Highways: Construction, Management, and Maintenance : Construction, Management, and Maintenance [1 ed.] 9781613245910, 9781617288623

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Copyright © 2010. Nova Science Publishers, Incorporated. All rights reserved. Highways: Construction, Management, and Maintenance : Construction, Management, and Maintenance, Nova Science Publishers, Incorporated, 2010.

Copyright © 2010. Nova Science Publishers, Incorporated. All rights reserved. Highways: Construction, Management, and Maintenance : Construction, Management, and Maintenance, Nova Science Publishers, Incorporated,

TRANSPORTATION INFRASTRUCTURE - ROADS, HIGHWAYS, BRIDGES, AIRPORTS AND MASS TRANSIT

HIGHWAYS

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CONSTRUCTION, MANAGEMENT AND MAINTENANCE

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TRANSPORTATION INFRASTRUCTURE ROADS, HIGHWAYS, BRIDGES, AIRPORTS AND MASS TRANSIT Additional books in this series can be found on Nova’s website under the Series tab.

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Highways: Construction, Management, and Maintenance : Construction, Management, and Maintenance, Nova Science Publishers, Incorporated,

TRANSPORTATION INFRASTRUCTURE - ROADS, HIGHWAYS, BRIDGES, AIRPORTS AND MASS TRANSIT

HIGHWAYS CONSTRUCTION, MANAGEMENT AND MAINTENANCE

Copyright © 2010. Nova Science Publishers, Incorporated. All rights reserved.

SAMANTHA R. JONES EDITOR

Nova Science Publishers, Inc. New York Highways: Construction, Management, and Maintenance : Construction, Management, and Maintenance, Nova Science Publishers, Incorporated,

Copyright © 2011 by Nova Science Publishers, Inc. All rights reserved. No part of this book may be reproduced, stored in a retrieval system or transmitted in any form or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without the written permission of the Publisher. For permission to use material from this book please contact us: Telephone 631-231-7269; Fax 631-231-8175 Web Site: http://www.novapublishers.com NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book. The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers’ use of, or reliance upon, this material. Any parts of this book based on government reports are so indicated and copyright is claimed for those parts to the extent applicable to compilations of such works.

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Independent verification should be sought for any data, advice or recommendations contained in this book. In addition, no responsibility is assumed by the publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication. This publication is designed to provide accurate and authoritative information with regard to the subject matter covered herein. It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services. If legal or any other expert assistance is required, the services of a competent person should be sought. FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS. Additional color graphics may be available in the e-book version of this book. LIBRARY OF CONGRESS CATALOGING-IN-PUBLICATION DATA Highways : construction, management, and maintenance / editor, Samantha R. Jones. p. cm. Includes index. ISBN  (H%RRN) 1. Roads--Design and construction. 2. Roads--Design and construction--Management. 3. Roads--Maintenance and repair. I. Jones, Samantha R. TE175.H57 2010 625.7--dc22 2010022896

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CONTENTS vii 

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Preface Chapter 1

The Role of Digital Road Maps in the Future Felipe Jiménez

Chapter 2

Financing Highway Construction: Revolving Loan Systems as PAY-AS-YOU-USE Jay Eungha Ryu 

1 

35 

Chapter 3

The Balanced Vehicular Traffic Model Florian Siebel and Sebastian-Andre-Weg 

51 

Chapter 4

Non-Driver Understanding of Traffic Signs Annie W. Y. Ng and Alan H. S. Chan 

63 

Chapter 5

Mitigation Measures to Reduce Impacts on Biodiversity Clara Grilo, John A. Bissonette and Patricia C. Cramer 

73 

Chapter 6

Study on Impact Compaction of Aeolian Sand Subgrade and Its Effect Evaluation Yu-qing Yuan, Jing Li, Jian Wang and Xuan-Cang Wang 

115 

The Trade-Offs Between Highway Construction and Expansion and Transit Oriented Development Diane Jones and Manoj K. Jha

121

Cost-Benefit Analysis in Applying Design Flexibility and Context Sensitive Solutions: A Case Study of Alternative Alignment of MD 43 Extension Manoj K. Jha, Bertrand Djiki, Min Wook Kang and Eungcheol Kim 

133 

Public-Private Partnerships in Highway and Transit Infrastructure Provision William J. Mallett

149 

Chapter 7

Chapter 8

Chapter 9

Index Highways: Construction, Management, and Maintenance : Construction, Management, and Maintenance, Nova Science Publishers, Incorporated,

181 

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PREFACE The steep rise in road mobility that has taken place during the last decades has resulted in some negative effects that are relevant from a social and economic point of view, such as accidents, congestion, and air pollution. Solutions to mitigate these effects have resulted in improvements in vehicles, infrastructure, and planning. Among these improvements are Intelligent Transport Systems (ITS), or digital road maps, which are based on electronics, control and telecommunications. This book discusses highway construction financing; the balanced vehicular traffic model that describes traffic flow on highways; non-drivers as road users; road engineering; and public-private partnerships in highway and transit infrastructure building. Chapter1- Driver assistance systems are based on data collection and processing from different sources of information. Digital road maps have been seen as an additional sensor to expand drivers’ sight distance and, hence, to adopt measures that improve safety, traffic efficiency, satisfaction, etc. However, these new applications that are supported by digital maps involve a higher level of detail and accuracy of vehicle positioning. In this chapter, applications in which digital maps have a relevant role are revised. On the one hand, autonomous assistance applications and active safety systems are analyzed. On the other hand, cooperative systems are commented. These systems represent a very active line of research nowadays because of the improvements they can provide for road transport operation. Among these applications, some are focused on avoiding collisions and others can improve information systems. Finally, applications of vehicle location and fleet management are cited. These applications enforce some requirements concerning the detail and accuracy of digital maps and vehicle positioning. These specifications are analyzed taking into account the most recent conclusions of working groups and international projects. Limitations in accuracy lead to the need to develop map-matching algorithms, which are briefly mentioned. Prior specifications condition the means of developing digital maps. These methods are compared. Finally, considering that digital map updating is crucial for maintaining assistance systems over time, this problem and some solutions are tackled in the chapter. Chapter2- Deteriorating conditions of national highway systems have been partly attributed to the lack of financial resources. In response to the worsening highway infrastructures and attendant negative impacts on the national economy, federal legislations allowed state governments to use federal highway assistance funds as the equity capitalization funds for the State Infrastructure Bank (SIB) programs. The key innovations in SIB programs

