet that needs to be dispatched is added. The construction process of the second phase of the AGV scheduling model is shown in Figure 4.

Figure 4.

construction process of the AGV scheduling model.

As shown in Table 2, the symbolic variables of the AGV robot operation scheduling model are defined. If the task e is the out-of-warehouse, the time consumed for the task e is the sum of the pallet travel time and the picking table working time: Te ¼ t0 þ DisInitiale ;Ende =Vloc; 8e 2 e

(25)

minfmaxðtve þ Te Þg

(26)

The objective function:

The object of Phase II is to minimize the completion time of the last task of the AGV, which differs from Phase I where the AGV robot scheduling objective is to maximize the efficiency of the AGV and minimize the total path length of the AGV. The proof is given below to demonstrate that the objective function at this phase covers the optimization objective in Phase I, and the reasonability of the objective function at this phase is analyzed.

108

Table 2.

Sets, parameters, and variables for model.

Symbol

Quantity

e n Be

Set of the pallet schedule task Set of the AGV that can be scheduled hk The type of the task.Bck e ¼ 1 if the type of task e is out-of-warehouse, Be ¼ 1if the type ¼ 1,e 2 e of task e is return, and Bhs e Pallets ID of the task e, e 2 e Time consumption of pallets in the task e, e 2 e The picking table of the task e, and Chue ¼ 0if Bck e ¼ 0, e 2 e The initial storage position of the task e, e 2 e The storage position for returning AGV in the task e, andHkeq ¼ 0ifBhk e ¼ 0, e 2 e the recovery position of the task e, andHser ¼ 0if Bhs e ¼ 0, e 2 e The travel distance of pallets in the task e, e 2 e The initial position of the task e, e 2 e The final position of the task e, e 2 e The shortest distance for AGV travelling from the initial position to the final position in the task e, e 2 e The shortest distance for AGV travelling from the final position of the task e to the initial position of the task e0 , e; e0 2 e The default speed of AGV Infinitely large value Target weight value, which is a constant between 0 and 1, sve ¼ 1if the AGV v is assigned to perform the task e, otherwisesve ¼ 0,v 2 V; e 2 e aee0 ¼ 1if the task e is performed before the task e0 , otherwiseaee0 ¼ 0,e; e0 2 e The start time of the task e performed by the AGV v,v 2 V; e 2 e the distance for the pallet i travelling to the picking table m,i 2 I ; m 2 M

Nume Te Chue Joue Hke Hse Dise Initiale Ende DisInitiale ;Ende DisEnde ;Initiale0 Vloc M a sve aee0 tve dve

Proof 1: Under the background of the objective of minimizing the total route length of an AGV in Phase I, we assume that any AGV is assigned a total of w tasks to complete in the planning period (w  2), and the last task to be completed is ew (ew 2 e). Assume that the AGV robot starts the task ew at a time of tew , and that the AGV robot starts the last task at a time not less than the time it finishes the penultimate task, and similarly that the robot starts the penultimate task at a time not less than the time it finishes the reciprocal third task. Therefore,

te w 

w1 X

w1 P

Te i þ

DisEndi1 ;Initiali

i¼2

Vloc

i¼1

þ te1

where Tei ; Vloc are parameters and te1  0. As tew is only related to

(27) w1 P

DisEndi1 ;Initiali and te1 , as

i¼2

well as te1 is the time at which the AGV robot starts the task e1 , the (27) can be simplified to

tew 

w1 X

w1 P

Tei þ

i¼1

where

w1 P

DisEndi1 ;Initiali

i¼2

Vloc

(28)

DisEndi1 ;Initiali is the total length of the path traveled by the AGV robot to complete

i¼2

the task. As a result, we can minimize the path traveled by the AGV robot to perform the task by minimizing the start time of the last task of the AGV robot. The total path length of 109

the robot is the sum of the total path length of the pallets optimized in the first phase and the total path length of the AGV robot for the task, so minimizing the maximum of tew reduces the total path length of the robot as a whole. Proof 2: The objective of maximizing the efficiency of the AGV robot for Phase 1 can be simplified to minimize the AGV robot stand-by time, i.e., the total amount of time that the robot is not assigned a task during the planning period. We assume that any AGV is assigned a total of w tasks to complete in the planning period (w  2), and the last task to be completed is ew (ew 2 e). Assume that the AGV robot starts the task ew at a time of tew , and the start time of the last task of the robot shall be equal to the sum of the time after the penultimate task and the standby time of the robot after the penultimate task, and the start time of the penultimate task of the robot shall be equal to the sum of the time after the reciprocal third task and the standby time of the robot after the reciprocal third task. Therefore,

tew ¼

w1 X

w1 P

Tei þ

DisEndi1 ;Initiali

i¼2

þ te1 þ

Vloc

i¼1

w1 X

temptyei

(28)

i¼1

where temptyei is the standby time of the AGV robot after the task ei and Tei ; Vloc are parameters. Based on Proof 1, the (28) can be simplified to

te w 

w1 X

w1 P

Te i þ

w1 P

þ

Vloc

i¼1

As tew is only related to

DisEndi1 ;Initiali

i¼2

DisEndi1 ;Initiali and

i¼2

w1 X

temptyei

(29)

i¼1 w1 P

temptyei , minimizing the maximum tew

i¼1

reduces the overall standby time and increases the efficiency of the robots, while reducing the AGV robots’ total path length. Constraints: 1. Constraints on task assignment.

X

sve ¼ 1; 8e 2 e

(30)

v2V

aee0 þ ae0 e ¼ 1; 8e; e0 2 e; e 6¼ e0

(31)

Constraint (30) ensures that any one task e can be performed by one and only one AGV robot. Constraint (31) limits the order in which any two tasks (e and e0 ) are executed to exist one and only one. 2. Constraints on the same vehicle dispatch time interval. tve  tve0 þ Mð3  aee0  sve  sve0 Þ  Te þ DisEnde ;Initiale0 =Vloc8v 2 V; 8e; e0 2 e; e 6¼ e0 (32) tv0 e0  tve þ MðInitiale  Ende0 Þ þ MðHke þ Hse  Chue0 Þ  8v; v0 2 V; 8e; e0 2 e; e 6¼ e0 tv0 e0  tve þ MðInitiale  Ende0 Þ þ MðChue  Hke0 Þ 

(33)

DisInitiale0 ;Ende0 8v; v0 2 V; 8e; e0 2 e; e 6¼ e0 Vloc (34)

tve  M
sve  0; 8v 2 V; e 2 e 110

DisInitiale0 ;Ende0 Vloc

(35)

Constraint (32) states that in the case that both e and e0 are performed by the same AGV robot and the e is performed before the e0 , the time interval between the start of the task e0 and the start of the task e shall be greater than the sum of the minimum time between the time the pallet is in the task e and the time the AGV robot travels from its position at the end of the task e to its position at the start of the task e0 . Constraint (33) states that if the task e is returned or recovered, the task e0 is out-of-warehouse and the picking position where the task e starts is the same as the picking position of the task e0 , the task e0 should arrive at the picking position after the task e has started. Constraint (34) states that if the task e is out-of-warehouse, the task e0 is the return, and the storage position where the task e starts is the same as the storage position of the task e0 , the task e0 should arrive at the storage position after the task e has started. Constraint (35) states that tve ¼ 0 if the task e is not assigned to an AGV robot. 3. Constraints on the same pallet scheduling interval. 2 hs 0 0 Bck e þ Be0  aee0  1 þ MðNume  Nume0 Þ ; 8e; e 2 e; e 6¼ e

(36)

2 hk 0 0 Bck e þ Be0  aee0  1 þ MðNume  Nume0 Þ ; 8e; e 2 e; e 6¼ e

(37)

Constraint (36) states that if e and e0 belong to the same pallet which is out-ofwarehouse and recovery respectively, the task e must be executed before the task e0 . Constraint (36) states that if e and e0 belong to the same pallet which is out-of-warehouse and returned respectively, the task e must be executed before the task e0 . 4. Constraints on the priority of the out-of-warehouse and return tasks.   hk 0 0 aee0 þ M Bck (38) e  Be0 þ MðJoue  Hke0 Þ  1; 8e; e 2 e; e 6¼ e Constraint (38) states that if the task e is out-of-warehouse, the task e0 is the return, and the storage position where the task e starts is the same as the storage position of the task e0 , the task e must be executed before the task e0 . 2.3

Algorithm

Table 3.

Neighborhood search algorithm pseudo-code.

Require: For each pallet, we record its fitness value Distsum i , distance from the store station to the picking station Distchu i , distance from the picking station to the store station Disthk i , distance from the picking station to recycle station Disths i Generate maximum cycles of neighborhood search X j; Distsum The total number of fitness values Distsum ¼ i ; i2I kk ¼ 1; while kk 0 (continued)

111

Table 3. 16: 17: 18: 19: 20: 21: 22: 23: 24: 25: 26: 27: 28:

Continued

then choose with equal probability among the store station; else if Disths i > 0 then choose with equal probability among the recycle station; end if end if else if i > 0:5 then choose randomly the pallet except for pallets that have been selected from the pallet set to meeting order requirements to replace pallets i; chu hk hs assign pallet related information and calculate Distsum i ; Disti ; Disti ; Disti end if kk ¼ kk þ 1 end while

Table 4.

NS-SA algorithm parameter setting.

Parameters

Value

Initial simulated annealing temperature Maximum number of simulated annealing cycles NI Maximum number of cycles for local search j Initial acceptance rate of degenerate solutions t0 Temperature reduction factor a Final temperature Tf

T0 1200 50 0.4 0.99 T0 =107

All experiments were performed using MATLAB R2016a in a computer with an Intel i79700KF8C8T Processor and 16GB RAM. We input the above algorithm parameters as well as order data and warehouse information data for simulation and get the first phase solution results with the number of iterations in the AGV scheduling mechanism as shown in Figure 5, and we get the second phase solution results and variation with the number of iterations in AGV scheduling mechanism is shown in Figure 6.

Figure 5.

The change of adaptation in the first phase of the AGV scheduling mechanism.

112

Figure 6.

The change of adaptation in the second phase of the AGV scheduling mechanism.

As shown in the figure, the algorithm can converge after 1200 iterations. According to the solution result of the AGV scheduling mechanism, the total length of the walking path of the AGV robot is calculated to be 6273 unit lengths, which is optimized by 34.7% compared with the initial feasible solution, and the maximum task completion time is at the 347th unit time, which is optimized by 33.3% compared with the initial feasible solution. This indicates that the optimization effect is significant and that the proposed AGV scheduling mechanism and method are effective.

3 CONCLUSION We present an innovative approach for the integrated optimal scheduling of merchandise pallets, storage spaces, and AGVs in complex unmanned warehouses, including a merchandise pallet assignment model, an AGV collaborative optimal scheduling mechanism, an AGV operation scheduling optimization model, and a simulated annealing algorithm designed to solve the above models. The proposed framework can be used to provide reference and theoretical support for unmanned warehouse operation decision-making using real-time merchandise order data. A case study of typical order data shows that the total length of the proposed AGV scheduling mechanism and method AGV travel path is 6273 unit lengths, which is 34.7% optimized compared to the initial feasible solution, and the maximum task completion time is at the 347th unit time, which is 33.3% optimized compared to the initial feasible solution, resulting in significant optimization effects, and meeting the actual operational requirements. The significance of this paper is that the proposed optimization mechanism and method for the refined scheduling of AGVs in complex unmanned warehouses brings significant benefits for improving operational efficiency and reducing the operating costs of unmanned warehouses. Unmanned warehouse managers can adequately compare or integrate storage positions, pallets, and multi-dimensional spatial-temporal data of AGV operations to manage the operation of unmanned warehouses under each commodity order flow.

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REFERENCES Ding Y., Ma H., Li S. Path Planning of Omnidirectional Mobile Vehicle Based on Road Condition. 2019 IEEE International Conference on Mechatronics and Automation (ICMA). IEEE, 2019: 1425–1429. Kumar N.V., Kumar C.S. Development of Collision-free Path Planning Algorithm for Warehouse Mobile Robot. Procedia Computer Science, 2018, 133: 456–463. Le A.V., Arunmozhi M., Veerajagadheswar P., et al. Complete Path Planning for a Tetris-inspired Selfreconfigurable Robot by the Genetic Algorithm of the Traveling Salesman Problem. Electronics, 2018, 7 (12): 344. Sun Q. The Research on Path Planning of Automated Guided Vehicle System[D]. Zhejiang University, 2012. Sheng D.D. Research on Picking Path Planning for Multi-Mobile Robots in Intelligent Warehouse[D]. Beijing Jiaotong University, 2019. Wang Z.Y. Research on Path Planning and Scheduling Algorithm for Multiple AGV System[D]. Beijing University of Posts and Telecommunications, 2019. Xiong X.X. Research on Path Planning of Multi-Mobile Robots in Intelligent Warehouse[D]. Zhejiang SCITECH University, 2021.

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Advances in Traffic Transportation and Civil Architecture – Liu, Qi & Law (eds) © 2023 copyright the Author(s), ISBN 978-1-032-51438-3

Socioeconomic contribution of rural road construction and revampment: A case study in China** Di Wu* Laboratory for Traffic & Transport Planning Digitalization, Transport Planning and Research Institute, Ministry of Transport Beijing, China

Xu Ji Department of Road Transport, Transport Planning and Research Institute, Ministry of Transport, Beijing, China

Chen Qiuren Division of Transport Planning, Sichuan Highway Planning, Survey, Design and Research Institute Ltd., Chengdu, China

ABSTRACT: Road improvement has always been the top issue of China’s rural policies, which have brought magnificent changes in the economy, and social development to the rural areas, exerted far-reaching contributions to poverty uplifting, and the completion of a well-off society. An emerging necessity of systematically evaluating the contribution of rural roads to socioeconomic development in China is addressed to deploy funds and resources to the most wanted area under the new circumstances and requirements of the rural revitalization strategy stipulated in 2020. Through a factor analysis of the contribution indicators of rural roads identified from previous research, this article has extracted and interpreted three major common dimensions of rural roads - contribution to the economy, social development, and transport. The construction of scales for each contribution indicator of rural roads is also measured after detailed statistical tests of the adequacy and validity of the sample data. The findings in this article can distinguish significant contributions of future rural road projects and enhance policymaking efficiency through optimized arrangements of resources and funds.