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Samantha R. Jones

are revolving loan fund and leveraged loan fund structures. Those loan funding structures stretch scarce financial resources to expedite highway constructions. This chapter investigates the revolving loan systems (RLS) as viable PAY-AS-YOU-USE financing alternatives to traditional tax revenues. Elaborate computer simulations tested potential fiscal impacts of RLS loan interest rates, RLS loan shares, RLS borrowing interest rates, RLS borrowing shares, maturities of loans and borrowings, and their interactions. The simulation results reveal that RLS loan interest rates, RLS borrowing shares, interactions of RLS borrowing shares and loan interest rates, and most of all, maturities of loans and borrowings are the most critical parameters to maximize the amount of annual loans made to potential highway construction projects. This chapter further suggests three major caveats in implementing RLS programs: minimizing stimulating unnecessary investment projects, reinforcing marketing of RLS programs, and assessing project and credit capacities of the entities that borrow from RLS programs. Chapter3- The balanced vehicular traffic model is a macroscopic traffic model that describes traffic flow on highways as a hyperbolic system of partial differential equations. As many other macroscopic traffic models, it is based on an equilibrium velocity or fundamental diagram. Nevertheless, due to a generalized source term in the velocity equation, which can be motivated by a finite reaction time of drivers, flow values will be scattered in the flowdensity diagram for medium to high traffic densities as proposed by the three-phase traffic theory of B.S. Kerner. Moreover, stable and metastable steady state solutions form an inverted  in the flow-density diagram. Coupling conditions at general junctions can be formulated, which allow traffic simulations on general highway networks. Chapter4- Non-drivers are obviously road users who do not drive but, as pedestrians or cyclists, they do use the roads. Previous research has indicated that the relatively poor understanding of traffic signs by non-drivers may be due to a general lack of awareness of traffic signs in daily life. However, this proposition has not been investigated. The current study was conducted to examine the hypothesis that: - the more generally aware non-drivers are of traffic signs, the better their understanding of the signs. The understanding and awareness of traffic signs were assessed by means of a multiple-choice comprehension task and subjective rating respectively, for seventy-seven non-drivers and twenty-one Hong Kong traffic signs. The results showed that comprehension scores differed from sign to sign for non-drivers, and for most traffic signs, comprehension performance did not increase with awareness level of traffic signs. Ergonomics recommendations on traffic sign design were derived from the traffic sign comprehension responses of the non-drivers, so as to facilitate the design of more effective traffic signs in the future. Chapter5- Roads and traffic impact wildlife in a variety of ways. In some animal populations, they enhance mortality, limit mobility, fragment populations, and decrease habitat amount and quality, resulting in a limitation on food, shelter, and space availability, all fundamental to species’ survival. Those impacts and associated mitigations are becoming a major focus of research in conservation, namely in significant and emerging fields such as landscape and road ecology. In this chapter, the authors describe the main effects of roads on terrestrial vertebrates as well as a variety of mitigation measures that have been widely used for different taxa: crossing structures, dry ledges, fencing, right-of-way escape ramps and noise barriers. Because some species have similar needs and respond in the same way to mitigation measures, specific design standards are provided for five wildlife functional groups: 1) amphibians and riparian reptiles, 2) upland reptiles, 3) birds and bats, 4) small- and

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Preface

ix

meso-sized mammals and 5) large mammals. The authors discuss methods to evaluate crossing efficiency and several important directions for future research in this area will be suggested. Chapter6- In order to solve the problem of aeolian sand subgrade compaction, we studied the technology of impact compaction, applied it to the engineering practice and analyzed its effect with Rayleigh wave. The technology of impact compaction can combine the compaction of potential energy and kinetic energy and makes it easier for the materials to reach their elastic stage. With the combined function of "knead-roll-impact", the impact compaction road roller can compact the soil body and offers 6～10 times impact force and 3～4 times the depth of influence more than the vibratory roller. The methods of the impact compaction of aeolian sand subgrade is put forward. The comparative field compaction tests between impact and vibratory compaction are carried through, which are detected by Rayleigh wave. The results show that the impact compaction can make the density of the aeolian sand subgrade 2～5% higer than the vibratory compaction, and reach the influence depth of 7 metres. To sum up, the impact compaction can clearly increases the strength and stiffness of aeolian sand subgrade with a dynamic elastic modulus of 202.63MPa. Chapter7- Transportation is essential to urban life and is intricately connected to economic, social and environmental issues. In an effort to incorporate social justice concerns into transportation infrastructure planning, acknowledge successful urban planning principals, and not repeat the mistakes of the past, it is important to study the tradeoffs between highway construction and expansion and transitoriented development. This paper will define and explore the impacts both auto-oriented and transit-oriented development have on land use, quality-of-life and mobility. Issues of equity and the socioeconomic effects of both types of development will be explored. Analysis of the benefits and constraints of both highway expansion and transit-oriented development will be presented and analyzed to shape criteria for future transportation planning. A review of existing case studies exemplifying the best practices that have been undertaken in both auto- and transit-oriented planning will be presented. The purpose of transportation is to provide accessibility and mobility to desired destinations. A multiobjective analysis focusing on land use, travel time, and community impact (livability and equity) will be explored to determine how highway development and expansion, and transit-oriented development shape these concepts. This paper concludes that transportation development can have a negative or positive impact on communities. Transportation shapes land use; land use and the methods used to provide mobility and access to destinations affect community and how we live. An analytical multi-objective approach for investigating the aforementioned trade-off analysis will be developed in future works. Chapter8- This paper presents a cost-benefit analysis approach in applying design flexibility and obtaining a context sensitive solution (CSS) for the best alternative alignment of Maryland state route (MD) 43 extension. MD 43 extension is an urban arterial highway located in Eastern Baltimore, Maryland. CSS initiative establishes a better balance among environment, land-use, and other resources of a place with the design and purpose of the road. The payback of this innovation is valuable in many respects. The cost savings can be used to improve transportation or maintenance of existing roadways in the community. CSS conserves environmental and community resources. It facilitates and rationalizes the process of National Environmental Policy Act (NEPA) compliance, while saving time. It shortens the project development process by gaining consensus early, and thereby minimizing litigation

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and redesign, and expediting permit approvals. While this paper presented a manual benefitcost analysis approach based on weighing criteria, a fuzzy multiobjective optimization approach for evaluating alignment alternatives can be developed in future works. Chapter9- Growing demands on the transportation system and constraints on public resources have led to calls for more private sector involvement in the provision of highway and transit infrastructure through what are known as “public-private partnerships” (PPPs). A PPP, broadly defined, is any arrangement whereby the private sector assumes more responsibility than is traditional for infrastructure planning, financing, design, construction, operation, and maintenance. This report describes the wide variety of public-private partnerships in highways and transit, but focuses on the two types of highway PPPs that are generating the most debate: the leasing by the public sector to the private sector of existing infrastructure; and the building, leasing, and owning of new infrastructure by private entities. PPP proponents argue that, in addition to being the best hope for injecting additional resources into the surface freight and passenger transportation systems for upkeep and expansion, private sector involvement potentially reduces costs, project delivery time, and public sector risk, and may also improve project selection and project quality. Detractors, on the other hand, argue that the potential for PPPs is limited, and that, unless carefully regulated, PPPs will disrupt the operation of the surface transportation network, increase driving and other costs for the traveling public, and subvert the public planning process. Some of the specific issues raised in highway operation and costs include the effects of PPPs on trucking, low-income households, and traffic diversion. Issues raised in transportation planning include non-compete provisions in PPP agreements, unsolicited proposals, lease duration, and foreign control of transportation assets. On the question of new resources, the evidence suggests that there is significant private funding available for investment in surface transportation infrastructure, but that it is unlikely to amount to more than 10% of the ongoing needs of highways over the next 20 years or so, if that, and probably a much smaller share of transit needs. With competing demands for public funds, there is also a concern that private funding will substitute for public resources with no net gain in transportation infrastructure. The effect of PPPs on the planning and operation of the transportation system is a more open question because of the numerous forms they can take, and because they are dependent on the detailed agreements negotiated between the public and private partners. For this reason, some have suggested that the federal government needs to more systematically identify and evaluate the public interest, particularly the national public interest, in projects that employ a PPP. Three broad policy options Congress might consider in how to deal with PPPs in federal transportation programs and regulations are discussed in this report. The first option is to continue with the current policy of incremental changes and experimentation in program incentives and regulation. Second is to actively encourage PPPs with program incentives, but with relatively tight regulatory controls. Third is to aggressively encourage the use of PPPs through program incentives and limited, if any, regulation.

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Chapter 1

THE ROLE OF DIGITAL ROAD MAPS IN THE FUTURE Felipe Jiménez Universidad Politécnica de Madrid University Institute for Automobile Research (INSIA) Campus Sur UPM, Carretera de Valencia km 7 28031, Madrid (Spain)

ABSTRACT

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Driver assistance systems are based on data collection and processing from different sources of information. Digital road maps have been seen as an additional sensor to expand drivers’ sight distance and, hence, to adopt measures that improve safety, traffic efficiency, satisfaction, etc. However, these new applications that are supported by digital maps involve a higher level of detail and accuracy of vehicle positioning. In this chapter, applications in which digital maps have a relevant role are revised. On the one hand, autonomous assistance applications and active safety systems are analyzed. On the other hand, cooperative systems are commented. These systems represent a very active line of research nowadays because of the improvements they can provide for road transport operation. Among these applications, some are focused on avoiding collisions and others can improve information systems. Finally, applications of vehicle location and fleet management are cited. These applications enforce some requirements concerning the detail and accuracy of digital maps and vehicle positioning. These specifications are analyzed taking into account the most recent conclusions of working groups and international projects. Limitations in accuracy lead to the need to develop map-matching algorithms, which are briefly mentioned. Prior specifications condition the means of developing digital maps. These methods are compared. Finally, considering that digital map updating is crucial for maintaining assistance systems over time, this problem and some solutions are tackled in the chapter.



Telephone: +34 91 336 53 17, Fax: + 34 91 336 53 02, Email: [email protected]

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1. INTRODUCTION The steep rise in road mobility that has taken place during the last decades has resulted in some negative effects that are highly relevant from a social and economic point of view, such as accidents, congestion and air pollution. Some relevant figures are as follows (McDonald et al, 2006; Jiménez and Aparicio, 2008): -

-

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-

Approximately 10% of the road network is affected daily by congestion. Transportation is the fastest growing sector in energy demand of all sectors, with road transport accounting for 83%, and this, in turn, has a direct bearing on CO2 emissions. Vehicles are the major source of pollution in towns and 20% of cities suffer unacceptable levels of noise. Each year in the European Union, there are about 40,000 deaths and 1.7 million injured in road accidents. In economic terms, the effects of congestion account for 0.5% of GDP, environmental impact 0.6% and accidents 1.5%.

In this sense, we have sought solutions to mitigate these effects, which have resulted in improvements in vehicles, infrastructure, planning, etc. Among these improvements that can be highlighted are Intelligent Transport Systems (ITS), based on advances in electronics, control and telecommunications, In this context, digital maps play an important role in supporting multiple systems, but their specifications have also grown, both in level of detail and in precision, because some applications require much more detailed information than that included in the first navigation systems. Basically, the relevance of digital maps is based on the establishment of an "electronic horizon" above the driver's visual field, so that we can predict future situations and suggest appropriate actions, because the system has more information (Reichart et al, 1998; Venhovens et al, 1999, Njord et al, 2006; Wevers and Lu, 2007). While the early use of navigation in road vehicles did not involve high requirements of precision and detail, their use as a support for Advanced Driver Assistance Systems (ADAS) meant a significant change from what was called a "navigation map" to a "safety map" (T'Siobbel, 2003; T'Siobbel and van Essen, 2004). In particular, some characteristics were established as fundamental, such as reliability, completeness and accuracy, which involved new forms of construction, operation, maintenance and distribution of digital maps. Thus, a map for navigation can be used in vehicle positioning fleet management, traffic monitoring, etc.., and high accuracy is not required to offer these services. However, a safety map should include new variables and the positioning accuracy should be greater. Within the strategic agenda given by the European Road Transport Research Advisory Council (ERTRAC), in which 4 major areas of activity are established (Urban Mobility, Energy, Resources, and Climate Change, Long Distance Freight Transport, Road Transport Safety), the horizontal priority of “development of universal digital maps for navigation and positioning with integrated real-time updating” is identified (European Road Transport Research Advisory Council, 2008). Similarly, on the strategic research agenda presented at the eSafety Forum (2006), in which the main lines of research are mobility services for people and for goods, intelligent vehicle systems, cooperative systems and field operational tests, the development of enhanced digital

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maps and enhanced position systems is specifically cited as an important horizontal activity. Thus, it is stated that innovative, enhanced digital maps need to be developed and maintained in a cost efficient way to support systems and services. The main objectives are accuracy and up-todateness of the maps. The same organization created a Working Group on digital maps in order to promote the availability of safety-related attributes in digital maps. In turn, the Road Map Working Group indicated the need to develop this enhanced digital map and to create publicprivate partnerships to produce, maintain, verify and distribute this map database. Figure 1 shows how new applications or enhancements of existing benefits force meeting higher requirements on digital maps regarding precision and detail and proper positioning on these maps (Noronha and Goodchild, 2000; Baum , 2003, Organization for Economic Cooperation and Development, 2003, Lu et al, 2004; eSafety Forum, 2005).

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Figure 1. Schematic overview of the requirements imposed on digital maps and vehicle positioning.