1 INTRODUCTION China has always attached great importance to developing rural areas, undertaking wideranging reforms to address the “three rural issues” – agriculture, rural areas, and farmers. A series of rural-related policies such as poverty alleviation, poverty reduction, and the building of a well-off society in an all-round way have been promoted early since 1978 - the time reform and opening-up policy was stipulated – through the Document Number One issued by CCP every year and thousands of supporting guiding opinions, practices, ordinances, and regulations detailed by government departments at all levels, accompanied by a large amount of investment in capital, human resources, and other social resources. Since 2003, according to the deployment requirements of the central government’s “three rural issues” and the political slogan “Want rich, build a road first”, the Ministry of Transport of the People’s Republic of China (MTPRC) has put forward the construction goal of “repairing rural roads, *

Corresponding Author: [email protected] This article is sponsored by the Fund of Transport Planning and Research Institute for Distinguished Young Scholars (No. 072106-002). **

DOI: 10.1201/9781003402220-13

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serving urbanization, and allowing farmers to walk on asphalt roads and cement roads.” In 2005, MTPRC issued the big “Construction Plan for Rural Roads”. The total length of rural roads has skyrocketed since then and has reached 4.3M kilometers by the end of 2020. More than 99% of townships and villages in rural China had paved roads and county bus services, according to MTPRC (Ministry of Transport of the People’s Republic of China 2021). The magnificent changes in the rural economy and social development are accompanied by the soaring mileage of rural roads. The income of rural residents has increased substantially, with the per capita net income of farmers increasing from 134 yuan in 1978 to 17131 yuan in 2020; the per capita living consumption expenditure of rural residents will increase from 116 yuan to 15,916 yuan (Ministry of Transport of the People’s Republic of China 2021). All rural poor people and 832 poverty-stricken counties were lifted out of poverty under the current standard in 2020. The rural infrastructure has been continuously strengthened, and the level of essential public services has constantly been improved. The townships with primary schools and kindergartens reached 98% and 97%, respectively; 99.5%, 99.7%, and 90% of rural areas have access to electricity, telephone, and internet (National Bureau of Statistics of China 2017). Rural non-agricultural industries are developing rapidly, and the agricultural product processing industry has become a new bright spot in township enterprises’ development, accounting for 24% of the total output value of township enterprises (Ministry of Agriculture and Rural Affairs of the People’s Republic of China 2017). Improvement of rural roads and public transport services has served as a crucial means to achieve the abovementioned achievements and far-reaching contributions to the rural areas. Starting in 2020, China has achieved the completion of a well-off society in an all-around way and launched the strategy of rural revitalization to uplift the communities to prosperity by 2035. There is a need to conduct a systematic evaluation of the contribution of rural roads to deploy funds and resources to the most wanted area. This study adopts a systematic approach to determine the major common dimensions of the contribution of rural roads to China’s socioeconomic development. First, a literature review is conducted to identify potential contributions in previous research from academic institutions’ perspectives. Related experience has been drawn from the last rural road projects in different regions in China and interviews with experts, industry professionals, and government officials. Second, a generalized list of contribution indicators of rural roads is shown based on previous research. The relative significance of these indicators is testified through a questionnaire survey. Third, factor analysis has been carried out to extract the major components of the identified contribution indicators of rural roads using the survey result. Finally, interpretations of the major structured components are given based on the common traits of contribution indicators of rural roads under each dimension. The following sections introduce the research results.

2 CONTRIBUTION OF RURAL ROADS 2.1

Review from the academic sector

Extensive research has testified to the positive impact of highway construction on the economy, transportation conditions, governance, health, environment, and education. Eagle stated that in counties that were economic centers, increases in highway expenditures had a positive and long-term effect on employment increase and the economy (Eagle 1987). Chandra and Thompson used historical data from 1969 to 1993 in the United States. They found that highways had a differential impact across industries: specific industries would grow due to reduced transport costs while others might shrink as economic activity relocated (Chandra 2000). Wang used an input-output method and the industry correlation theory to quantitatively analyze the impact of the increase in the unit output value of the transportation industry on the national economic growth and argued that the rise in the output value of

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the transportation industry would cause the multiplier growth of the GDP and the number of labor employment (Wang 2004). Ozbay et al. utilized 1990-2000 New York and New Panel data in Jersey to analyze the spatiotemporal effects of transportation investment on economic growth and found spillover effects and time-lag effects between transportation infrastructure and economic growth (Ozbay 2007). Shen reviewed 50 feasibility study reports of highway projects involving the coordinated development of urban and rural areas and argued that the construction of transportation infrastructure in rural areas could effectively reduce the cost of agricultural products and enhance the benefits of economic activities in rural areas (Shen 2012). Agbelie et al. selected the economic and social panel data of 40 countries and believed that investment in transportation infrastructure such as roads would affect GDP, but the magnitude of the impact varied among counties, which fluctuated wildly under the influence of policies (Agbelie 2014). Starkey and Hine suggested that investment in rural roads had delivered comprehensive benefits, including economic returns and poverty reduction, especially in countries with low road network densities (Starkey 2014). Chakrabarti analyzed changes in the density of national highways in India across 25 states and found that a 10% increase in national highway density was associated with a 1-6% increase in private sector employment (Chakrabarti 2018). 2.2

Experiences from experts and government officials

Since 2018, we’ve investigated 303 counties in China with rural road projects that had just finished construction. Through a with-or-without comparison of these areas, various changes occurred after the construction of rural roads, such as increased agricultural products selling and resources extraction, growth of tourists, increase in employment rate, and improvements in rural environments and infrastructures. However, the types and amplitudes of changes vary depending on their location. These changing phenomena and uplifting socioeconomic levels were further confirmed through interviews with government officials in corresponding areas of different. Officials also argued that the type and magnitude of rural road contribution might vary depending on where and when the rural roads were constructed. This further underlines the importance of a systematic evaluation of the contribution of rural roads. 2.3

Contribution indicators of rural roads

Several researchers have conducted studies on the contribution of highways and other transport infrastructures. Shi evaluated the environmental impact of highway construction from four aspects of water, acoustic, atmosphere, and soil, using an uncertain multi-attribute highway traffic environment comprehensive evaluation model (Shi 2007). Liang et al. used an index method of the logical thinking of value-purpose-goal-standard to construct the corresponding relationship between the mainstream values and the values embodied in highway projects (Liang 2007). Then, they used the index to analyze the impact of several highway construction projects in Yan’an, China, from perspectives such as planning and implementation consistency, the evaluation of engineering effects, the evaluation of the impact on grassroots governance capacity, the evaluation of social and economic benefits, and the degree of contribution to new rural construction. Ma and Wang followed the general theories of enterprise management, traffic planning, and evaluation and analyzed the impact of highways based on the target evaluation method, comprehensive index method, and historical dynamic evaluation method and used rural road projects in Zhejiang province as a case study (Ma 2007). Chen and Liao used a comprehensive evaluation method to evaluate the impact of a highway transportation system from several indicators: sustainable development level, economic effect, social effect, resource consumption, and environmental impact. The selection of indicators is mainly intensity, road access rate to established villages, etc. (Chen 2009). 117

The author has conducted research to identify the contribution of rural roads in general. Seventeen contribution indicators of rural roads have been identified through a systematic literature review approach, investigation of previous rural road projects, and interviews with experts and professionals from the Ministry of Transport, DOTs at the provincial, township, and county levels. Detailed descriptions of the identified contribution indicators of rural roads are listed in Table 1.

Table 1.

Contribution indicators from rural roads in general.

Indicators

Description

Increase in gross domestic product

The gross regional product can directly reflect the superiority of a region’s material foundation and is an evaluation index to measure the level of economic development in the index system.

Increase in rural tourism passengers

The improvement of rural traffic conditions has accelerated the development of rural scenic spots while attracting more urban residents to enjoy rural scenery and farmhouses in rural areas. This indicator reflects the impact of rural road construction on the attractiveness of local rural tourism.

Growth in the number of individual service businesses

Rural road construction drives and affects the development of the service industry in the region where it is located. Especially with the increase in the intensity of rural tourism and resource development, the growth of the service industry has accelerated, and the number of individual businesses has gradually increased.

Increase in the number of agriculture processing industries

This indicator reflects the degree of development of the specialty product processing industry.

Increase in social investment attraction.

The development of transportation in rural areas effectively improves the local investment environment and attracts more social capital.

Increase in the average price of rural land

There are differences in land value due to its social and natural attributes, which are directly reflected in the price gap. The theory of intermediate differential land rent in economics emphasizes that the closer the land and basic transportation facilities are, the higher the rent and the higher the use value. Therefore, from the perspective of socioeconomics, rural road construction affects the appreciation of space and the potential of rural land.

Increase in the amount of natural resource extraction

The construction of resource-based rural roads can effectively improve the traffic conditions around the resource, reduce transportation costs, and increase the mining scale.

Value-added in per capita disposable income

Improving traffic conditions in rural areas increases regional mobility, increases employment opportunities, and promotes income growth.

Value-added per capita consumption expenditure

The improvement of traffic conditions in rural areas has made the circulation of commodities more convenient. The supply of various consumer goods has increased in quantity and type. The consumption concept of residents has also changed with the continuous contact with the outside world.

Growth of motor vehicle ownership

New rural road construction improves travel conditions, similar to induced traffic demand and increased willingness to travel. It induces (continued )

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Table 1.

Continued

Indicators

Description more vehicle demand, encouraging more rural households to purchase private cars (WSP 2018).

Increase residents’ willingness New rural roads will connect villages to towns and suburbs from to travel. scratch and bring great convenience to residents living in the rural area, which consequentially induces travel demand and increases residents’ willingness to travel, according to the theory of induced traffic and demand (Lee 1999; WSP 2018). Expand residents’ travel radius

Travel radius can be roughly summarized as the ability to reach the attraction point from the source point by a particular mode of transportation, which is deeply affected by the convenience and accessibility of transportation infrastructures. Rural road construction will improve the travel condition in rural areas and expand residents’ travel scope.

Increase in the employment rate

On the one hand, the construction of rural roads has created many direct jobs; on the other hand, it has also increased related indirect employment opportunities such as building materials and logistics. This indicator reflects the impact of rural road construction on local employment.

Poor reduction rate

This indicator reflects the impact of rural road construction on poverty alleviation.

Increase in prices of major agricultural products Increase in school-age children’s enrolment

This indicator reflects the degree of marketization and commercialization of agricultural production. The enrollment rate of school-age children refers to the proportion of school-age students enrolled in school. It is mainly used to measure the educational status of children in the region, and it also reflects the educational level of the school-age population.

Improve the convenience of medical treatment

According to WHO’s study, the travel time for a resident to receive medical treatment should not exceed 2 hours (World Health Organization 2001). The development of rural transportation has laid the foundation for the popularization of essential public services such as medical treatment, making the treatment of patients timelier and more convenient.

2.4

Contribution indicators of rural roads

To analyze the significance of the identified contribution indicators of rural roads, we conducted a questionnaire survey to solicit necessary information. The respondents were asked to give their expert opinions on the relative significance of the identified contribution on a Likert scale table from 1 to 5 (“1” denotes “not significant,” “2” means “fairly significant,” “3” represents “significant,” “4” indicates “very significant,” and “5” denotes “extremely significant.”). Besides, respondents were also required to provide information about their professional background, the region they lived in, and whether they had experience working in rural areas. The questionnaire survey started in February 2020 and lasted for a year. Three hundred ninety-one questionnaires were sent out, and 391 completed questionnaires were retrieved with a completion ratio equal to 100%. Most of the respondents have working experience in rural areas, and statistical analysis results of all seventeen identified contribution indicators of rural roads have a relative significance of more significant than 70%, indicating that these indicators are essential and should be considered appropriate to evaluate the contribution of rural roads.

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3 FACTOR ANALYSIS OF CONTRIBUTION FACTORS 3.1

Basic idea and steps of factor analysis

The basic idea of factor analysis is to group the original variables according to the correlation between the data so that the correlation between variables in the same group is high. In contrast, the correlation between variables in different groups is low. Each group of variables represents a basic structure and is characterized by an unobservable composite variable, and this basic structure becomes a common factor. Extracting common factors by factor analysis is the classification process; each factor is a classification index. The linear combination of factors has a more considerable absolute value of the coefficient, and the equivalent variable is a one-way under the classification index. Generally, there are four steps for factor analysis: (1) check the adequacy of factor analysis; (2) generate the correlation matrix and extract initial factors; (3) conduct rotation; (4) interpret the meanings of extracted significant components. 3.2

Adequacy analysis

Before conducting factor analysis, the Kaiser-Meyer-Olkin (KMO) test and Barlett’s test of sphericity are required to examine whether the sample data is appropriate to use factor analysis. KMO is a statistical measure comparing simple correlation coefficients and partial correlation coefficients between variables. The closer the KMO measure is to 1, the more suitable it indicates for factor analysis, and the threshold value is 0.5. The Bartlett test of sphericity is a statistical measure for judging the null hypothesis that the correlation matrix is an identity matrix. If the significance p is smaller than 0.05, the null hypothesis is rejected, indicating the data is appropriate for factor analysis. Table 2 shows the results of all 17 contribution indicators of rural roads. The KMO measure is 0.946, and the observed significance level of Bartlett’s test of sphericity is 0.000, indicating the survey data is suitable for factor analysis.

Table 2.

KMO and bartlett’s test results. Bartlett’s test of sphericity

Kaiser-Meyer-Olkin measure of sampling adequacy

Approximate chi-square

Degree of freedom

Significance

0.946

4625.029

136

0.000

3.3

Factor extraction

We use the Principal Components method to extract the initial factors. Figure 1 shows the scree plot of all 17 contribution indicators of rural roads. Three principal components are removed by specifying eigenvalues greater than 1. Table 3 also indicates that the cumulative variance of the first three major components is 68.33%, that is, these three major components can explain 68.33% of the 17 indicators. 3.4

Component rotation and interpretation

The varimax rotation method is further used to get the rotation factor loading matrix of the extracted major components. As shown in Table 4, the rotated component matrix indicates

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Figure 1.

Scree plot for factor analysis of contribution indicators.

Table 3.

Total variance explained by extracted major components. Initial eigenvalues

Rotation sums of squared loadings

Components

Total

% ofVariance

Cumulative %

Total

% ofVariance

Cumulative %

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

9.305 1.268 1.043 0.723 0.642 0.588 0.449 0.417 0.382 0.366 0.338 0.311 0.282 0.276 0.249 0.194 0.166

54.735 7.458 6.137 4.255 3.774 3.461 2.642 2.454 2.248 2.152 1.989 1.829 1.658 1.623 1.463 1.142 0.979

54.735 62.193 68.330 72.585 76.359 79.820 82.462 84.917 87.165 89.317 91.306 93.135 94.793 96.416 97.880 99.021 100.000

4.398 4.321 2.897

25.869 25.419 17.042

25.869 51.288 68.330

the linear relationship between the extracted components and the original indicators, that is, each component is a linear combination of the contribution indicators of rural roads. The first component reflects the influential role of rural road construction in promoting local resource development, industrial growth, and economic development. More intensively reflects the pulling effect of rural road construction on the local economy. Therefore, the first component can be named “Contribution to the economy.” The second component reflects the strong spillover effect and external impact of rural road construction on improving people’s livelihood, community development, income and consumption, education, employment, etc. Therefore, the second component can be named “Contribution to social development.”

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Table 4.

Rotated component matrix for the contribution indicators of rural roads. Components

Contribution Indicators

1

2

3

Increase in gross domestic product Increase in rural tourism passengers Growth in the number of individual service businesses Increase in the number of agriculture processing industries Increase in social investment attraction. Increase in the average price of rural land Increase in the amount of natural resource extraction Value-added per capita disposable income Value-added per capita consumption expenditure Growth of motor vehicle ownership Increase residents’ willingness to travel. Expand residents’ travel radius Increase in the employment rate Poor reduction rate Change in prices of major agricultural products Change in school-age children’s enrolment Increased convenience of medical treatment

0.597 0.749 0.712 0.752 0.776 0.620 0.660 – – – – – – – – – –

– – – – – – – 0.619 0.649 – – – 0.695 0.797 0.753 0.787 0.693

– – – – – – – – – 0.723 0.823 0.821 – – – – –

The third component primarily reflects the traffic benefits directly generated by the construction of rural roads, so the third public factor can be named “Contribution to transport.” Interpretation of the major components is summarized in Table 5. Table 5.

Interpretation of the major components.