This involves new sensors and systems for collecting and processing measurements, new techniques for data validation and verification of the consistency of the database, new communication solutions to facilitate the coordination of data and new tools for distributing and updating the database (Baum, 2003). Furthermore, the above is not confined only to static information, but should extend to dynamic information, changing over short time intervals. Thus, the stored geometry can be considered as the framework of other information and only simplified horizontal alignment is not sufficient. Thus, simplifications and omissions that are acceptable in navigation systems, in many cases make conventional maps useless for more advanced applications. This chapter develops the various points raised in Figure 1. Firstly, applications that can exploit or in the future will be able to exploit the deployment of more accurate and detailed digital maps are shown in section 2. In section 3, the attributes that digital maps of the future must include to be employed in previous applications are addressed. Section 4 outlines the methods for collecting and updating map information. Finally, the conclusions are outlined in section 5.

2. APLICATIONS: SYSTEMS AND SERVICES 2.1. Intelligent Transport Systems There are many classifications of Intelligent Transport Systems, although they all converge on the same large areas (Chowdhury and Sadek, 2003; Committee on Transport, 2003, Centro Zaragoza, 2003, Miles and Chen, 2004, Aparicio et al, 2008). Table 1 shows a summary of these areas.

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Table 1. ITS main areas. Area

Intelligent Transport System or Service

Traveller information

Pre-trip information Trip planning support On-trip information On-trip information on public transport Personal information services Route Guidance and Navigation

Traffic Management

Transport planning support Traffic control Incident management Demand management Infrastructure maintenance operations

Commercial vehicle operations

Commercial vehicles control Administrative processes Automatic inspection of vehicles Vehicle monitoring Commercial vehicles fleet management

Public Transport operations

Public transport management Demand-responsible transport management Shared administration of transport

Electronic payment

Integrated payment service

Emergency services

Incident notifications Emergency vehicles management Hazardous materials and incident notifications

Vehicle services: driver assistance and vehicle control

Driver assistance Vision enhancement Autonomous operation of vehicles Collision avoidance (longitudinal and lateral) Pre-collision systems deployment Adaptive restraint systems Vulnerable users protection measures Intelligent intersections

2.2. Use of digital maps in Intelligent Transport Systems and Services In the field of driver assistance systems, the first and most widely used and known application of digital maps and geo-positioning is navigation systems that provide the driver instructions on how to reach a particular destination. However, over the years and the evolution of in-vehicle technologies and communications with the outside, there has been an important group of applications that may rely more or less on digital maps.

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There are two separate areas of vehicle positioning on a digital map: onboard positioning and positioning in a management centre. In the first case, the information obtained from this positioning is used to provide information for the driver or take action on the vehicle, while in the second, the information can be used in the management centre itself or be distributed to either one or a group of vehicles. Figure 2 shows this situation and some examples of systems and services. Moreover, digital maps allow supporting many systems, including some whose performance has traditionally been done without such maps. In these cases, the maps add information or may even come to replace other sensors, optimizing the global architecture, since the positioning system would be used for different applications without additional cost. The digital map is integrated as an additional sensor that provides information for the driver and other vehicle systems.

Figure 2. Onboard positioning and positioning in a management centre.

In this operation as a sensor, the primary sensor operation can be distinguished when the information derived from it is of great importance and the secondary sensor when map information is used to validate data from other sensors or to enable more efficient detection of the primary sensors of the system. Table 2 shows some examples of systems classified according to the role of the digital map. In the following sections some of the most representative systems and services using digital maps and vehicle positioning are described, in order to appreciate the requirements in terms of detail and precision of the digital maps, which will condition the means of obtaining and updating the map, as well as the positioning system on it. The reference is not intended to be exhaustive but to provide an overview of the types of systems which rely to a greater or lesser extent on digital maps.

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Table 2. Typical safety applications categorised by use of a map as a primary or secondary sensor. Role of the map as sensor for safety systems Primary sensor

Secondary sensor

Speed Limit Assistance: Informs the driver of the legal speed limit at the location of the vehicle and/or warns the driver when exceeding the legal speed limit.

Adaptive cruise control: Adapts a vehicle’s desired speed to the speed of preceding vehicles or road geometry ahead. Adaptive cruise control typically works at higher speeds only.

Curve Warning: Warns the driver when his/her current speed exceeds the safe speed for the curve ahead, and possibly the distance to the curve and the required brake force.

Stop and Go: Adapts the vehicle’s speed and course on basis of a desired speed to the speed of preceding vehicles. Stop and Go typically works at lower speeds.

Curve Control: Automatically reduces the speed of the vehicle to a safe speed for an approaching curve.

Advanced front-lighting system: Directs the front light beam in the direction of the turn a car intends to take, or adapts beam width and reach on the basis of the vehicle’s speed and the road lay out.

Predictive Powertrain Control: Informs the system of upcoming slopes thus enabling gear shifts to avoid inefficient speed reduction.

Lane keeping assistance: Informs the driver when the vehicle is likely to leave the current lane unintentionally.

“Hotspot” Warning: Informs the driver about a potentially hazardous location ahead.

Lane Change Assistance: Informs the driver when it is safe/unsafe to change lanes.

Intersection Assistance: Informs the driver on intersection characteristics (right of way situation, traffic lights) and which lanes to choose in order to safely traverse an intersection.

Collision avoidance: Adapting the vehicle’s speed and direction of travel in order to avoid a collision.

2.3. Assistance and Safety Systems Among assistance and safety systems, autonomous systems and cooperative systems can be distinguished. However, it should be noted that in the first description, driver assistance systems defined as "wholly autonomous in-vehicle systems" only considered information that could be obtained within the vehicle itself, without any source of external information (McDonald et al, 2006). However, this definition is too strict and would exclude those systems based on positioning (via GPS, for example) on a digital map. For this reason, this definition has become obsolete. On the other hand, cooperative systems include communications between vehicles or between vehicles and infrastructure. Currently, there is overlap in many cases with traditional systems that could be considered autonomous increasing their services due to a higher or lesser degree of communication with the

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environment. This fact reflects the change of name of the IVI (Intelligent Vehicle Initiative) programme, which evolved in the USA, to VII (Vehicle Infrastructure Integration).

2.3.1. Navigation Systems 6% of the travelled distance in the United States can be considered as "unnecessary driving" (King and Mast, 1987) when trying to locate the destination in regions that the driver does not know, and, since the number of accidents involving fatalities is approximately proportional to the distance travelled, an increase in travelled kilometres results in an increase in accidents (Fridström et al, 1995). Furthermore, potentially dangerous situations that could result from manoeuvres of drivers who do not know the path to follow should also be considered. The purpose of a navigation and guidance system is to indicate the quickest / simplest / shortest route to go from one point to another. This is based on the type and geometry of roads and historical traffic data, although the weather or traffic may condition the calculated route, if such data are available, which gives rise to dynamic or intelligent navigation systems. The integration of navigation systems into motor vehicles has been remarkable in recent years (Khan, 2003). Detailed maps and calculation possibilities allow better functionality. For precise navigation, the attributes used by the system are about 150, including street names, restrictions of movement, particularities in certain sections depending on the time or day of the week, location of points of interest, expected speed, etc. (Blamire and Marugg, 2004). However, details of the geometry of the road, such as road grade or superelevation rate are not necessary. Positioning in the digital map is usually done through the signal received by a GPS receiver, greatly reducing the error if differential corrections are considered. However, in shadow areas (tunnels, proximity to high buildings or in areas with high trees) this signal is often lost (Forssell et al, 2004). While short-term losses are not significant, longer losses cannot be admissible. To remedy the situation, at present, the integration of vehicle speed (which provides longitudinal position) and yaw angle are used. These variables are available in modern vehicles. Several studies on the impact of in-vehicle navigation systems show that they can produce reductions in accident risk of 4% and time savings of more than 10%. 2.3.2. Intelligent Navigation Systems Often, drivers do not know the optimal route based on changing conditions. Intelligent navigation refers to the type of navigation that recalculates the route to achieve a specific objective by considering incidents such as congestion, road cuts, etc, that are detected (before being reached) through communications with the information distribution centres. This system overlaps with traffic monitoring systems. To obtain this dynamic information, sensors in the road or floating vehicles can be used. They send the information to a control centre that processes and integrates all data and infers traffic conditions. From that information, the system makes predictions of the traffic conditions expected in a short time horizon. Its objective is to maximize the capacity of infrastructure routes and avoid wrong choices. Thus, route optimization by attending to different criteria and the addition of traffic data, allows greater customization of the navigation system. It should be mentioned that some users prefer the system to recalculate the route based on dynamic information while others prefer to know that information and make their own decisions.

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Following this line, the ADVANCE project, between 1991 and 1995, was the first large scale project to deal with dynamic route calculation in America. Savings of 4% were verified in the travel time in normal conditions and the ability to detect incidents so that users could avoid them was improved. The main problems were collecting, processing and distributing the information. Moreover, VICS (Vehicle Information and Communication Systems), in Japan, includes dynamic navigation systems that consider road and traffic conditions in real time. Knowing the bottlenecks and other constraints, the system determines the best route according to destination and can continuously recalculate the arrival time. The success of VICS is due to good information of the variables of influence, an aspect that is not always attainable, because numerous public and private entities are involved. Calculating the benefits expected by the use of these systems has several uncertainties. The first relates to the algorithms of dynamic traffic assignment and the stability of the predictions of travel times. The continuous development of these algorithms also makes it difficult to establish the advantages of dynamic methods compared to static ones. The second concerns the quality of data received in the information centre. Currently, data on traffic intensity and speed at different points in the network are collected almost exclusively through detectors in the infrastructure. By using simulations, some studies have found that these systems can result in a reduction of accidents, although of little value. Other studies, however, show an increase in accidents in the routes of shortest travel time, which is justified by the fact that the minimum is achieved when the traffic flow is shared equally by all areas so that there are not more congested areas, but this results in an increase in conflicts at intersections (Maher et al, 1993). However, when studying the effects on mobility, significant reductions in travel time have been observed ranging between 6-7% (Stoneman, 1992] and 11% (Kawashima, 1991) and increases in road capacity of 10%.

2.3.3. Onboard Traffic Signs Information The traffic signs that comply well with their work must meet the following conditions (Elvik and Vaa, 2004): -

the signs must be located so that they are easy to see the signs must be readable both in daylight and in the dark the signs must be used in such a way that road users take them seriously the signs must be enforced, to prevent violations the signs must be understandable

Not always are these conditions met, and a significant percentage of signs are damaged. Some of the most frequent errors that have been found are: location defects, design faults, too many road signs in a stretch and a wrong use of the sign. The study by Lyles et al (1986) shows that improvements in road signs allow a reduction of accidents with injuries by 15%. In this sense, onboard information has the following advantages: -

Confirms the physical sign Cannot be damaged There are no visibility problems

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The system can remind the driver of the information at every moment It can show dynamic information if it is updated

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Providing this onboard information can be done by various means and not only by using digital maps containing signs data. In particular, there are artificial vision systems that recognize the sign and show it on a head-up display, and communications systems with the infrastructure, in which information is transmitted between them In the same way, digital maps can be used to alert the presence of “hotspots”.

2.3.4. Intelligent Speed Adaptation (ISA) Systems Despite the well known negative effect of inappropriate speed on safety, consumption and pollutant emissions, drivers often hold the belief that they can control the vehicle when travelling at speeds above the legal speed (Rumar, 1985). On the other hand, there is an increased risk of accidents when travelling at speeds far from the average speed (Solomon, 1964; Hauer, 1971; Salusjarvi 1988, Finch et al, 1994; Maycock et al., 1998; Quimby et al, 1999; Kloeden and McLean, 2001). Intelligent speed adaptation (ISA) is the generic name given to those systems in which the vehicle "knows" the speed limit and is able to use this information to alert the driver if necessary or carry out some actions in order to control the vehicle’s speed. The first study of these systems for adjusting the speed was presented in the eighties, although the most relevant projects began in the mid-nineties. ISA systems can be classified according to various criteria (Carsten et al, 2000; Carsten and Tate, 2001). One of them considers the permissiveness of the system with the driver's actions, with the following categories being distinguished: informative, voluntary and mandatory. Another classification, in line with the type of digital map used, is based on the frequency with which it updates the speed limits and variables used for this purpose, providing the following categories: - Fixed: the Vehicle receives information on the legal speed limits. In this case, the digital map must contain the information on speed limits in each road section, but positioning accuracy is not critical, although it is necessary to discriminate on which road the vehicle is. - Variable: the vehicle is informed of certain specific points where the limit is lower, for example, pedestrian crossings, dangerous curves, and so on. The digital map should contain information of the infrastructure with a high degree of detail and accuracy as, for example, data such as the radius and superelevation of curves, to estimate the safe speed on them. - Dynamic: more restrictions because of environmental conditions (slow traffic in fog, slippery road, etc.) are imposed so that the update is continuous in time. In the latter situation, dynamic parameter information, which involves establishing communications in general, with the infrastructure or control centres, should be added to the map of the previous type. The most widely used option is the first one because it is the simplest one considering the current state of the art. However, there have been estimates of the effect of these systems in reducing accidents, reaching the conclusion that some versions could contribute positively to 36% of accidents with injured and 59% of fatal ones if the most advanced settings were

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applied (Carsten and Tate, 2005). These results are even more positive than those reported for variable speed limits in winter, which are estimated to reduce accidents by 21% (Peltola, 1991) and fatal accidents by 40% (Hantula, 1995). Depending on the system type, the requirements regarding the digital map change, and greater or lesser detail and accuracy of the information contained in it is needed. In terms of positioning, in general, excessive accuracy is not required, but the exception occurs if the system is intended to discriminate safe speeds depending on the lane and not to give false alarms in complex scenarios such as acceleration-deceleration lanes where the speed limit may be very different from the rest of the road. Moreover, possible applications of ISA systems have been studied to improve traffic flow (Hogema and van der Horst, 1999), so that by sending drivers optimal speeds depending on traffic conditions, a much more homogenous longitudinal vehicle control is achieved, and sudden braking manoeuvres are prevented.