Components

Contribution indicators

Contribution to the economy

Increase in gross domestic product Growth in rural tourism passengers Growth in the number of individual service businesses Increase in the number of agriculture processing industries Increase in social investment attraction. Increase in the average price of rural land Increase in the amount of natural resource extraction Value-added per capita disposable income Value-added per capita consumption expenditure Increase in the employment rate Poor reduction rate Change in prices of major agricultural products Change in school-age children’s enrolment Increased convenience of medical treatment Growth of motor vehicle ownership Increase in residents’ willingness to travel. Expand residents’ travel radius

Contribution to the social development

Contribution to the transport

4 CONCLUSIONS The nearly 20 years of large-scale rural road construction have brought up tremendous direct and indirect benefits to the socio-economic development of the countryside in China. Nevertheless, until now, the income, education, public infrastructures, medical treatment,

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and other social welfare services still vary in rural areas of different regions in China, with the development of the eastern part being relatively fast, followed by the central part, and the western part is still lagging. With the overall improvement of rural economic and social levels, it is necessary to evaluate the positive effect of rural road construction through a systematic and scientific evaluation, to provide an essential basis for implementing the revitalization strategy. Based on the previous relevant research data, fully testing its validity, adequacy, and other statistical indicators, this paper uses the factor analysis method to establish a rural road contribution evaluation modal. It includes three main components: economic contribution, social contribution, and transport contribution, as well as 17 specific evaluation indicators. This evaluation system will provide a reference for the decisionmaking and implementation of future rural road project construction and help formulate scientific and reasonable regional and urban-rural transportation integration development strategies.

REFERENCES Agbelie B.R.D.K.: An Empirical Analysis of Three Econometric Frameworks for Evaluating Economic Impacts of Transportation Infrastructure Expenditures Across Countries. Transport Policy. 35, 304–310, 2014. https://doi.org/10.1016/j.tranpol.2014.06.009. Chandra A. and Thompson E.: Does Public Infrastructure Affect Economic Activity? Evidence From The Rural Interstate Highway System. 34, 2000. Chakrabarti S.: Can Highway Development Promote Employment Growth in India? Transport Policy. 69, 1– 9, 2018. https://doi.org/10.1016/j.tranpol.2018.05.009. Chen B. and Liao X.: Research on Evaluation Index System of Highway Transportation Sustainable Development. China Journal of Highway and Transport. 22, 111–117, 2009. Eagle J.D.: Dynamic Highway Impacts on Economic Development. Transportation Research Record. 7, 1987. Lee D.B., Klein L.A., Camus G.: Induced Traffic and Induced Demand. Transportation Research Record. 1659, 68–75, 1999. https://doi.org/10.3141/1659-09. Guo-Hua L., Qi Y., Rong-Guo M. Approach to Construction of Index System of Performance Evaluation About Rural Highway. China Journal of Highway and Transport. 20, 111–116, 2007. National Bureau of Statistics of China: Bulletin of the Main Data of the Third National Agricultural Census (no. 1), http://www.stats.gov.cn/tjsj/tjgb/nypcgb/qgnypcgb/201712/t20171214_1562740.html, last accessed 2022/ 02/17. Ma S., Wang Y. Approach to Performance Evaluation Theories and Methods About Rural Highway. Highway. 107–111, 2007. Ministry of Transport of the People’s Republic of China. 2020 China Transportation Statistics Yearbook. China Statistics Press, Beijing, China, 2021. Ministry of Agriculture and Rural Affairs of the People’s Republic of China: History will not Forget the Important Contribution of Township Enterprises, http://www.moa.gov.cn/xw/bmdt/201807/t20180731_ 6154959.htm, last accessed2022/04/17. Ozbay K., Ozmen-Ertekin D., Berechman J. Contribution of Transportation Investments to County Output. Transport Policy. 14, 317–329, 2007. https://doi.org/10.1016/j.tranpol.2007.03.004. Shen L., Jiang S., Yuan H. Critical Indicators for Assessing the Contribution of Infrastructure Projects to Coordinated Urban–Rural Development in China. Habitat International. 36, 237–246, 2012. https://doi. org/10.1016/j.habitatint.2011.10.003. Shi L. Uncertainty Multi-attribute Highway Environment Comprehensive Evaluation Model. Journal of Highway and Transportation Research and Development. 24, 155–158, 2007. Starkey P., Hine J.: How Transport Affects Poor People with Policy Implications for Poverty Reduction: A Literature Review. UN-Habitat, the Overseas Development Institute, 2014. Wang C. Quantitative Study of Transportation’s Contribution to National Economic Growth. China Journal of Highway and Transport. 17, 94–97, 2004. WSP: Latest Evidence on Induced Travel Demand: An Evidence Review, Salford, Manchester, UK (2018). World Health Organization: Background Paper for the Technical Consultation on Effective Coverage of Health Systems, Geneva, Switzerland (2001).

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Advances in Traffic Transportation and Civil Architecture – Liu, Qi & Law (eds) © 2023 copyright the Author(s), ISBN 978-1-032-51438-3

Noise cancellation in highway construction Yufan Zhang* Highway Institute, Chang’an University, Xi’an, China

ABSTRACT: In recent years, to satisfy the need for high-speed development of China’s economy and the steady progress of urbanization, the government invests a lot in the infrastructure. However, the environmental situation in the city gets worse and causes enormous problems under this background, like the noise from traffic or the construction site. This brings an increasing number of serious impacts on human life activities and transportation comfort. Thus, this article aims to sum up some methods to detect some methods in highway construction to detect and evaluate noise, such as the 3D GIS tool and Deep Audio Representation Networks. Some ways can solve highway construction noise being included, Negative Noise Control or Active Noise Control Techniques. These methods can improve the quality of people’s life and satisfy the people-oriented need for sustainable transportation, increasing transportation comfort.

1 INTRODUCTION To support economic development and stimulate society production and consumption, the government in China or the US usually takes a method that enlarges the investment in infrastructure construction. However, in recent years, highway construction noise becomes a serious problem (Cheuk 2000; Hunashal & Patil 2012; Wang 2016). On both sides of the street, if there is a construction site and it produces a lot of noise, whether high-rise or lowrise, the quality of residents’ lives will decline. Like Figure 1, economic development needs infrastructure and it improves the quality of people’s life. Infrastructure refers to the engineering facilities that provide public services for

Figure 1. *

Economic development affects life.

Corresponding Author: [email protected]

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DOI: 10.1201/9781003402220-14

social production and people’s life. It ensures the country keeps normal activities. Due to transportation infrastructure usually having high technical content and a long investment time and large scale, it will have a relatively large impact on the environment once it is put into use and even causes some harmful problems like highway construction noise. Eventually, the noise will decrease the quality of life which will reduce people’s happiness. In some developed countries, the problem will get worse. In Korea, the problem caused by noise takes up 85% of total environment-related disputes from 2005 to 2013 (NEDRC 2013). Table 1 shows the statutes of the disputes in Korea.

Table 1.

Data on the noise disputes in Korea from 2005 to 2013 (NEDRC 2013).

Year

Total disputes

Noise disputes

2013 2012 2011 2010 2009 2008 2007 2006 2005

189 255 185 174 283 209 172 165 174

152 102 164 148 241 173 142 150 151

Table 1 shows that noise disputes take up a high proportion every year, generally higher than 80%. It becomes a serious social problem in some developed countries and some developing countries. In China, the problem is still fully serious in Report on Environmental Noise Pollution prevention and control. Noise complaints usually take up nearly 45% and the construction noise is the primary part, like Figure 2. It shows in most cities in China, noise problems decrease the quality of people’s life. Thus, it causes a high proportion of noise complaints in recent years.

Figure 2.

Data on the noise disputes in China from 2013 to 2021.

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The construction noise belongs to a kind of sound, which is the same as the common sound. It is mainly caused by the vibration of objects, and the objects that produce or emit noise are called sources (Wang 2019). These objects include the pile driver, pile pulling machine, some air compressors, and so on, which can make a large noise. The characteristics of the noise include universality, abruptness, and impermanence. They mean that in most places on construction sites, noise exists extensively. Thus, this article aims to summarize methods to improve the quality of people’s lives and satisfy the people-oriented need for sustainable transportation, increasing transportation comfort, assumptions, and justifications. Also, it is helpful to be a lower price and higher performance.

2 DETECTION AND EVALUATION 2.1

3D GIS tool

The GIS is a technical system that can collect store and manage and it has enormous ways to describe the whole or part of the earth’s surface with the support of a computer. The 3D GIS tool is established with ArcGIS scripts. The 3D GIS tool can provide a function called ‘enriched information’ to display the distribution and strength of noise which can be used in the decision-making process. The analysis process includes three parts. Firstly, the department should set a target based on the country’s standards which people can bear or people can have a proper life quality, compared to the people’s noise exposure. Secondly, by using the standard and making data normalization, the department can relate people to the excessive noise in the same standard. Finally, a proper exposure-response indicator is chosen, and the data and standard are distributed in the same 2D map. Eventually, the department can ensure the noise hot spots and make some decisions, like Figure 3 (Virginia et al. 2020). Figure 3 shows the noise strength in the red zone is greater than 70dB that people usually cannot bear, and the deep green zone is relatively more proper for people’s life. To realize the 3D map goal, the author puts forward the E-R system and the researchers put some sensors in the large mansions and both sides of the carriageway. With the help of 3D sensors at different heights, the departments can get a 3D noise hot-spot map. For example, the emission points on the road show the potential of 3D tools. It can be seen that the people

Figure 3.

The hot spots in the 2D map (Virginia et al. 2020).

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exposed to the noise and the efficiency of measures we take and various shields degree of different buildings. It can be seen in Figure 4 that the effect after the 3D tools get involved and the efficiency of noise cancellation is increased a lot. It is helpful to the decision-making mechanism. In the testing process, the tool was validated with a benchmark test using 20 stretches of the highway chosen at random among areas with populations exposed to more than 50 dB. The results show a relative mean error of 1.33% after analyzing 3597 receivers above 50dB.

Figure 4.

2.2

The comparison between 2D and 3D tools (Virginia et al. 2020).

Deep audio representation and BLSTM network

The method is designed to detect highway construction noise, because road construction noise has an acoustic indication, like the drilling and other construction activities. In this method, the author takes some features as audio representations and uses the classifier to classify the class of the sound by short-term memory. Then, it can automatically determine what the noise belongs to. Even more innovative, researchers proposed a new frame based on a multiple-stage deep autoencoder network to find the deep audio representation that mixes different information from different features. It can help the departments determine what is the kind of noise and the level of the noise which is helpful in noise cancellation (Li et al. 2018). Figure 5 shows the system of the audio representation and the BLSTM network. The first step of the system is extracting the features of the noise audio. Then, the features come into the next step called the BLSTM network classification which is based on a multiple-stage deep autoencoder network. Eventually, it determines the class of sound to which the audio segment belongs.

Figure 5.

Deep audio representation system (Li et al. 2018).

The system is prominently helpful in some fields of decision and helps the departments classify what kind of noise is, such as transitory car accident noise and long construction noise. It is regarded as a decision-making tool and the government dispatches some relevant

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staff to solve disputes and complaints caused by high construction noise and immediately contacts the police apparatus to solve the accidents. 2.3

Real-time information release system

The real-time information release system is designed to publish the noise information and detect the noise level. It uses an innovative structure called the C/S and B/S structure. The researchers put the detector on both sides of the highway and collect the noise information. After signal processing, the researchers can get the real-time decibel value and transmit them to the public Internet by the GPRS. Eventually, the monitoring center can analyze the data and make decisions based on the data (Wang & Zhou 2014). Figure 6 shows the different parts of the system and different parts are responsible for the different work. The monitoring center receives and analyzes the data and publishes the useful data in real-time through the Web. Then the protection department can formulate a plan and policy to decrease the noise and give support to environmental protection.

Figure 6.

The Real-time information release system.

3 NOISE CONTROL TECHNIQUES After detecting and evaluating, the government and the relevant departments develop some techniques to solve the noise or decrease the bad influence caused by highway construction. This part aims to summarize some ways about noise control techniques, which include the negative noise control techniques and the active noise control techniques in recent years. The negative noise control techniques always in a low price but have a lower effect in a settled place. Active noise control techniques always have a higher price but can deal with different situations. Both of the methods have their service conditions and limiting conditions. 3.1

Negative noise control techniques

The negative noise control techniques always change materials. Structures or researchers can add some soundproof walls (Ding 2020; Li et al. 2015) and an acoustic panel to decrease the noise. The locations are limited and the volume of the equipment is usually large (Hu & Zou 2022; Li 2021). 3.1.1 Green belt bamboo constructions Natural plants like bamboo always grow faster than other vegetation and they are capable of significantly reducing noise. In Indonesia, the climate can satisfy the high growth speed of bamboo. Thus, the bamboo belt can be built in a short time and at a high density. One of the advantages of the bamboo green belt is the low price and environmentally friendly, and it doesn’t need cement or rebar and decreases the price a lot (Yasin et al. 2020). The bamboo can grow 120cm-150cm in one day in a tropical area like Indonesia, so the green belt can be built at a high speed. Besides, the bamboo green belt can be evergreen in a tropical area or subtropical area, so the bamboo green belt can be also used in the southeast of China, like the Zhejiang province, and the climate can satisfy the need for bamboo growth.

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3.1.2 Movable noise barriers The movable noise barriers are designed to reduce the noise near the road when there is construction near the highway construction (Lee et al. 2015). Like Figure 7, the movable noise barrier is designed to be movable and detachable easily, when there is a construction site like the breaker work, gang form, and asphalt saw. The barrier can be built in a short time and does not disturb normal transportation or people’s life.

Figure 7.

Movable noise barrier (Lee et al. 2015).

To verify the usability, it measures in four directions and researchers measure the noise level at the same time. The researchers find the noise level decreased by 20dB than before. 3.1.3 Multi-objective optimization model This method wants to find an optimal balance between noise reduction, safety, and cost control. To achieve this goal, the researchers take the hybrid genetic algorithm-ant colony optimization model to solve the multi-objective optimization problem. The researchers deem that optimizing the site layout can help them decrease the noise. However, the change in the layout may increase the cost of the construction and decrease the safety of the workers. Therefore, they design a method considering the potential safety risk and transportation cost arising from interactions between onsite facilities. The multi-objective optimization problem must exist an optimal solution and it can provide the construction department with a new idea to solve the problem (Ning et al. 2019).

3.2

Active noise control techniques

The previous article provides some methods to solve the noise problem, such as soundproofing, but these methods have some limits when some equipment emits high-frequency noise (Kwon et al. 2016). The research aims to exploit a new technique to decrease highfrequency noise and testify to the applicability of active noise control (ANC) as a new approach. The researchers make some simulations to demonstrate and certify the cancellation of the high-frequency noise. Like Figure 8, the microphone emits some adverse frequency sound waves to counteract the noise sound wave and make the central area become a quiet zone. Thus, people in the quiet zone will receive less noise and sound waves and have a higher quality of life. 129

Figure 8.

ANC system (Kwon et al. 2016).

Figure 9 shows that the simulation results show that noise cancellation can be highly efficient in the low- and mid-frequency bands below 1,000 Hz and that the reduction effect degraded as the frequency increased. Thus, the workers can get the conclusion that the ANC system is more suitable for high-frequency noise and has high efficiency.

Figure 9.

Noise signal processing (Kwon et al. 2016).

4 CONCLUSIONS The methods still have some limitations and problems which may restrict the project or the construction. These limitations can be the improvement directions in the future. 4.1

Price

The quantity of the equipment exists at an optimum value with less price and higher performance. The equipment is expensive and most of the company and governments cannot afford them. It will decrease the buying inclination and slow down the speed of development. The market scale and the development prospect will be reduced. 130

4.2

Application condition

Like the bamboo green belt, in the north of China, it is inadmissible. That is because, in the north of China, it is cold and dry in winter. Most of the bamboo cannot survive in the area. Thus, some plants are constrained by the climate (Li et al. 2011). In the north of China, most of the plants are deciduous trees and boreal coniferous forests. That determines that large trees cannot be planted in a high density. For the local government, it is not practical to use green belt noise soundproofing in the north. 4.3

The construction of the difficulty

In the southwest of China, the karst landform is wide distribution and mountains lead to very complex sound transmission conditions. This makes it more difficult for researchers to survey, and we don’t know when geological disasters happen. It is also difficult in the southeast of China because most of the places have high temperatures and rainy climates in the summer, so the time limit of the project will be delayed and the rain will bring about some problems like acid rain erosion. This project aims to sum up some methods to detect and evaluate the level of noise. Most importantly, the government must improve the happiness of the public. Thus, some methods which are helpful for the departments to reduce noise are summarized. Even though there are some limitations in the methods, they are still useful for the government to use for reference to reduce highway construction noise.

REFERENCES Cheuk F. N. (2000). “Effects of Building Construction Noise on Residents: A Quasi-Experiment.” J. Environ. Psychol., 20(4), 375–385. Ding C. “A Brief Discussion on the Construction and Application Analysis of Highway Sound Insulation and Noise Reduction Barrier Project.” Heilongjiang Transportation Technology 43.06(2020):193–194. DOI:10.16402/j.cnki.issn1008-3383.2020.06.113. Hunashal R.B. and Patil Y.B. (2012). “Assessment of Noise Pollution Indices in the City of Kolhapur, India.” Proc., Int. Conf., Vol. 37, Elsevier, Amsterdam, Netherlands, 448–457. Hu M. and Zou G. “Analysis of the Progress of Sound Insulation Materials in the Field of Construction Engineering.” Chinese Architectural Decoration .01(2022):82–83. Hu Z. “Effect Analysis of Cement Specimens on Acid Rain Corrosion Resistance of Concrete.” Sichuan Cement. 09(2016):11+24. https://baike.baidu.com/item/%E5%9F%BA%E7%A1%80%E8%AE%BE%E6% 96%BD/3831695. https://baike.baidu.com/item/%E5%9C%B0%E7%90%86%E4%BF%A1%E6%81%AF%E7%B3%BB%E7% BB%9F/171830?fromtitle=GIS&fromid=31541&fr=aladdin https://wenda.huabaike.com/hywd/74253.html https://www.mee.gov.cn/hjzl/sthjzk/hjzywr/index.shtml. Kwon N. et al. “Construction Noise Management Using Active Noise Control Techniques.” Journal of Construction Engineering & Management (2016):04016014. Li L. et al. “Design and Manufacture of Highway Sound Insulation Wall with Light Control Lighting System.” Knowledge of Modern Physics 27.02(2015):41–44. doi:10.13405/j.cnki.xdwz.2015.02.021.3. Lee S.C., Chung Y.J. and Im J.B. “Site Mitigation Plan for Noise Sources from Construction Sites by Developing Movable Noise Barriers”. Transactions of the Korean Society for Noise and Vibration Engineering 25.1(2015): 58–64. Li Y., Li X., Zhang Y., Liu M., and Wang W., “Anomalous Sound Detection Using Deep Audio Representation and a BLSTM Network for Audio Surveillance of Roads,” in IEEE Access, vol. 6, pp. 58043–58055, 2018, DOI:10.1109/ACCESS.2018.2872931. Li Y. “Research on Design and Construction of Drainage, Noise Reduction, and Anti-skid Asphalt Pavement Materials.” Transportation World. 28(2021): 140–141. doi:10.16248/j.cnki.11-3723/u.2021.28.066.. Li Y., et al. “Review of Studies on the Effects of Climatic Factors on Bamboo Growth.” Transactions on Bamboo Research 30.03(2011):9–12+17.

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Ning X., Qi J., Wu C., Wang W., Reducing Noise Pollution by Planning Construction Site Layout via a Multi-objective Optimization Model, Journal of Cleaner Production, Volume 222, 2019, Pages 218–230, ISSN 0959-6526, https://doi.org/10.1016/j.jclepro.2019.03.018. NEDRC (National Environmental Disputes Resolution Commission in Korea). (2013). “Environmental Dispute Mediation Status Report.” Seoul Puyana-Romero V., Cueto J.L., Gey R., A 3D GIS tool for the detection of noise hot-spots from major roads, Transportation Research Part D: Transport and Environment, Volume 84,2020,102376, ISSN1361-9209, http://doi.org/10.1016/j/trd.2020.102376 Wang H. (2016). Assessment of the Environmental Impact of Transportation Infrastructure Construction. Green Building Materials (09), 51. DOI:10.16767/j.cnki.10-1213/tu.2016.09.044. Wang K. and Zhou C. “Urban Road Traffic Noise Detection and Real-time Information Release System.” Report of Science and Technology 30.07(2014):199–202. doi:10.13774/j.cnki.kjtb.2014.07.048. Wang Y. (2019). Research On Noise Pollution Control Strategy of a Construction Site (Hunan university). https://kns.cnki.net/KCMS/detail/detail.aspx?dbname=CMFD202002&filename=1020702586.nh. Yasin I., Widaryanto L.H. and Sutrisno W. The Technique of Green Belt Bamboo Constructions for Highway Noise Effect Reductions[J]. Journal of Physics: Conference Series, 2020, 1456: 012006–012006.

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Advances in Traffic Transportation and Civil Architecture – Liu, Qi & Law (eds) © 2023 copyright the Author(s), ISBN 978-1-032-51438-3

Integrated emergency evacuation and supply scheduling for disaster emergency rescue Yanping Zhang School of Civil Engineering and Architecture, University of Jinan, Jinan, Shandong, China

Na Cui* School of Civil Engineering and Architecture, University of Jinan, Jinan, Shandong, China School of Engineering and Technology, Aston University, Birmingham, UK

ABSTRACT: All kinds of sudden disasters occur frequently, causing serious impacts such as casualties, economic losses, and social disorder. Pre-disaster evacuation is the most direct and effective way to reduce casualties. Meanwhile, the distribution of relief supplies is also an important emergency measure to improve the physiological endurance of victims. To solve the problem of organizing the effective evacuation of the masses before the disaster and reasonably allocating emergency supplies, the overall optimization strategy of personnel evacuation and supplies allocation among multiple service facilities is studied. In the modeling process, the interests of the people who have not been evacuated are measured by the penalty coefficient, combined with the travel costs of the people, supplies transportation costs, and purchase costs in the network transportation system, and finally, the dynamic programming model is established with the optimization goal of minimizing the total system cost. In this paper, through GAMS platform programming, numerical experiments are carried out on the model under the 17-node road network to verify the feasibility of the model and to provide an auxiliary decision-making reference for the formulation of safe evacuation plans.

1 INTRODUCTION The occurrence of natural disasters poses a great threat to the safety of human life and property. In recent years, the number of casualties caused by natural disasters is numerous, and the economic losses caused are incalculable. Under the background of the high frequency of disasters, evacuation is the primary task of emergency management and the most direct and effective way to reduce casualties and property losses. At the same time, how to quickly allocate emergency supplies is also a key problem to be solved in emergency response for the people in the shelter who are facing a shortage of daily materials. Therefore, this paper studies the coordination and optimization of multiple links in the emergency activity chain, that is, on the one hand, to organize the effective evacuation of the masses, and on the other hand, to allocate emergency supplies for the evacuees. In recent years, many scholars at home and abroad have made a lot of comprehensive research on emergency evacuation. Duan Xiaohong et al. (Zhao et al. 2022) considered

*

Corresponding Author: [email protected]

DOI: 10.1201/9781003402220-15

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that there was a collaborative decision-making process between emergency vehicle scheduling and traffic evacuation, built the model by using the bi-level programming method, and designed a bi-level bat algorithm to solve the model. Zhao Chuanlin et al. (Sun et al. 2019) incorporated the theory of rational negligence into the study of evacuation problems and built a two-layer model of evacuation network optimization. Sun Dian et al. (Rambha et al. 2021) and Tarun Rambha et al. (Zhang et al. 2019) considered the safe evacuation of residents before the disaster as an important decision-making goal and studied and optimized the evacuation strategy. Zhang Baishang et al. (Shahparvari et al. 2016) and Shahrooz Shahparvari et al. (Cui et al. 2014) studied the impact of uncertainty factors on evacuation optimization, established a multi-objective integer model, and provided auxiliary decision-making reference for formulating the optimal evacuation planning scheme. Some scholars also considered the interests of the victims in the modeling process. Cui Xuan et al. (Camur et al. 2021) proposed the waiting psychological cost measurement function based on the multi-attribute utility theory and established a mixed integer programming model. Mustafa C. Camur et al. (Zhu et al. 2020) included priority to capture the deprivation cost of disaster victims lacking materials, established an evacuation optimization model, and studied the dynamic logistics of maritime evacuation. How to quickly allocate and distribute relief supplies for evacuees is also one of the key research issues. Julie et al. (Bai 2015) established a multi-objective dynamic scheduling model to study the post-disaster emergency supplies allocation problem. Bai Xuejie (Kamyabniya et al. 2021) studied the supplies preset strategy, considered the uncertainty of demand, road, and other factors, and built the emergency supplies the preset model. Afshin Kamyabniya et al. proposed a double objective robust optimization model in the multi-level logistics network and gave the optimal allocation strategy of special emergency supplies (platelets). In the existing research, most scholars only optimize the single link of emergency evacuation or relief supplies allocation, but in the actual rescue decision-making process, the effective evacuation of the masses and the reasonable allocation of supplies are often dynamically combined. At the same time, controlling economic cost is an inevitable key consideration goal in emergency rescue response activities, and the related cost analysis in the process of the emergency response activity chain is also less reflected in previous studies. Given this, this paper studies the comprehensive optimization problem of the safe evacuation of the masses and the rational allocation of emergency supplies, that is, on the one hand, to organize the effective evacuation of the masses, and on the other hand, to allocate appropriate emergency supplies for the successfully evacuated masses.

2 PROBLEM DESCRIPTION In the process of humanitarian relief, timely evacuation of the victims is the most direct and effective way to reduce casualties, and providing emergency supplies for the victims is also an important part of the emergency plan. To achieve the safe evacuation of the masses and the rational allocation of emergency supplies, this paper studies the emergency response problem and its activity diagram is shown in Figure 1. When the government releases disaster warning information to the people in the disaster-stricken areas, the people in the disasterstricken areas rely on available means of transportation or walk to the shelter. At the same time, emergency supplies are allocated from the supply points to meet the needs of the people in the shelters.

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Figure 1.

Schematic diagram of emergency evacuation and material allocation activities.

3 SYMBOL DEFINITION AND MODEL CONSTRUCTION In this paper, i 2 I represents the disaster point, j 2 J represents the shelter, k 2 K represents the supply point, and the total emergency rescue time is expressed in t 2 T. dij and dkj are used to represent the travel distance between the disaster-stricken point and the material supply point, and the refuge point respectively. c1 and s represent the unit travel cost of evacuees and the penalty cost of non-evacuees respectively. The purchase cost and transportation cost of each unit of emergency supplies are represented by c2 and cb. The initial total number of people at the disaster point is defined as n0i , the response rate of people who actively evacuate at each time interval is lt, and the number of relief supplies consumed per person is m. Gj is the maximum capacity of each refuge point. We introduce decision variables wtij and xtkj to represent the number of people evacuated to the refuge site and the number of emergency supplies transported, nti represents the total number of people not evacuated at the affected site at the beginning of the period (t  1) and Njt represents the cumulative total number of people in the shelters. Considering the total transportation of the system and taking the total cost generated as the target, a dynamic programming model is established. The model minimizes the total cost of the entire operation network system. In addition to the actual cost generated in the emergency network, we introduce a penalty coefficient s to construct a penalty cost for the number of people who have not evacuated at the mass gathering point in the early warning cycle to represent the interests of the victims. The objective function is: min

 XXX  XXX c1 dij wtij þ c2 xtkj i2I j2J t2T

þ

XXX

!

k2K j2J t2T



cb  dkj xtkj þ

k2K j2J t2T

XX

s nti 

i2I t2T

X

(1)

wtij

j2J

The constraints required for the model are as follows: X

wtij  lt nti

8i 2 I ; t 2 T

(2)

j2J

 nti ¼ nt1 i

X

wt1 ij

j2J

135

8i 2 I ; t 2 T

(3)

Nj0 ¼ 0 8j 2 J X wtij 8j 2 J; t 2 T Njt ¼ Njt1 þ

(4) (5)

i2I

mNjt  XX

X

xtkj

8j 2 J; t 2 T

(6)

8j 2 J

(7)

8i 2 I ; j 2 J; k 2 K; t 2 T

(8)

k2K

wtij  Gj

i2I t2T

wtij ; nti ; Njt ; xtkl 2 Zþ

Wherein, Equation (1) is the objective function, which represents the total cost of minimizing the system, including the travel cost of the masses, the purchase cost of emergency supplies, the transportation cost, and the penalty cost of the people who have not been evacuated at the disaster site. Equation (2) indicates that the number of evacuees at any time cannot exceed the maximum number of responders. Equation (3) calculates the remaining number of people who have not been evacuated. Equation (4) stipulates that the number of evacuees at the refuge point at the initial time is zero. Equation (5) is used to calculate the total number of evacuees in the shelter. Equation (6) indicates that the transportation volume of emergency supplies shall meet the personal needs of the evacuation population at this point. Equation (7) is the number and capacity limit of the shelter. Equation (8) defines the type of decision variable.

4 NUMERICAL EXPERIMENT 4.1

Parameter setting

In this paper, GAMS programming is adopted, and a 17-node road network is selected to conduct numerical experiments on the model. The values of model parameters are as follows: the walking cost of the mass unit is 0.5 yuan/person, and the unit purchase cost and unit transportation cost of emergency supplies are 5 yuan/piece and 0.08 yuan/piece respectively. At the initial time, the number of people at each disaster-affected point follows a random distribution [500,600], and the space area of each refuge point is 800m2. The whole emergency cycle is set as 3 periods, assuming that each mass needs to consume about 2 units of emergency supplies in each period. The penalty cost coefficient of non-evacuated people is 30. Table 1 shows the evacuation response rate in each period.

Table 1.

4.2

Evacuation response rate in each period.

Period

t=1

t=2

t=3

Evacuation response rate

0.5

0.7

1.0

Optimization results

The model solution results, GAP values, and CPU under the 17-node experimental road network are shown in Table 2. The model operation efficiency is high, and the optimal solution can be obtained within 1s. At the same time, the calculation accuracy of the model is very high, and the GAP value is 0. Considering the influence of model parameters on the experimental results, we set up two different groups of experiments respectively to adjust the evacuation response rate and the 136

Table 2.

Optimization results under experimental road networks.

Experiment pattern of road distribution

Mass Supplies Supplies Penalty travel cost transportation cost purchase cost cost

total cost

17 Nodes

3.39

23.68 0

3.41

5.96

10.92

GAP (%)

CPU (s) 0.18

Note: All cost units in the table are
104 yuan.

capacity limit of the shelter, to observe the influence of the key parameters of the model on the optimal decisions. These experimental results provide a reference for emergency managers when formulating actual emergency plans. The parameter adjustment changes and optimization results are shown in Tables 3 and 4. The result trend changes as shown in Figures 2 and 3.

Table 3.

Parameter changes in evacuation response rate and results of evacuation number. Evacuation response rate

Case

t=1

t=2

t=3

Number of people evacuated

Number of people not evacuated

Case 1 Benchmark case Case 2

0.3 0.5 1.0

0.3 0.7 1.0

0.3 1.0 1.0

1560 2450 2600

1640 750 600

Table 4.

Parameter changes of the capacity limit of the shelter and results of evacuation number.

Case

Capacity limit of the shelter

Number of people evacuated

Number of people not evacuated

Case 1 Benchmark case Case 2

400 m2 800 m2 1600 m2

2000 2450 2450

1200 750 750

Figure 2.

Impact of different evacuation response rates on the number of evacuees.

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Figure 3.

Evacuation results under different capacities of shelters.