2.3.5. Curve Speed Warning Systems (CSWS) The justification of these systems is based on studies that analyze the effect of observing the legal speed limits on curves that show accident reduction estimates of between 20 and 29% (e.g. Hammer, 1969; Rutley, 1972). These systems can be understood as a particular case of ISA systems with variable or dynamic speed limits, since they are based on the same principles and require a detailed digital map with detailed and precise geometric features in order to calculate safe speeds. Also, the map must contain the road signs. Pomerleau et al (1999) refer to the conditions to be complied with by speed adaptation systems and the implications that positioning inaccuracies have on the calculation of the speed limit. The simplest configuration would correspond to that established for ISA systems, in which system decisions are based on road geometry. However, this solution is not completely realistic, since each driver describes the curves in a different way, so the effective radius of the path does not often coincide with the geometric centreline of the lane (Gambard, 1985). Therefore, some authors advocate the incorporation of other sensors in order to establish and compare the actual path at all times with the one stored in the digital map to determine the degree of proximity to a risk situation (Revue et al, 2003). In an initial state, such systems can function to include positioning on the road, but not in the lane, though, if control measures are considered, lane level positioning should be achieved in order to discriminate which lane the vehicle is in. Of the parameters contained in the map (radius and curve angle, surface condition, superelevation rate, number of lanes, sight distance, etc.), curvature is the most important, and errors of up to 5 -10% could be permitted. 2.3.6. Fuel Consumption Optimization Considering the Following Stretches of Road Another evolution of ISA systems is called Situation Adaptive Drivetrain Management (SAM) system, which aims to minimize consumption through a proper control of acceleration and braking processes according to the following stretches of road known from the digital map (Reichart et al, 1998; Venhovens et al, 1999). Tests on driving simulators have shown that its application involves significant reductions in consumption, although slight increases were detected in travel time. In this case, the information that is critical in the digital map is the vertical alignment and the legal speed limits.

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2.3.7. Adaptive Cruise Control Adaptive Cruise Control is an example where the digital map could act as secondary sensor, because it is usually only based on onboard sensor systems. Thus, through environmental monitoring technologies (mainly radar, although laser, artificial vision, etc. can also be used), the system detects potential obstacles in the path of the vehicle, alerting the driver or performing braking manoeuvres. Thus, it integrates essentially two main functions. On the one hand, it works like a standard cruise control and, secondly, it controls the distance to vehicles in front of it (Prestl et al, 2000). The system therefore requires data on the variables set by the driver, the kinematic characteristics of the obstacles encountered and the vehicle dynamics in terms of speed, acceleration and yaw (Widmann et al, 2000). The most important difference with cruise control is the analysis of the vehicle’s surroundings (Rohr et al, 2000). This, however, has a number of problems. It has been found that the mere detection of obstacles is not enough in the driving environment. For example, on a curve, a car travelling in the opposite direction but in the opposite lane could be interpreted as a threat (when, in general, it is not) and would cause a sharp response from the system. Besides the complex geometry of the road, we must take into account the behaviour of the driver and other road users, which further complicates the prediction of future paths. In this sense, the digital map can help estimate the paths of the vehicle and potential obstacles and more reliably discriminate which of them truly represents a risk (Prestl et al, 2000). It is most useful in the case of roads with sharp bends or complex environments, and vehicle positioning in the lane is necessary. The potential accident reduction using this system has been studied. The results show reductions of 50% in rear-end accidents (Farber and Paley, 1993; Chira-Chavala and Yoo, 1994, Regan et al, 2001). The estimated impact on mobility has also been studied because headways between vehicles can be modified. 2.3.8. Lane Departure Warning System Lane departure avoidance systems are generally based on sensors that detect the lines defining the lane and alert the driver or act in the event that the vehicle is approaching too close to them without the driver having indicated that this movement is premeditated using the indicators. However, a precise positioning on a map can produce the same effect, even in situations where the detection of the lines is not entirely feasible (e.g. due to adverse weather conditions). The main difficulty lies in the fact that it requires a very high level of accuracy in the map and in the positioning of the vehicle, which is usually only achievable with differential GPS positioning and only if the operating conditions are favourable. Although it is an exceptional solution, for a particular type of vehicle, Kwon et al (2003) and Alexander et al (2005) show an experience in which very precise positioning on a detailed map is used to alert the driver of emergency vehicles in order for them to stay in lane under adverse conditions of snow and fog. Furthermore, this allows other information such as speed limits to be transmitted. This solution is expensive because it requires differential GPS positioning or magnetic sensors in the infrastructure and needs great skill on the part of the driver. It should be noted that the requirements regarding accuracy on the map and the positioning of this system are much higher than those previously dealt with since the lateral position of the vehicle in the lane has to be known, with only errors of less than 0.5 meters

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being able to be accepted. Since current technology does not permit such precision under all operating conditions, solutions based on sensor fusion are being studied.

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2.3.9. Intelligent Lighting System The dynamic nature of the driving task involves, almost naturally, the introduction of active lights that adapt to the traffic conditions. Since it is not reasonable to increase the driver’s tasks, this adaptation should be performed automatically. Thus, when the vehicle is going to enter a curve, as detected from the turn of the steering wheel, for example, the beam could be redirected to illuminate the area of interest for the driver, simultaneously reducing the risk of dazzling other users (Prestl et al, 2000; Wördenweber, 2001). Similarly, in urban areas or intersections it may be of interest to widen the beam near the confluence of streets. In this regard, the positioning of the vehicle in a digital map can improve knowledge of the following sections of road, in anticipation of these road stretches. It should be noted, therefore, that optimal system behaviour depends on the road type, a feature that should be included in the digital map, and the road section, but high levels of accuracy are not necessary. 2.3.10. E-Call The basic aim of tertiary safety systems is to prevent the consequences arising from any injuries suffered in the accident through improved care of victims, both as first aid and immediate assistance in evacuation. The emergency call is a system that can be activated by the vehicle occupants or be activated automatically from onboard sensor signals. Thus, the vehicle may contact the emergency services and provide useful information for the rescue phase. The justification of this system relies on different estimations (Berzal et al, 2005). For example, a fast medical response would reduce deaths by 11% and severely injured by 12%. A study of the European Commission (IST-1999-14093 LOCUS, Location of Cellular Users for Emergency Services) shows a reduction of 10% in deaths in traffic accidents if response time is reduced. Vehicle positioning is critical, but a high level of accuracy is not required. However, in the case of motorways it is critical to know the direction of movement, because errors in this variable can lead to excessively long response times. The next step in this type of system is the so-called advanced e-call, whose aim is to provide a more effective response to the vehicle’s occupants, in order for the vehicle to be able to give information concerning itself, the occupants and the accident. Apart from the location of the accident and the direction the vehicle had before the accident, details of the vehicle and its state, the state of the occupants and the severity of their injuries, etc, should be transmitted to the emergency services. 2.3.11. Autonomous Driving The issue of automatic driving has been raised for various purposes for many years. There are automated highway experiences in which a platoon of vehicles can be automatically driven along dedicated lanes. The aim is to improve safety and efficiency while reducing environmental impact and travel times.