The experimental results in Figure 2 are obvious. With the improvement in the evacuation response rate, the total number of people who did not evacuate showed a downward trend. From the benchmark case to Case 1, the number of people who did not evacuate increased by 890. When the evacuation response rate in each period reaches 1.0, the number of people who have not evacuated is only 150 less than the base case. At this time, the reason for limiting the number of people who have not evacuated is no longer the evacuation response rate, but the distance affects the decision-making choices of the masses. Figure 3 shows the evacuation results under 400m2, 800m2, and 1600m2 respectively. We can see that when the capacity of facilities decreases, many people will not evacuate because there is no place to go. When the capacity of refuge points is excessively increased, more people will not be evacuated. In two different groups of experiments, some people chose to avoid danger on the spot because they were too far away. Therefore, in the actual rescue situation, reducing the shelter capacity will reduce the evacuation efficiency, but the capacity will be too large, and will not receive all the victims indefinitely, at the same time, it will increase excessive economic costs, which provides a reference for emergency managers when formulating actual emergency plans.

5 CONCLUSIONS This paper aims at the evacuation cost, relief supplies purchase cost, transportation cost, and punishment cost of non-evacuated people in the emergency system, and constructs a dynamic programming model to study the multi-link emergency problems such as mass evacuation and supplies transportation. The model was solved by programming on the GAMS platform, and a series of numerical experiments were conducted on the model through the 17-point experimental road network. The experimental results show that improving the mass evacuation response rate can better organize an evacuation, and the number of evacuees is also affected by other factors, such as travel distance; Increasing the capacity of shelters can increase the number of evacuees to some extent, but the excessive capacity of shelters will cause waste and cost too much economic cost, which provides decision support for decision-makers when making emergency plans. In addition, the model construction of this paper is still simplified, focusing on multiple interests, establishing a multi-objective model, and considering the uncertainty of facility location, relief supplies demand, and other aspects are feasible directions for future research.

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ACKNOWLEDGMENT This research was supported by the National Natural Science Foundation of China (NSFC) (Grant # 71874069) and the Youth Innovation Support Program of Shandong Colleges and Universities.

REFERENCES Bai X.J. (2015) Optimization for Pre-positioning Emergency Supplies Problem Under Fuzzy Environment. Systems Engineering-Theory & Practice, 35: 1465–1473. Camur M.C., Sharkey T.C., Dorsey C., Grabowski M.R. and Wallace W.A. (2021) Optimizing the Response for Arctic Mass Rescue Events. Transportation Research Part E: Logistics and Transportation Review, 152. Cui X., Ding Y. and Ling G. L. (2014) Study on Evacuation Model Considering the Psychological Cost of Waiting in Disaster Relief. Science Technology and Engineering, 14: 309–314. Duan X.H., Wu J.X. and Zhou Z.Q. (2020) Collaborative Decision-making of Emergency Vehicle Scheduling and Traffic Evacuation Based on Bi-level Bat Algorithm. Journal of Transportation Systems Engineering and Information Technology, 20: 157–165. Kamyabniya A., Noormohammadzadeh Z., Sauré A. and Patrick J. (2021) A Robust Integrated Logistics Model for Age-based Multi-group Platelets in Disaster Relief Operations. Transportation Research Part E: Logistics and Transportation Review, 152. Rambha T., Nozick L.K., Davidson R., Yi W.Q. and Yang K. (2021) A Stochastic Optimization Model for Staged Hospital Evacuation During Hurricanes. Transportation Research Part E: Logistics and Transportation Review, 151. Shahparvari S., Chhetri P., Abbasi B. and Abareshi A. (2016) Enhancing Emergency Evacuation Response of Late Evacuees: Revisiting the Case of Australian Black Saturday Bushfire. Transportation Research Part E: Logistics and Transportation Review, 93. Sun D., Song Y. and Su Z.F. (2019) Research on Optimization of Regional Evacuation Warning Model. Operations Research and Management Science., 28: 11–18. Zhao C.L., He S.S., Sun S.M.and Wang Y.H. (2022) Bi-level Optimization Model in Transportation Evacuation Network Based on Rational Inattention. Journal of Transportation Systems Engineering and Information Technology, 22: 239–246. Zhang B.S., Fu W.B., Tang P., Shan S.Q. and Yin R.F. (2019) Collaborative Scheduling Optimization Model for Community Evacuation Under Demand Uncertainty Condition. Operations Research and Management Science, 28: 93–99. Zhu L., Cao J., Gu J. and Zheng Y. (2020) Dynamic Emergency Supply Distribution Considering Fair Mitigation of Victim Suffering. Systems Engineering-Theory & Practice, 40: 2427–2437.

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Advances in Traffic Transportation and Civil Architecture – Liu, Qi & Law (eds) © 2023 copyright the Author(s), ISBN 978-1-032-51438-3

Case study of cathodic protection of concrete piles to extend the service life of marine jetty Haihong Li*, Xiang Fang & Chunlin Deng CCCC Fourth Harbour Engineering Institution Co., Ltd., Guangzhou, China

ABSTRACT: To solve the problem of corrosion of chloride ions existing in steel bars during underwater repair of reinforced concrete pile foundations, a complete construction technology of cathodic protection is proposed to extend the service life of the pile after repair. The technology uses a self-designed sacrificial anode framework with high-efficiency features. Underwater welding operations are substantially reduced, as well as to ensure stable and reliable construction quality. The practice of a project in Zhanjiang Port proved that the cathodic protection system had provided adequate protection on the pile foundation.

1 INTRODUCTION Due to the influence of waves and loads, prestressed reinforced concrete piles that have been in service for a long time are prone to defects in the concrete cover, such as cracks, thereby providing a convenient path for the intrusion of chloride ions (Bertolini et al. 2002; Lau et al. 2007; Liam et al. 1992; Shao et al. 2022). In practice, due to the construction quality problems of the newly built cast-in-place concrete piles, after the steel casing is removed, the concrete cover is also prone to insufficient thickness or partial defects, which seriously affects the service life of the reinforced concrete pile foundation. For the repair of underwater defects of concrete piles, the traditional method is to use local repair methods such as crack repair and pouring of underwater concrete or polymer composite (Kim et al. 2009; Khayat et al. 1997; Sen & Mullins 2007; Yi et al. 2010). The concrete piles repaired by these methods cannot remove the chloride ions infiltrated into the concrete piles. They cannot prevent the corroded steel bars from continuing to corrode. Moreover, due to the difference in corrosion potential, the steel bars near the repaired area are prone to galvanic corrosion, resulting in accelerated corrosion of the steel bars near the repaired area (Andrade et al. 1992; Lliso-Ferrando et al. 2022). Therefore, to prevent the steel bars from continuing to corrode or local accelerated galvanic corrosion and to ensure the long-term durability of the concrete piles after repair, it is necessary to apply cathodic protection for the steel bars of concrete piles. In this paper, based on the sacrificial anode cathodic protection for steel structures, combined with the characteristics of concrete piles, a practical cathodic protection construction technology for the underwater area of concrete piles is proposed. Engineering practice has proved that effective cathodic protection has been achieved, ensuring the durability of the concrete pile foundation in the underwater area.

*

Corresponding Author: [email protected]

140

DOI: 10.1201/9781003402220-16

2 METHODS AND MATERIALS In the rehabilitation and retrofit project of a jetty in Zhanjiang Port, the beam slabs of the original wharf need to be removed, and a new concrete superstructure should be built. To reduce the project budget, the pile foundation should use the original concrete piles as much as possible. According to the pile foundation test results, the concrete piles’ surface appearance is overall in good condition. However, the original pile foundation has been used for nearly 30 years. The estimated remaining service life is about 20 years, according to the durability inspection and calculation, which cannot meet the 50-year design service life requirement after the renovation. As a result, a hybrid repair method is proposed according to the concrete pile’s characteristics. For the concrete pile above the design elevation + 1.50 m, the old concrete is removed, and new concrete shall be re-casted. And for the underwater area of piles below the elevation + 1.50 m, a sacrificial anode cathodic protection system is applied to extend the service life of the marine jetty. The sacrificial anodes of the cathodic protection system adopt aluminum-zinc-indiummagnesium-titanium alloys with two different types, as shown in Table 1. The design life of the anode is 25 years and can be upgraded once again to fulfill the 50-year design service life. Totally 525 concrete square piles with a side length of 50 cm are protected. A typical pile group consists of 10 concrete piles numbered from A to K from the seaside to the landside. The anode arrangement is shown in Figure 1. Table 1.

Datasheet of sacrificial anodes.

Type

Length/mm

Width/mm

Height/mm

Weight/Kg

Quantity/pcs

I II

600 900

200 (top)+240 (bottom) 220 (top)+240 (bottom)

160 180

65.5 105.6

305 240

Figure 1.

Typical layout of sacrificial anodes.

3 APPLICATION OF CATHODIC PROTECTION SYSTEM 3.1

Electrical connection of steel bars

For all cathodically protected concrete piles, the electrical connection resistance between steel bars should be less than 1 W (BS EN ISO 12696:2022, 2022). When the concrete square pile head is exposed after the old concrete is removed, 4*40 mm flat sheets of steel are used to

141

weld and electrically connect the main steel bars between the concrete square piles. Before pouring new superstructure concrete, we check the electrical connectivity by on-site testing the electrical resistance or potential difference between the piles. To ensure cathodic protection current generated by the sacrificial anode reaches the steel bars of the concrete square piles, an electrical connection also should be established between them. For this purpose, steel bars with a diameter of 16 mm are welded to the main steel bars in a pile, drawn out to the outside of the square pile head, and extended along the surface of the square pile to the installation position of the sacrificial anode, as shown in Figure 2. To ensure the reliability of the electrical connection of the sacrificial anode, two steel bars are used for redundancy.

Figure 2.

3.2

Electrical connection steel bars exposed on the square pile surface.

Design and fabrication of sacrificial anode framework

For underwater installation of sacrificial anodes for concrete piles, it can be utilized by mounting on the surface of the piles or laying in the bottom of the seabed near the piles. Due to the current shielding effect and the influence of sediment siltation which will affect the current output, the latter is generally only used in areas with insufficient water depth. Considering that the section size of the concrete square pile is relatively small and the weight of the anode is not large, it is an ideal choice to use a special anode bracket to fix the anode on the surface of the square pile. For this purpose, a simple and reliable anode framework is designed for this project, as shown in Figure 3. The anode bracket is composed of two half squares connected by fasteners. Depending on the weight of the anode, reinforcing ribs can be attached on both sides of the connecting plate to avoid deformation and difficulty in fastening and fitting. The framework is produced in the workshop, which has high work efficiency and is convenient for mass production. At the same time, the fastening connection is adopted, which is convenient for on-site assembly. By adjusting the length of the fastening screw, the difficulty of matching caused by the section size deviation of the concrete square piles can be effectively avoided, which greatly enhances its practicability. 3.3

Anode assembly

The assembly of the sacrificial anode and the bracket is realized by welding. The welding seam between the anode steel core and the bracket is a circular welding seam welded on the construction site above the water surface. This method ensures stable and reliable welding quality and high work efficiency compared to traditional underwater welding. At the same 142

Figure 3.

Schematic diagram of the installed sacrificial anode framework.

Figure 4.

The welded anode assembly on site.

time, the underwater operation time is greatly reduced, and the diving cost is significantly decreased. Figure 4 shows the finished welded anode assembly on site. 3.4

Underwater installation of sacrificial anode

Since the square piles’ surface condition greatly influences their close fit with the anode bracket, the surface quality at the installation area must be carefully checked and prepared before the anode is installed. For the marine organisms attached to the concrete surface, steel

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shovels are used to chisel away, and then the surface is prepared with underwater pneumatic grinding tools. After the anode framework is installed and fastened on the pile, an electrical connection is established by welding the steel bar to the anode bracket. The electrical connection can also be achieved by using special bolt joints to avoid underwater welding operations completely. 3.5

Cathodic protection potential measurement

The cathodic protection potential is the most direct parameter to evaluate the cathodic protection effect of concrete piles. One month after the installation of the sacrificial anodes, the cathodic protection potentials are measured on the concrete pile, as shown in Table 2. Generally, steel bars of concrete square piles are prestressed steels, and the protection potential criteria specified by the standard is between -0.72 -0.90 V (relative to the silver/ silver chloride/seawater reference electrode) (BS EN ISO 12696:2022 2022). The measured protection potentials meet the requirements of the specification. Thus, the steel bars in the square pile are in a good cathodic protection state, which shall extend the service life of the marine jetty up to 50 years, according to the design.

Table 2.

Measured cathodic protection potential of typical pile group.

Pile Designation

A

B

C

D

E

F

G

Potential/V

–872

–880

–894

–883

–889

–876

–868

4 CONCLUSION 1. A cathodic protection system is proposed and successfully implemented to extend the service life of concrete pile foundations. 2. The self-designed sacrificial anode framework is simple and practical with high work efficiency, greatly reducing underwater operations and ensuring stable and reliable construction quality. 3. The cathodic protection potential survey shows that the pile foundation is protected well.

REFERENCES Andrade C., Maribona I.R., Feliu S., González J.A., & Feliu Jr S. (1992). The Effect of Macrocells Between Active and Passive Areas of Steel Reinforcements. Corrosion Science, 33(2), 237–249. Bertolini L., Gastaldi M., Pedeferri M., & Redaelli E. (2002). Prevention of Steel Corrosion in Concrete Exposed to Seawater with Submerged Sacrificial Anodes. Corrosion Science, 44(7), 1497–1513. BS EN ISO 12696:2022. Cathodic Protection of Steel in Concrete[S]. Khayat K.H., Ballivy G., & Gaudreault M. (1997). High-performance Cement Grout for Underwater Crack Injection. Canadian Journal of Civil Engineering, 24(3), 405–418. Kim S.B., Yi N.H., Phan H.D., Nam J.W., & Kim J.H.J. (2009). Development of Aqua Epoxy for Repair and Strengthening RC Structural Members Underwater. Construction and Building Materials, 23(9), 3079– 3086. Lau K., Sagues A.A., Yao L., & Powers R.G. (2007). Corrosion Performance of Concrete Cylinder Piles. Corrosion, 63(4), 366–378. Lliso-Ferrando J.R., Gasch I., Martínez-Ibernón A., & Valcuende M. (2022). Effect of Macrocell Currents on Rebar Corrosion in Reinforced Concrete Structures Exposed to a Marine Environment. Ocean Engineering, 257, 111680.

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Liam K.C., Roy S.K., & North wood D.O. (1992). Chloride Ingress Measurements and Corrosion Potential Mapping Study of a 24-year-old Reinforced Concrete Jetty Structure in a Tropical Marine Environment. Magazine of Concrete Research, 44(160), 205–215. Shao W., Wang Y., & Shi D. (2022). Corrosion-fatigue Life Prediction of Reinforced Concrete Square Piles in Marine Environments. Engineering Failure Analysis, 138, 106324. Sen R., & Mullins G. (2007). Application of FRP Composites for Underwater Piles Repair. Composites Part B: Engineering, 38(5–6), 751–758. Yi N.H., Nam J.W., Kim S.B., Kim I.S., & Kim J.H.J. (2010). Evaluation of Material and Structural Performances of Developed Aqua-advanced-FRP for Retrofitting of Underwater Concrete Structural Members. Construction and Building Materials, 24(4), 566–576.

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Advances in Traffic Transportation and Civil Architecture – Liu, Qi & Law (eds) © 2023 copyright the Author(s), ISBN 978-1-032-51438-3

Comparison of carbon and pollution emissions of conventional diesel and new energy short-haul heavy-duty trucks over their full life cycle based on the GREET model and assessment of influencing factors Huangchen Luo* Chang’an Dublin International College of Transportation at Chang’an University, Chang’an University, Xi’an, China

ABSTRACT: With the lack of energy and the aggravation of environmental hardship, road traffic as a major source of pollution emissions in the transport sector has attracted extensive attention from scholars, and new energy vehicles have been studied extensively as an effective means of addressing emissions. As heavy-duty vehicles are less used in daily life, there are not many studies on the new energy emissions of heavy-duty vehicles. This paper compares the carbon emission data of new energy heavy-duty vehicles based on different energy sources in the Greet model at different stages of their complete life cycle and finds that gaseous hydrogen and liquid natural gas energy sources have lower emissions overall, traditional fuel oil, and natural gas heavy-duty vehicles. New energy heavy-duty vehicles have little difference in pollution emissions and can continue to be used in daily life, while biomass fuels have higher emissions in the fuel transportation stage. Further research is needed on their origin and extraction methods.