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Vehicle positioning can be performed using different techniques such as beacons in the infrastructure, the monitoring of lines painted on the road or GPS positioning (Naranjo et al, 2007, Naranjo et al, 2009). However, it should be noted that the required accuracy is high and in the case of using satellite positioning, differential corrections should be used. This fact limits their use and requires sensor fusion. In this case, as a complement to positioning, detection systems of the surroundings of the vehicle by radar, lidar, artificial vision, etc. can be used for autonomous driving (Broggi et al, 2006).

2.3.12. Cooperative Systems Cooperative systems are those that establish unidirectional or bidirectional communications between vehicles (V2V) or between vehicles and infrastructure (V2I), either short or long-range (Naranjo et al, 2008; Jimenez and Aparicio, 2008). Safety applications have been developed to warn of different risks such as vehicles stopped on the shoulder, emergency vehicles approaching, potential conflict situations at intersections, and vehicles in adjacent lanes or in front circulating at lower speeds, and so on. The information transmitted can be supported by a digital map in order to achieve a better representation of the environment and deduce the risks and evaluate the best decisions to take. The integration of vehicle positioning into cooperative collision avoidance systems is cited, for example, in Tan and Huang (2006), and Shladover and Tan (2006). In particular, the latter study examines the accuracy requirements of vehicle positioning for their effective use in such systems.

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2.4. Centralized Management Services 2.4.1. Traffic Monitoring and Management Traffic management systems in urban and rural areas require specific digital maps in which the exact geometry of the road loses part of its value, although other aspects such as location of crossings, traffic lights, number of lanes, etc, take on greater importance. In general, the systems have a network of sensors, a network of "actuators" (variable message signs, traffic lights, etc.), a communications network and a management centre. In the latter, the status of each road section and the state of the elements of the system appear in a Geographic Information System. Variables such as average intensity, speed distributions and hotspots are considered relevant for traffic management. Traffic management experiences are several. The results in terms of network efficiency and reduced travel time have been significant. For example, one study shows a reduction of total delay of 14-19% after installing a computerized traffic signal system and optimized signal timing on several intersections (DMJM Harris, 2005). Similarly, on-time reliability is promoted through the traveller information systems (Shah et al, 2003). Also, growing user satisfaction is perceived because users receive more dynamic information, allowing them to plan their route in a better way (Ran et al, 2002). Similarly, announcements of the state of the road or the weather are also perceived positively by users and result in improved safety (Goodwin 2003) and reduce the likelihood of arriving late at the destination (Pilli-Sihvola and Jukka, 1995, Battelle and the Meyer, Mohaddes Associates, 2004).

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In addition to traffic management itself, the collection of variables such as speed, hotspots with a large accumulation of accidents, the daily measurements of intensities or the intensities of heavy vehicles are helpful for planning actions to improve the road network.

2.4.2. Commercial Vehicles Fleet Control The lines of research being conducted in the ICT field to mitigate the problems associated with road freight transport are oriented towards the flexible and dynamic management of corridors for transporting goods, and the use of positioning systems with real-time information with vehicle-to-infrastructure communications, which allows the management of infrastructure and traffic as a complete system to optimize capacity. Another relevant aspect is advanced logistics management. Despite improvements in the logistics system, it still has the following negative points -

A large number of uncoordinated operations conducted by small fleets Much of the transport capacity is underutilized (because of the empty return journeys) Loss of time for various reasons

In urban areas, there are some peculiarities regarding transport. Among others, we can highlight: -

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-

Need to control access to areas with limited access. There are many small distributors that operate quite independently. For the purpose intended, there is limited use of telematics by authorities to control the distribution process. Lack of communication between fleet managers and traffic agencies.

Both from the points of view of the fleet manager and for the end-user, it is interesting to monitor vehicles and cargo in real time. In this monitoring, positioning information is always necessary. These measurements can be used for different purposes, such as the diagnosis of vehicle operation, supervision of drivers, security applications and fleet management. Furthermore, nowadays 'just in time' is becoming a key issue for freight transport, as well as the need to offer additional services to the client, such as cargo tracking, etc. This monitoring of goods and vehicles is also very useful for transport companies themselves to optimize their operation and find anomalies to be corrected. Thus, it is proposed that vehicles can be redirected to avoid or reduce waiting times and make a delivery faster and more efficiently, without delays. Among other things, telematics can address the following issues: -

Positioning of vehicles for transport planning and statistical studies Calculation of the best sequence for saving fuel and time Communication between the driver and the management centre and exchange messages and automate administrative processes such as delivery confirmation. This is a service that gives added value (“Infomobility”)

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Fleet management leads to improvements in efficiency and makes better use of vehicle capacity. Among other options, continuous monitoring and communication with the management centre offer the possibility of restructuring and adapting to changing requirements. Within this area, the following fundamental operations can be highlighted: -

Monitoring of vehicles and load Programming and changing routes Incident management

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The fundamental problem of fleet management is the organization, in real time, of vehicles that are responsible for the collection and delivery operations. This objective is also related with dynamic management algorithms, vehicle location, and the estimation of expected delays through a comparison between the expected travel time and an estimate of the real time dynamics. The route choice systems can be distinguished in two categories: systems that consider static data (choosing the route with the minimum distance criterion or minimum time and in the latter case, usually from the nominal speeds of the roads, or using historical data on traffic intensity) and systems based on a real-time estimation of travel time, from receiving traffic data from an information centre. Although an accurate quantification of the benefits of the use of fleet management systems is often not available, some of the expected improvements are: reduction of travel times, improved visibility and customer services, increased control over operations, reduced delivery times, improved strategic decisions in the medium and long term and reduced administrative costs. Some experiences show that centralized route planning systems reduce vehicle travel distances by 18% and decrease travel time by 14%.