1 INTRODUCTION With global warming and environmental pollution intensifying in recent years, energy shortage, environmental pollution, and carbon emissions have gradually become hot topics of academic research (Chen & Lin 2019). Road traffic is the major source of pollution and carbon emissions in the transportation industry, so how to achieve energy saving and emission reduction for vehicles has naturally received a lot of attention from scholars. According to Su, among the energy structures of the automotive industry, new energy vehicles (NEVs) have long been one of the most effective means used by governments to reduce air pollution (Su et al. 2021), and by developing new energy markets and popularizing the use of new energy vehicles, road traffic pollution emissions and carbon emissions can be effectively reduced. However, there are still many scholars who doubt that the massive use of new energy vehicles can effectively reduce emissions (Huo et al. 2010). Heavy vehicles, as the main source of particulate matter emissions from road traffic in China, account for more than 90% of particulate matter emissions. Because of their heavy weight, the pollution emissions caused by their brakes are also one of the main sources of non-tailpipe pollution emissions, and as heavy-duty vehicles are less used daily, research on their new energy sources is also rare. Emissions from heavy vehicles are naturally the focus of this research. This study is based on the collection and comparative assessment of carbon and pollution *

Corresponding Author: [email protected]

146

DOI: 10.1201/9781003402220-17

emissions over the complete life cycle of conventional fuel vehicles and new energy shorthaul heavy-duty trucks from the GREET model developed by the US Department of Energy’s Argonne National Laboratory. The GREET model is a spreadsheet-based fuel cycle model developed by Argonne Labs in 1995 with support from the U.S. Department of Energy’s Office of Transportation to replace conventional transportation fuels, promote advanced vehicle technologies to reduce U.S. dependence on imported oil, reduce greenhouse gas emissions, and address urban air pollution (Wang 2000). There is a large amount of publicly available life-cycle data in the GREET model that can be collected for research. In this research, emissions of carbon dioxide, nitrogen oxides, PM2.5, and PM10 from different energy sources were collected for short-haul heavy-duty trucks of 1.8t and above in the same scenario, at the vehicle manufacturing, fuel transportation, vehicle operation, and end-of-life stages. In this research, low-sulfur diesel, liquefied natural gas, gaseous hydrogen, electricity, corn straw, soybean fuel, and dimethyl ether fuel were selected as the energy sources to compare the collected emission factors to determine which energy source can maximize the reduction of carbon and pollution emissions from short-haul heavy-duty trucks and thus more effectively achieve the goal of carbon neutrality and carbon peaking. The research found that modified LNG and gaseous hydrogen vehicles performed well over their full life cycle. Electric heavy-duty vehicles account for a larger share of upstream emissions and emit more polluting gases than conventional fueled heavy-duty vehicles. In addition, the high emissions from the WTW (well-to-wheel) phase of biomass fuels for heavy-duty vehicles indicate that more mature methods are needed for the extraction of biomass fuels and the selection of the origin. The future market for biomass fuels is very promising.

2 RESULTS 2.1

Research analysis

In general, it is clear that greenhouse gas emissions far exceed their pollution emissions, while NOx accounts for the majority of pollution emissions, and particulate matter emissions are relatively small. However, particulate pollutants are extremely harmful to the human body, and PM2.5 and PM10 can invade the human throat and alveoli respectively, causing great damage to the human body. Therefore, although the emissions are small, they still need to be taken seriously. The ADR emissions and carbon emissions are equal for all types of energy trucks, which means that the Car scrap recycling process is similar, and the emissions generated are very small, so this research does not analyze much of the relevant car scrap recycling data. 2.2

Introduction to the research subject and analysis of salient features

2.2.1 Low-sulfur diesel vehicles Most heavy-duty diesel vehicles use low-sulfur diesel, so the research selected low-sulfur diesel vehicles as a representative of traditional diesel vehicles for analysis. From Table 1, we can observe that the NOx and CO2 emissions of low-sulfur diesel vehicles are mainly in the Vehicle operation phase, while the particulate emissions are more evenly distributed, accounting for a larger proportion in the WTP phase and the vehicle manufacturing phase. 2.2.2 LNG natural gas vehicles The conventional gas market has changed significantly over the last 20 years and LNG is becoming a major driver of the market (Karasalihovic´ Sedlar et al. 2010; Ledenko et al. 2018; Nosic´ et al. 2017). LNG vehicles, as traditional fuel gas vehicles, can reduce

147

Table 1.

Carbon emissions and pollutant gas emissions from heavy-duty trucks. NOx (g/mile) PM10 (g/mile) PM2.5 (g/mile) CO2 (kg/mile)

Low-sulfur diesel WTP Operation only WTW Car manufacturing ADR Total

0.519 2.1 2.6 0.0344 0.00134 2.7

0.0379 0.00463 0.0426 0.0142 0.000218 0.0702

.0.0321 0.00426 0.0364 0.00676 0.000129 0.05

0.3796 2.2937 2.6706 0.029 0.0016 2.7306

LNG

WTP Operation only WTW Car manufacturing ADR Total

0.682 0.001 0.0017 0 0 0.0017

0.0256 0.00416 0.0298 0 0 0.0298

0.0237 0.0383 0.0275 0 0 0.0275

0.3454 1.7956 2.1411 0 0 2.1411

Gaseous H2

WTP Operation only WTW Car manufacturing ADR Total

0.559 0 0.559 0.017 0.00134 0.643

0.0777 0 0.0777 0.0164 0.000218 0.108

0.0645 0 0.0645 0.00733 0.000129 0.0787

1.679 0 1.679 0.0378 0.0016 1.7479

Electricity

WTP Operation only WTW Car manufacturing ADR Total

1.6 0 1.6 0.182 0.00134 1.8

0.32 0 0.32 0.0728 0.000218 0.407

0.145 0 0.145 0.0308 0.000129 0.182

2.0468 0 2.0468 0.1332 0.0016 2.2112

Dimethyl diesel

WTP Operation only WTW Car manufacturing ADR

0.853 2.1 3 0 0

0.13 0.00463 0.135 0 0

0.0865 0.00426 0.0908 0 0

1.8976 2.0276 0.0399 0 0

Total WTP Operation only WTW Car manufacturing ADR

3 0.537 2.1 2.6 0 0

0.135 0.0789 0.00463 0.0835 0 0

0.0908 0.0643 0.00426 .0.0686 0 0

0.0399 0.8072 2.2223 3.0295 0 0

Total WTP Operation only WTW Car manufacturing ADR Total

2.6 0.619 2.1 2.7 0 0 2.7

0.0835 0.0527 0.00463 0.0573 0 0 0.0573

0.0686 0.0421 0.00426 0.00464 0 0 0.00464

3.0295 0.5055 2.2223 2.7278 0 0 2.7278

Corn Stover

Soybean

greenhouse gas emissions by 10% compared to diesel at the end of their use (Smajla et al. 2019). As shown in Table 1, the emissions of pollutants and greenhouse gases are similar to those of low-sulfur diesel vehicles, but LNG has a higher economic effect than conventional diesel vehicles. The overall emissions compared to heavy-duty diesel vehicles show that natural gas vehicles have slightly lower emissions than heavy-duty diesel trucks.

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2.2.3 Gaseous hydrogen energy vehicles Unlike conventional diesel and liquid natural gas vehicles, gaseous hydrogen is a clean energy source that does not produce any emissions during the operation of the vehicle, which is a very good feature. However, as Table 1 shows, gaseous hydrogen energy generates relatively high levels of pollutants and greenhouse gases during fuel transport, accounting for a significant proportion of its total emissions. 2.2.4 Electric vehicles Electricity, as one of the earlier developed new energy sources, has a relatively mature industry related to its light-duty vehicles, but there is less research related to electric heavyduty trucks. From Table 1, it can be found that electricity, like gaseous hydrogen energy, as a clean energy source, does not cause any pollution emissions during the operation of the vehicle. Similarly, the pollutant gas and greenhouse gas emissions produced by electric heavy-duty trucks during fuel transportation are larger in total emissions as a percentage of total emissions, which shows that electric heavy-duty vehicles and gaseous hydrogen heavyduty vehicles are both clean energy heavy-duty vehicles with relatively similar emissions over the complete life cycle of the vehicle. 2.2.5 Biomass fuelled vehicles The increasing emissions of greenhouse gases and their negative effects have sparked an increasing interest in biomass fuels. Biomass fuels have the advantages of being biodegradable, low toxicity, and abundant resources. Compared to low-sulfur diesel, biomass diesel can also provide good lubricity, which can greatly reduce the wear and tear of engine components due to friction. At the same time, biomass fuel can be used directly without any modification to the vehicle engine or other components and is a direct alternative fuel to diesel. (Knothe et al. 2005; Pullen & Saeed 2014). Therefore, biomass fuels are of great importance for the further development of new energy heavy-duty vehicles, so three new energy vehicles were selected for the collection of emission factors in this study. These are dimethyl ether, corn straw, and soybean fuel. From Table 1, it can be seen that dimethyl ether fuel diesel has a negative CO2 during fuel transport, as the dimethyl ether fuel diesel selected for this study is derived from corn stover, which can achieve near zero carbon under ideal conditions, so the greenhouse gas emissions of DME diesel can be found to be ideal. The two biomass fuels, namely, corn stover and soybean, can be found to have some similarities, as they are different biomass fuels taken from the same scenario. The emission data for the vehicle operation phase is the same for both fuels, but after comparison, it can be found that the overall emission of soybean is slightly lower than that of corn stover, probably because the soybean-derived biodiesel contains 10% by weight of oxygen (Özener et al. 2014), which can help the engine to burn more fully and reduce the cold start pollution emission of heavy vehicles. 2.3

Data comparison

Table 1 is used for the analysis of different energy characteristics only due to the large amount of data and the difficulty to understand the analysis visually, which has been processed in this paper. In this research, the complete life cycle of a truck is divided into four stages: fuel transport (WTP), vehicle operation (Operation), car manufacturing, and car scrapping and recycling (ADR). Table 1 shows that the CO2 emissions from vehicles are much larger than the pollutant gas emissions from vehicles. This research has separated CO2 emissions from pollutant gases for cross-sectional comparison. The difference in the order of magnitude between NOx and particulate emissions PM2.5 and PM10 is large, about 1000 times larger in the vehicle 149

operation phase. This research normalizes the same pollutant emissions from different energy sources of heavy short-haul trucks at the same stage for better analysis. 2.4

Comparative analysis

Figures 1 to 4 represent the comparison of pollutant emissions of different energy vehicles over their entire life cycle. As can be seen from Figure 4, the pollutant emissions of traditional low-sulfur diesel trucks are not significant compared to new energy-heavy trucks, and modified LNG vehicles perform well in terms of emissions over the entire life cycle of the vehicle, especially the emission share of particulate matter is very small compared to other energy vehicles. The study conjectures today’s research on traditional diesel heavy trucks. Emission reduction studies are more frequent and largely reduce the emissions of conventional diesel heavy trucks. As can be seen from Figure 3, except for gaseous hydrogen energy and electric energy, which have zero emissions during the vehicle operation phase, there is not much difference between the pollutant gas emissions of the trucks with different energy sources during the vehicle operation phase, so this study does not do too much analysis on this phase.

Figure 1.

WTP phase pollutant gas emissions.

Figure 2.

Car manufacturing phase pollutant gas emissions.

150

Figure 3.

Operation phase pollutant gas emissions.

Figure 4.

Total pollutant gas emissions.

Electricity as a more mature new clean energy vehicle, with zero emissions during vehicle operation, should ideally result in a significant reduction in total pollution emissions over the complete life cycle of an electric heavy-duty vehicle. However, as can be seen from Figures 1 and 2, electric vehicles emit too much pollution during fuel transportation and vehicle manufacturing, resulting in higher total particulate emissions than other energy vehicles. Gaseous hydrogen, which is also a clean energy source, emits far less particulate matter and NOx than electric vehicles, with studies showing that G.H2 reduces greenhouse gas emissions by 20–45%, NOx emissions by 62–83%, PM10 by 19–43%, and PM2.5 by 27–44% during the WTW process (Lee et al. 2018). In the context of imperfect research on biomass fuels, both gaseous hydrogen vehicles and electric vehicles have great potential for the future automotive market. Biomass fuels are emerging new energy sources and as a direct replacement for conventional diesel, their emission reductions are expected to be high research that is enthusiastic, but as can be seen from Figure 5, emissions from corn stover, soybean fuel, and DME diesel are not low and still account for a high proportion of total emissions during vehicle operation and fuel transport. This may be because the current method of extraction of biomass fuels is relatively simple and has many drawbacks, for example, the extraction of bio-oil from maize stover is the conventional rapid cracking method, but this method has a serious disadvantage that the extracted biomass fuel has a high oxygen content and high moisture content and therefore needs to be improved. This study suggests that hydrocracking could be used to reduce these disadvantages. 151

Figure 5.

Truck CO2 emissions over the complete life cycle.

In terms of CO2 greenhouse gas emissions from heavy-duty vehicles, it is clear that conventional fuel-fired vehicles produce more CO2 during the operational phase, while clean energy vehicles such as gaseous hydrogen and electric vehicles produce more carbon emissions during the fuel transport phase. The total emissions from electric and gaseous hydrogen vehicles are relatively low compared to biomass fuels such as soybeans and corn stover. At the same time, biomass fuels, due to their imperfect extraction methods, contain a large amount of oxygen and water in the biomass diesel, which generates more CO2 during the vehicle operation phase. It is worth drawing attention to the fact that the greenhouse gas emissions of dimethyl ether fuel, which is also a biomass fuel, are much lower than those of other fuels, as dimethyl ether has very low CO2 emissions during fuel transport and acts directly on the diesel engine, a very efficient way of operating, which also makes dimethyl ether fuel tend to have zero carbon emissions over the complete life cycle of the vehicle. Apart from the specificity of DME fuels, it can be seen from Figure 4 that, gaseous hydrogen energy vehicles perform the best in terms of total carbon emissions, probably because petroleum liquefaction is the second largest contributor to life-cycle greenhouse gases and the use of gaseous hydrogen energy reduces liquefaction greenhouse gas emissions by 19–45% (Lee et al. 2018) and is responsible for the lower overall G.H2 greenhouse gas emissions.

3 CONCLUSION Gaseous hydrogen trucks have low overall emissions, and as one of the leading new energy sources, the use of solar and wind energy for hydrogen production is one of the technologies of interest for the future, as it can significantly reduce fuel consumption and pollution emissions for fuel transport. Electric trucks, which also use clean energy, currently have higher emissions than heavyduty vehicles, probably due to the difference in the difficulty of generating electricity in different areas. In colder climates, the energy loss and gas emissions caused by electricity generation are much greater than in warmer climates, which is one of the reasons why electric vehicles have much higher particulate emissions than other energy vehicles. Secondly, the research found that the difference in emissions between conventional fuel, natural gas trucks, and new energy vehicles is not significant and that conventional fuel trucks can continue to be used even though new energy technologies are not very advanced 152

today. In particular, LNG heavy trucks have significantly lower emissions than electric and biomass-fueled vehicles. Biomass fuels are a valuable fuel for future research, but today’s research and extraction methods are not mature, so the corresponding data collected is high in terms of emissions. But biomass fuels are more resourceful and have renewable properties that traditional energy sources do not have, which are well worth studying in depth. After comparing the data in this paper, it was found that the emissions of biomass-fueled trucks are relatively similar to those of conventional fuel trucks in the operational phase. It is worth drawing attention to the fact that biomass fuels produce large emissions during the fuel transportation phase, and this paper suggests that this problem can be solved by starting with the extraction method and the type of feedstock and trying to adopt the hydrocracking method instead of using the traditional electrolysis method, which will achieve better.

REFERENCES Chen S.Y. and Lin B.Q., “Current Status and Prospects of Research on the Economics of Energy, Environment and Climate Change in China – Review of the First China Energy, Environment and Climate Change Economics Scholars Forum,” Economic Research, vol. 07 pp. 203–208, May 2019. Huo H., Zhang Q., Wang M., Streets D. and He K., “Environmental Implication of Electric Vehicles in China”. Environmental Science & Technology, vol. 44, no. 13, pp. 4856–4861, 2010. Karasalihovi´c Sedlar D., Hrnˇcevi´c L. and Brki´c V., “Impact of Unconventional Gas Production on LNG Supply and Demand”. Rudarsko-geološko-naftni zbornik, vol. 22, pp. 37–45, 2010. Knothe G., Krahl J. and Van Gerpen J., The Biodiesel Handbook. Elsevier, 2005. Ledenko M., Veli´c J. and Karasalihovi´c Sedlar D., “Analysis of Oil Reserves, Production and Oil Price Trends in 1995, 2005, 2015”. Rudarsko-geološko-naftni zbornik, vol. 33, p. 42, 2018. Lee D., Elgowainy A., Kotz A., Vijayagopal R. and Marcinkoski J., “Life-Cycle Implications of Hydrogen Fuel Cell Electric Vehicle Technology for Medium- and Heavy-Duty Trucks”. Journal of Power Sources, vol. 393, pp. 217–229, 2018. Nosi´c A., Sedlar D.K. and Juki´c L. “Oil and Gas Futures and Options Market”. Rudarsko-geološko-naftni zbornik, vol. 32, p. 38, 2017. Özener O., Yüksek L., Ergenç A. and Özkan M., “Effects of Soybean Biodiesel on a DI Diesel Engine Performance, Emission and Combustion Characteristics”. Fuel, vol. 115, pp. 875–883, 2014. Pullen J. and Saeed K., “Factors Affecting Biodiesel Engine Performance and Exhaust Emissions – Part I: Review”. Energy, vol. 72, pp. 1–16, 2014. Smajla I., Karasalihovi´c Sedlar D., Drljaˇca B. and Juki´c L., “Fuel Switch to LNG in Heavy Truck Traffic”. Energies, vol. 12, no. 3, p. 515, 2019. Available: 10.3390/en12030515. Su C., Yuan X., Tao R. and Umar M., “Can New Energy Vehicles Help to Achieve Carbon Neutrality Targets?” Journal of Environmental Management, vol. 297, p. 113348, 2021. Wang M. “The Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) Model Version 1.5”. 2000.

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Advances in Traffic Transportation and Civil Architecture – Liu, Qi & Law (eds) © 2023 copyright the Author(s), ISBN 978-1-032-51438-3

A cellular automata model with simulation-based optimization for multi-lane expressway Mo Chen* School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China

ABSTRACT: Microscopic traffic simulation models can represent individual vehicles and account for the correlative attributes, among which the cellular automata (CA) model can simulate and explain complex traffic phenomena with high simulation efficiency by defining a series of simple rules. However, at present, there are not enough reasonable multi-lane cellular automata models accompanied by the proper calibration methods for the simulation and calibration simultaneously of expressway traffic flow. Therefore, this paper develops a multi-lane cellular automata model from the microscopic aspect, and meanwhile uses the simulation-based optimization (SO) method to calibrate the parameter of the model. For verification, the vehicle data of a super bridge and a segment of an expressway are simulated, and the related parameter is calibrated. The results show that the model can simulate and provide a reasonable value of the probability of vehicles’ random slowdown behavior to better reflect the traffic situation.

1 INTRODUCTION The application of intelligent traffic technology draws the attention of multiple scientists to develop and calibrate traffic models to reflect the traffic phenomenon. In contrast with the macroscopic and mesoscopic models, microscopic models focus on the behavior of the individual vehicles and describe the driving behavior and interaction of each vehicle at every moment in the traffic system in a more detailed way. Generally, the micro-simulation models consist of two components: the exact description of the road networks, especially the geometric conditions and the transportation infrastructure, and the precise dynamic simulation of the behaviors of the vehicles generally influenced by the behaviors of their drivers. As one of the micro-simulation models, the Cellular Automata (CA) model, as well as its improvement and application to the simulation of microscopic traffic flow, has become increasingly popular. Among the studies, the simulation of the vehicles driving on the expressway is one of the key problems. NS or NaSch model (Nagel & Schreckenberg 1992) is the basic model which introduces four classic update principles including acceleration, slowing down, randomization, and car motion. Lárraga et al. (2005) developed a single-lane cellular automata model to simulate the highway with the closed boundary approach. Zeng et al. (2017) proposed a two-lane cellular automata model to simulate the expressway under two conditions that are the lane-changing rule and the speed-limit rule. However, on one hand, the rules of the multi-lane CA model typically when the number of lanes is more than two are not reasonable enough. On the other hand, few studies focus on operating simulation or calibration simultaneously in one framework to gain a more convenient process. Hence, this research not only focuses on building a new micro-simulation model, but also tries to provide a method for simultaneously calibrating the parameters of the developed model. *

Corresponding Author: [email protected]

154

DOI: 10.1201/9781003402220-18

Although some scholars have selected methods to calibrate the CA model (Korcek et al. 2012; Zhao et al. 2012), the application of the simulation-based optimization (SO) method for the calibration of the CA model has been overlooked. Several researchers have used the SO method to solve various transportation problems (Chen et al. 2016; Gu & Saberi 2021; Osorio & Bierlaire 2013) because of its feasibility for solving the system that lacks explicit mathematical formulation (Gu & Saberi 2021). Therefore, this research mainly focuses on three aspects: (a) A simulation model for the multi-lane expressway based on the CA model with rules and principles of the lane-changing and car-following process; (b) The simulationbased optimization method is applied for the calibration of the parameter that can be implemented without explicitly providing the exact analytical formula for the relationships between input and output; (c) A framework for simulation and calibration is proposed and applied on a case study to evaluate and analyze the performance. 2 MULTI-LANE CELLULAR AUTOMATA MODEL Generally, the developed multi-lane CA model contains input parameters, the evolution process, and output results. The traffic road system of the model consists of three parallel lanes in the same direction as shown in Figure 1. Each lane is regarded as a one-dimensional discrete lattice chain with a total length of L to simulate the traffic road. For the cell length l, considering the minimum space headway between vehicles in jam, it supposes that each cell represents 7.5 m, which is the inverse of the jam density (Kj = 133 veh/km). Furtherly, the number of cells can be decided by dividing road length L by cell length l. Each cell is empty or can only be occupied by one vehicle at one timestep to simulate the motion of the vehicles. The timestep is relatively arbitrary in value, which usually uses the driver reaction time that lies between 0.6 and 1.2s, so the timestep is chosen as one second in the model. The total evolution time is related to the timestep and the actual time needed for simulation. Moreover, it assumes that the maximum speed vi,max of vehicles is corresponding to the speed limit of each lane i of the simulated expressway. Given that the lane number parameter can be adjusted, the model can also accomplish the simulation for the other lane number. In the evolution, it contains the arrival of vehicles, the lane-changing process, and the carfollowing process. An additional assumption of the model is that no ramp exists on the road section of this traffic system.

Figure 1.

2.1

Simplification diagram of CA road system.

The arrival of vehicles and boundary conditions

Before updating, it initializes the position and speed of vehicles on the road. The initial position of vehicles in the model is mainly distributed by the principle of Poisson Distribution. Road vehicle initialization not only determines the initial vehicle position but also allocates the speed of each vehicle. In traffic measurement, researchers found that the 155

distribution of vehicle speed on the road conforms to the normal distribution, so the speed is allocated according to the normal distribution, and then rounded to determine the initialization velocity. However, the rules for the arrival of vehicles are only operated when there is no accurate relative traffic data. If the vehicles and their speed are determined, the arrival of vehicles is in line with the data. Besides, the model adopts periodic boundary conditions. The observed linear road system is regarded as a ring road. Every time the system completes a state update, it will compare the position of the first vehicle on the road with the length of the road section. If the updated position of the first vehicle is greater than the length of the lane, the vehicle will re-enter the observation road section from the end of the road and become the last vehicle in the system, while the speed of the vehicle is the new speed after the update. Simultaneously, the following vehicle becomes the first vehicle of the system after the update. 2.2

Car-following process

In actual driving conditions, the vehicles behind following the front vehicles constantly change their driving state, including constantly adjusting the acceleration, deceleration, or constant speed of the vehicle according to the change in the distance between the two vehicles. On one hand, a certain distance must be maintained between the two vehicles. When the front vehicle suddenly brakes, the driver of the rear vehicle needs to have a reflection time that has been considered in the timestep mentioned before. On the other hand, once the front vehicle changes its running state, such as accelerating and decelerating, the following vehicle will go through a state change. The following vehicles on the road network may be influenced by the state change, which is the transitivity in the car-following process. Considering these conditions, for the car-following process in the proposed model, the vehicle updates its speed and position at each timestep. The vehicles adjust their speeds at the next timestep (vt+1) with the limit of the maximum speed vi,max of each lane, the current speed vt, and the distance d in front. The state change of the vehicles can be simplified in (1). ( vtþ1 ¼ vt þ 1; ifd  vt þ 1 (1) vtþ1 ¼ d; ifd < vt þ 1 Additionally, for the consideration of safety, it introduces the probability of random slowdown pr for the consideration that drivers may be affected by some road environment and other factors. To be more specific, the velocity vt+1 of the vehicle, if greater than zero, is decreased by one with the probability pr. Then the position of the vehicle at the next timestep (vt+1) will be updated at each timestep according to (2). Xtþ1 ¼ Xt þ vtþ1 2.3

(2)

Lane-changing process

It is generally believed that lane-changing behavior is the response to the surrounding environment and achieves continuous follow-up by changing the driving road of vehicles. In the whole process of changing lanes, the vehicles are driven following the principles of overtaking and safety. 2.3.1 Lane-changing rules for lane-1 The distance between the target vehicle in lane-1 and its rear vehicle in lane-2 is d1, and the distance between the target vehicle in lane-1 and the front vehicle in lane-2 is d2. When d2 is greater than or equal to the speed vt+1 of the target vehicle at the next timestep, meanwhile, the sum of d1 and vt+1 is great than the speed of the rear vehicle at the next timestep, the target vehicle in lane-1 could change to lane-2. If any condition is not satisfied, the lanechanging process cannot be realized. 156

2.3.2 Lane-changing rules for lane-2 The vehicles in lane-2 can change to the left lane-1 or the right lane-3. As for the left change, the distance between the target vehicle in lane-2 and the rear vehicle in lane-1 is d21, and the distance between the target vehicle in lane-2 and the front vehicle in lane-1 is d22. When d22 is greater than or equal to the speed vt+1 of the target vehicle at the next timestep and the sum of the rear distance d21 and vt+1 is great than the speed of the rear vehicle at the next timestep, the target vehicle in lane-2 could change to the lane-1 to accomplish the left lane change. As for the right change, the distance between the target vehicle in lane-2 and the rear vehicle in lane-3 is d23, and the distance between the target vehicle in lane-2 and the front vehicle in lane-3 is d24. When d24 is greater than or equal to the speed vt+1 of the target vehicle at the next timestep and the sum of the rear distance d23 and vt+1 is great than the speed of the rear vehicle at the next timestep, the target vehicle in lane-2 could change to the lane-3 to realize the right-lane change. Besides, it is necessary to point out that in the proposed lane-changing process of the middle lane, this paper provides the preference of the vehicle for right-lane changing, which means that if the vehicle is driving in the middle lane and the lane-changing conditions are satisfied for changing to both lane-1 and lane-3, the final decision is changing to lane-3 based on this priority. 2.3.3 Lane-changing rules for lane-3 The process and rules are similar to the lane-changing process in lane-1, while the difference is that vehicle in lane-1 operates a left lane change and the vehicle in lane-3 operates a right lane change.

3 SIMULATION-BASED OPTIMIZATION METHOD Generally, the simulation-based optimization (SO) method provides a framework and process to find the optimal system design aiming at solving the problems whose relationships between input and output cannot be expressed directly by exact analytical formulas, also for the problems with high computational costs and unavailable source codes (Osorio & Bierlaire 2013). As the optimization algorithm, it contains decision variables, objective functions, and constraints. Hachicha et al. (Hachicha et al. 2010) summarized and presented a general formulation of the SO method in (3): Min f ðqÞ q2W

(3)

Where q is the vector of decision variables, f (q) is the objective function, and W is the feasible region. From the above equation, the objective function of the Simulation-based Optimization (SO) algorithm can be obtained from the simulator rather than presented as closed-form expressions, while the series of the analytical expressions for all constraints of the problems are closed-form. Therefore, the framework of the SO method provides the opportunity to connect the CA model with the optimization algorithm, for it does not need to input the complex functions to realize optimization, but calculates by its system to approach the objective function and find the optimal solutions. To be specific, the formulas used for the calibration of the proposed CA model can be simplified in (4): ( P min ðxobs  xsim Þ2 (4) xsim ¼ f ðmÞ In terms of this research, the objective function is to minimize the difference between the observed field data and the output of the proposed CA model. The parameter x for

157

comparison is the average speed of the road section. For the probability of random slowdown can influence the speed of the vehicles on road, it is selected as m to be the decision variable that is also the parameter to be calibrated for the model. As a result, the whole framework of the proposed CA model and SO method is shown in Figure 2, in which the processes with italic are the parts of calibration. The former three processes belong to the proposed CA model and initial parameters will be set according to the field data, then the outputs of the model will be used in the process of calibration. As far as the calibration process based on the SO method is concerned, the inputs of it include the field data and the outputs of the CA model. After operating the simulation optimization, it provides the suitable value which means the decision variable that has been defined before. And the updated parameter value will be assessed by the simulator and compared with the field data to finally determine the optimal output of the calibration.

Figure 2.

The framework of the simulation and calibration process.

4 CASE STUDY The study site (shown in Figure 3) locates at a super bridge with a total length of 212 meters of an expressway in Hebei Province, China. The starting mileage stake is K159 + 400 and the ending mileage stake is K159 + 612. The total expressway is a two-way four-lane expressway with a maximum speed limit of 110 km/h. This study uses Weigh in Motion (WIM) to get field data, including the arrival time of the vehicles, the speed of the vehicles, and the driving lane of the vehicles.

Figure 3.

Expressway segment on map.

The period is the evening peak hour from 5:00 pm to 6:00 pm, and the total number of vehicles is 485 in one hour. As for the proportion, the number of vehicles traveling in lane 2 occupies 78%, which is almost three times of the vehicles in lane 1. By manually collecting vehicles per 5 minutes in this hour, the number of vehicles is shown in the following Figure 4. 158

Figure 4.

Number of vehicles of statistical result (per 5 minutes).

In the proposed simulation model, the vehicle can move up to four cells per second and the corresponding actual speed of the vehicle ranges from 0 to 108 km/h (4 cell/sec=108 km/h). In addition, the arrival of vehicles and the maximum limit value of the density are adjusted to satisfy the lane occupation and the number of vehicles in one hour. The lane number is set as two to adapt to the actual situation and the other settings of the model have been illustrated in part of the proposal of the model. By combining the proposed model with the SO method, the simulation and calibration can operate at the same time. Apart from the initial parameters set in the CA model, the initial values should be input to start calibration, which means that the initial probability of random slowdown is 0.2 and the speed of the road section as the decision variable is 2.6 cell/s (=70.2 km/ h). In the process of calibration, the simulated speed values continue decreasing in the process of determining the optimal value of the probability of random slowdown until the 9th iteration. As a problem of local optimization, the algorithm has found the optimal solution of the probability of random slowdown yielding to the constraints and the final optimal value is 0.3234, and the corresponding average speed of the road section is 2.652 cell/sec (71.591 km/h). To be more specific, the values of the probability of random slowdown influence the speed values of the simulation model at each iteration. By converting the unit (cell/sec) of the output data into km/h, this research gains the following average speed of observed data and the average speed of the CA model to obtain the broken line graph for comparison in Figure 5, it

Figure 5.

Comparison of average speed.

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can find that the average speed of the CA model gradually decreased in the previous iterations and then gradually fitting with the average speed of the field data within the error range, and it means the simulator and the solution have completed.

5 CONCLUSION Based on the fundamental rules of the CA model and actual traffic conditions, this research improves the relevant evolution rules and establishes a multi-lane cellular automata traffic flow model. Meanwhile, the simulation-based optimization method is applied to carry out the calibration process to determine the optimal parameter value for the model. In the case study of this research, by simulating the vehicles of the peak hour and calibrating the parameter named probability of random slowdown, it founds that the application of the proposed CA model with the SO method provides the approach to finding the optimal value of the probability of random slowdown for the simulation of the collected data for the Super Bridge. Meanwhile, the simulation provides the close value of the average speed value of the road section with that of the actual average speed of the collected traffic data. Therefore, the proposed simulation model and calibration method can reproduce the relationship between traffic flow parameters and make a contribution to the research of microscopic traffic simulation and its parameter calibration. The study is limited by the lack of the influence of characteristics and differences of multiple drivers. As for the complex traffic environment, the ramps and the distinction between different types of vehicles are ignored. Thus, the main work in future research would include improving the lane-changing rules with consideration of more specific conditions like ramps, various drivers’ psychological responses, and distinctions between different vehicles.

REFERENCES Chen X.M., Xiong C., He X., Zhu Z. & Zhang L. (2016). Time-of-Day Vehicle Mileage Fees for Congestion Mitigation and Revenue Generation: A Simulation-Based Optimization Method and Its Real-World Application. Transportation Research Part C: Emerging Technologies, 63, 71–95. Gu Z. and Saberi M. (2021). Simulation-Based Optimization of Toll Pricing in Large-Scale Urban Networks Using the Network Fundamental Diagram: A Cross-Comparison of Methods. Transportation Research Part C: Emerging Technologies, 122, 102894. Hachicha W., Ammeri A., Masmoudi F. and Chachoub H. (2010). A Comprehensive Literature Classification of Simulation Optimization Methods. MPRA Paper, 27652. Korcek P., Sekanina L. and Fucik O. (2012, September). Calibration of Traffic Simulation Models Using Vehicle Travel Times. In International Conference on Cellular Automata (pp. 807–816). Springer, Berlin, Heidelberg. Lárraga M.E., Del Rio J.A. and Alvarez-Lcaza L. (2005). Cellular Automata for One-Lane Traffic Flow Modeling. Transportation Research Part C: Emerging Technologies, 13(1), 63–74. Nagel K. and Schreckenberg M. (1992). A Cellular Automaton Model for Freeway Traffic. Journal de Physique I, 2(12), 2221–2229. Osorio C. and Bierlaire M. (2013). A Simulation-Based Optimization Framework for Urban Transportation Problems. Operations Research, 61(6), 1333–1345. Zeng J., Yu S., Qian Y. and Feng X. (2017). Expressway Traffic Flow Model Study Based on Different Traffic Rules. IEEE/CAA Journal of Automatica Sinica, 5(6), 1099–1103. Zhao Y., Sadek A.W. and Fuglewicz D. (2012). Modeling the Impact of Inclement Weather on Freeway Traffic Speed at Macroscopic and Microscopic Levels. Transportation Research Record, 2272(1), 173–180.

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Advances in Traffic Transportation and Civil Architecture – Liu, Qi & Law (eds) © 2023 copyright the Author(s), ISBN 978-1-032-51438-3

Construction quality evaluation of expressway asphalt pavement based on digital construction Zhaoguang Hu China Road and Bridge Co., Beijing, China

He Jiang & Daxian Deng CCCC Road and Bridge Construction Co., Ltd, Beijing, China

ABSTRACT: With the continuous development of national urban traffic, the construction scale of the expressway in our country is increasing day by day, which put forward higher requirements for the construction quality and efficiency of the expressway, and digital construction emerges as the times require in pursuit of high construction efficiency and high construction quality. In order to study the construction quality of digital highway pavement paving, this paper took the digital paving construction of a pavement test section as an example, and established the evaluation model of highway pavement paving construction quality by taking compaction, flatness, deflection value, water penetration coefficient, skid resistance value of pavement and structural depth as the evaluation indexes of pavement construction quality. The results showed that the evaluation results of the construction quality of digital pavement paving in the test section are excellent. It shows that the digital pavement paving system can ensure construction efficiency, and has reference significance for the quality control of digital pavement paving construction.

1 INTRODUCTION With the continuous development of national urban traffic, the construction scale of the expressway in our country is increasing day by day. The construction quality of the expressway is not only related to the durability and service life of the expressway, but also directly affects the driving safety and comfort of the expressway (Han et al. 2022). Traditional highway pavement construction relies on manual inspection and proofreading, which usually leads to problems such as low qualified rate of elevation control and uneven pavement paving (Khan et al. 2013; Wilson et al. 2020), which directly affects the construction progress and cost control of the project, and also affects the safety of pavement driving (Ahmed & Kassem 2018). With the increase of the construction scale of the expressway, higher requirements are put forward for construction efficiency while paying attention to the construction quality. In order to pursue high construction efficiency and high construction quality, digital construction of highway pavement emerges as the times require. Digital construction of highway pavement aims to install a measurement control system on the grader and use the computer, satellite positioning, and mechanical automation control to realize construction automation and improve construction accuracy (Smits et al. 2017; Wang & Yin 2022). At present, there is little research on the quality control of digital construction, to study the construction quality of digital pavement paving systems, this paper uses the digital pavement paving system to construct the test section of a project, selects the main evaluation DOI: 10.1201/9781003402220-19

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index of highway pavement construction quality and establishes the evaluation model of pavement paving construction quality through investigation and analysis and according to the relevant standards and specifications. Finally, the construction quality of digital pavement paving in the test section is evaluated, and the evaluation results show that the construction quality of the digital pavement paving system is high, which can provide guidance for the follow-up section construction of the project, and also provide reference and ideas for the quality control evaluation of highway digital pavement paving construction.

2 DIGITAL CONSTRUCTION PAVEMENT PAVING SYSTEM 2.1

Working principle

The digital paving system, namely 3D digital intelligent control construction technology, is mainly composed of a measuring robot reference station and full-automatic monitoring components, and a program system for pavement paving (Jun & Haie 2021). The project described in this paper uses the automatic control system of the paving machine PCS900 of Trimble Company, and its main system components are shown in Figure 1. The PCS900 automatic control system of the paver adopts the total station technology to guide the construction, and the measuring robot carries out three-dimensional automatic control of the position and posture of the screed of the paver in real-time through the resection method, thus truly realizing the construction without piles. The digital paving system can realize construction process control, all-day construction operation, and digital construction management, standardize construction operation, improve construction efficiency, and ensure construction quality (Yan et al. 2021).

Figure 1.

2.2

Automatic control system of paver PCS900.

Work procedure

Firstly, the information on the original construction road surface is obtained by using the total station, and the calculation model of the construction site is established according to the terrain data obtained by the system. Through the comparison between the calculation 162

model of the construction site and the construction design data, the digital mechanical construction and inspection are guided. After the original data model is established, digital pavement construction can be implemented. When the digital pavement construction is adopted, the automatic control program of the system determines the specific position of the paver through the rear angle sensor and the sonar tracking and positioning system and determines the position coordinates of the paver ironing plate through the prism installed on the paver. Then the system program transmits the acquired position information to the control system of the paver in the form of an electrical signal through a data radio. Then the system program compares and analyzes the paver position information transmitted in realtime with the design data, and calculates the elevation information that is consistent with the model through complex data calculation, at the same time, corrects and corrects the given data, and finally feeds back to the leveling controller of the paver, so as to realize the threedimensional automatic control of the position and posture of the screed of the paver. The workflow is shown in Figure 2.

Figure 2.

Digital paving construction flow chart.

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3 ESTABLISHMENT OF CONSTRUCTION QUALITY EVALUATION MODEL 3.1

Index selection

The construction quality of highway pavement is related to the traffic safety of highway pavement, and the evaluation indexes affecting the construction quality of highway pavement are complex and diversified. According to the Technical Specification for Construction of Highway Asphalt Pavement (JTG F40-2017), six main quality evaluation indexes are selected, including compactness, flatness, deflection value, water permeability coefficient, skid resistance value of pavement, and structural depth. 3.2

Determination of weights

In the analytic hierarchy method (AHP), the establishment of a scientific judgment matrix plays an intuitive and important role in evaluating the scientificity and accuracy of the evaluation results. A large number of studies have shown (Díaz et al. 2022; Mousa et al. 2021; Ying et al. 2014) that the traditional analytic hierarchy method is computationally intensive when testing whether the matrix is consistent, especially when the elements of the judgment matrix need to be adjusted when the matrix is not consistent, which is often more complicated in practice. Therefore, this paper used the improved fuzzy hierarchy analysis method to study the established evaluation model, and the improved nine-scale method (fuzzy scale) was used to determine the weight of the digital construction quality evaluation index of highway asphalt pavement. 1. Construction of fuzzy complementary matrix The traditional analytic hierarchy method uses the nine-scale method to determine the judgment matrix, and the traditional nine-scale was shown in Table 1. In a large number of applications, it is found that the calculation of higher-order judgment matrices needs to be repeatedly adjusted to pass the consistency test, which is not only a huge amount of calculation but also the adjustment of the matrix is very complicated. Table 1.

Nine-scale method.

Scale

Definition (under the same standard)

1 3 5 7 9 2,4,6,8 Complementary scales

Both indexes are equally important One indicator is slightly more important than the other One indicator is more important than the other One indicator is significantly more important than the other One indicator is particularly important than the other The degree of importance between the two indicators The scale of Bi to Bj is aij, and the scale of Bj to Bi is 1=aij

Therefore, this  paper used the transfer function g ijðaÞ to change the initial judgment matrix A ¼ aij n
n into a fuzzy judgment matrix R ¼ g ij ðaÞ n
n , thereby avoiding a large number of consistency tests. Where the conversion function was shown in Equation (1). g ij ðaÞ ¼ 0:5 þ

a2ij  1 aaij

(1)

Where a is the conversion factor, and a  18, to meet 0 < g ij ðaÞ < 1. When  1  aij  9, there has 0 < g ij ðaÞ < 1, and g ii ðaÞ ¼ 0:5, g ij ðaÞ þ g ji ðaÞ ¼ 1. So R ¼ g ij ðaÞ n
n is a fuzzy

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judgment matrix. When a ¼ 18, the fuzzy scale has the largest range of values and is close to (0,1). The converted fuzzy scale was shown in Table 2. Table 2.

Fuzzy-scale.

Scale

Definition (under the same standard)

0.5 0.65 0.77 0.88 0.99 0.58, 0.71, 0.82, 0.94 Complementary scales

Both indexes are equally important One indicator is slightly more important than the other One indicator is more important than the other One indicator is significantly more important than the other One indicator is particularly important than the other The degree of importance between the two indicators The scale of Bi to Bj is gij , and the scale of Bj to Bi is 1  gij

(2) Determination of weight vectors Through the fuzzy judgment matrix, it could be converted into a fuzzy consistency judgment matrix, and the initial weight vector is obtained by Equations (2), (3), and (4) (Tian 2010). ri ¼

n X

g ij ðaÞ

(2)

j¼1

ri ¼ 0:5 þ wi ¼

ri  rj 2n

(3)

n 2b  n 1 X þ rik 2bn nb k¼1

Where the parameters should meet the conditions:b  n2 

(4)

n P

rik , the value of b can be

k¼1

obtained according to the actual situation. The power iteration is used to transform the initial weight vector of optimization into a final weight vector ðZðkÞÞ with higher accuracy. Take Z0 ¼ ðZ01 ; Z02 ; . . . ; Z0n ÞT ¼ w0 as the initial value, obtain the eigenvector (Zkþ1 ) by power iteration, and find the infinite norm kZkþ1 k1 . Zkþ1 ¼ EZk   E ¼ aij n
n

(5)

aij ¼ rij =rji

(7)

(6)

If kZkþ1 k1  kZk k1  e, kZkþ1 k1 is the max eigenvector, normalize to Zkþ1 to obtain the final weight vector (wðkÞ). 2

3T

6 Zkþ1;1 wðkÞ ¼ 6 ; n 4P Zkþ1;i i¼1

Zkþ1;2 ; n P Zkþ1;i i¼1

165

...;

Zkþ1;n 7 7 n 5 P Zkþ1;i i¼1

(8)

If this accuracy requirement is not met, take Equation (9) in again until the accuracy requirements are reached. 2 3T 6 Zkþ1;1 Zk ¼ 6 ; n 4P Zkþ1;i i¼1

Zkþ1;2 ; n P Zkþ1;i i¼1

...;

Zkþ1;n 7 7 n 5 P Zkþ1;i

(9)

i¼1

(3) Calculate the weights In this paper, the improved analytic hierarchy process was used to determine the weight of the selected evaluation index of highway pavement construction quality. To avoid the subjective evaluation results, the Delphi method was used to consult four professors and experts in highway engineering research in scientific research institutes, four pavement construction engineers with rich construction experience, and two graduate students in highway engineering. There are a total of 10 experts and scholars. The judgment matrix in the analytic hierarchy process was obtained through expert scoring, and the weight was calculated. Finally, the weight results after the consistency test and normalization were shown in Table 3. Table 3. quality.

3.3

Weights of evaluation indexes for highway pavement construction

Evaluation Indexes

Weight

Compactness Flatness Deflection value Water penetration coefficient Skid resistance value of pavement Structural depth

0.3393 0.2250 0.1662 0.1126 0.0762 0.0807

Model establishment

This paper evaluated the digital construction quality of a trial paving section of a highway project in Cambodia. The construction length of the trial section was 340 meters, and the paving width was 10.45 meters. The trial pavement structure was the upper layer of 4 cm thick fine-grained modified asphalt concrete (AC-13C). The upper surface course of asphalt concrete shall be constructed in the way of centralized mixing and continuous paving by the paver. Through the construction of the test section of the upper layer of asphalt concrete, the feasibility of the production mix proportion was verified. At the same time, relevant data shall be obtained to guide the subsequent construction of the upper layer of asphalt. In this test section, 40 test points were selected to test the compactness, flatness, deflection value, permeability coefficient, pavement anti-skid value, and structural depth of each point, and each index was studied according to the relevant specifications and design requirements. Quality evaluation index (QI) of highway pavement paving, which can be used to evaluate the construction quality of various pavements (Zhang & Li 2022), as shown in Equation (10).  n X Qi QI ¼ 100
(10) wi 1  Q i¼1 In the formula, wi represents the weight of the i-th evaluation index; Q represents the total number of detection points of the whole test section; Qi represents the number of unqualified detection points of the i-th evaluation index.

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According to the calculation results of the evaluation index of pavement paving quality, the pavement paving quality of the highway can be evaluated, and the evaluation results are shown in Table 4.

Table 4.

3.4

The evaluation results.

QI

>95

85