2.4.3. Public Transport Fleet Control The situation regarding the management of public transport fleets is similar to that of freight transport in some aspects, although there are clear differences as well. Within systems of public transport operation, the following elements are involved: fleet management systems, traveller information systems and electronic payment systems. Vehicle positioning at every instant and submitting this information to management centres allows adjusting the service intervals and compensating mismatches. Also, other information available, such as travel times, occupation, etc, can be used to better align supply with demand. Thus, the main objectives aimed at improving the efficiency and safety of transportation systems can be summarized as: -

To improve service quality and regularity To improve the information provided to the user To match supply with demand To reduce operating costs and investment To improve flexibility To improve fleet control

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the level of user satisfaction (Daigle and Zimmerman, 2003). It was also found that fleet management reduces delays on bus routes (Jones, 1995; Giugno, 1995). A combination between traffic management systems and improving the efficiency of public transport is the priority system for these types of vehicles at intersections, which is also valid for emergency vehicles.

3. DETAIL AND ACCURACY REQUIREMENTS

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3.1. Specifications of Digital Maps and Positioning Systems In current navigation applications, the accuracy requirements of a digital map and positioning systems are limited, since it is sufficient for the vehicle to be located on a route and identify the location of the turns to be driven to achieve the predefined destination. Additionally, location information of places of interest, such as hotels, parks, important places, radars, etc, has been incorporated. In no case is it essential to have a precise knowledge of the geometry of the road or positioning in the lane. However, this additional information is of interest in applications that, for example, continuously determine the optimal speed, or prevent lane departure even in adverse weather situations in which the boundaries of the lanes are barely visible, or systems that alert of the presence of obstacles on the road, discriminating true from false alarms in complex road environments. Thus, since ADAS systems require much more detailed information, the data contained in the maps have also grown, requiring far greater detail and accuracy. This additional information has been called "safety attributes" by the European eSafety Forum (eSafety Forum, 2005). Along the same lines, the MAPS and ADAS subproject (T'Siobbel et al, 2004) of the European Integrated Project PREVENT presents an extensive list of data to be included in digital maps for ADAS for present and future applications. Another objective of the subproject was to harmonize the use of maps for different ADAS applications in order to have a valid architecture for them. The attributes of the map were classified into 4 major groups (geometry, feature, attribute and relationship) and analyzed whether, at present, they were available and how accurate they were. Moreover, attributes for the first versions of ADAS and for future versions were distinguished (Blervaque et al, 2004). The key safety attributes are shown in Table 3, which also indicates what the requirements of each system are. Among other works, the NEXTMAP project (Loewenau et al, 2002) proposed the establishment of compulsory requirements for digital maps in terms of the detail and precision to be used in new applications for driver assistance. Moreover, the HIGHWAY project seeks to define architecture and specifications to offer integrated safety and added-value services, in which the role of digital maps is central.

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X

Traffic signs

Hazard signs, Right-of-Way signs, etc.

X

Lane information

Number of lanes, lane width, divider characteristics between lanes

Traffic lights

Indication of traffic lights at an intersection.

Crossings

Pedestrian or cycle crossings, tram crossings, etc.

X

Accident hot spots

Potentially dangerous locations, plus a specification of these circumstances

X

X

X

X

X

X

X

X

Cooperative systems

Maximum speed limit, possibly modified by time of day, weather conditions, etc.

X X

Autonomous driving

Legal speed limit

X X

X

E-call

Longitudinal road gradient Transverse road gradient

X

Intelligent lighting system

Road grade Superelevation

X

Lane departure warning system

X

Adaptive cruise control

Characteristics of road geometry

Fuel consumption optimization

Horizontal alignment

Curve speed warning

Description

Onboard traffic signs information

Safety attribute

Intelligent Speed Adaptation

Table 3. Overview of key safety attributes.

(Intelligent) Navigation system

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X

X

X

X

X X X

X

X

X X

X

X

X

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Moreover, 3 levels of positioning accuracy can be defined along the lines established by EDMap Consortium (2004): “which road” (the level reached with current technology except in very unfavourable operating conditions), “which lane” (a state that is estimated achievable over the next decade) and “laterally where in lane” (a state that presents greater difficulties and a much longer time horizon to achieve). It should be noted that vehicle positioning can be done through various means, as shown in Table 4.

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Table 4. Available technologies for vehicle positioning. Technology Beacons on the infrastructure

Operation Beacons communicate with the vehicle and send the position data to the control centre

Advantages Proven technology

Drawbacks It requires an investment in infrastructure. It is not efficient for vehicles with no fixed route.

GPS

Positioning using satellite signals

It does not require investment in infrastructure. It can be operated at any place where there is satellite coverage. It can provide high accuracy

Signal can be lost in areas with high buildings, trees, tunnels, etc.

Radio

Triangulation of radio signals

It does not require investment in infrastructure. It can be operated at any place where there is radio coverage.

Signal can be lost in areas with high buildings, trees, tunnels, etc.

Inertial sensors

Using inertial sensors, path is estimated and the position is located on the digital map. It can support other positioning technologies.

It requires low investment

Low accuracy compared to other technologies

The most common method is satellite positioning, since it is the most widespread and versatile today. For the future, improvements to the system will increase accuracy and robustness, although no substantial changes are anticipated regarding the design of the vehicle positioning system. In the shadow areas where the GPS signal is lost, inertial sensors are often used, which allow an acceptable answer if the distance travelled is not very long. Beacon-based solutions have also been tested. This system can identify and position a vehicle, and transmit information bidirectionally between the vehicle and infrastructure.

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However, the use of beacons as a means of positioning has a number of disadvantages (that make other solutions more interesting alternatives) such as: -

-

Implementation is slow and progressive. The overall costs for the central government are high. The long process of implementation leads to areas with the system operating and others where it is not, which can lead to confusion. Inter-operability between countries is much more complicated. The robustness of the system is not assured and, in the event of a beacon failure there is not a repositioning until the next one, while, with GPS and onboard sensors, an almost immediate correction is possible. Maintenance costs are important. A very high density of beacons is required to have precise positioning.

Similar to positioning accuracy, different levels of detail included in the map can be defined and it should be noted that the final error combines both the map and the positioning errors and that the commonly allowable limits are those displayed in table 5.

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Table 5. Error budget for “which lane” and “where in lane” applications. Total error budget

Map error

Vehicle positioning error

which lane

1.5 m

0.5 m

1.0 m

where in lane

0.5 m

0.2 m

0.3 m

3.2. Map-Matching Algorithms Finally, we need to mention the problem of the interrelationship between the positioning system and location in the digital map. This problem is not trivial when dealing with maps and / or imprecise positioning systems, like the present ones. In such cases, providing a specific location of a vehicle on a roadway presents difficulties in complex environments such as urban environments, highway junctions and roads that are near and parallel to each other, where it is necessary to use other signals such as the ones provided by inertial sensors and to consider how the position has evolved over the preceding instants, which involves implementing complex and reliable algorithms. Map-matching algorithms try to overcome the inaccuracies of digital maps and positioning systems (e.g., White et al, 2000; Quddus et al, 2006). However, it should be noted that they may fail if there are several similar options. If satisfactory results are not achieved, some algorithms can go back and try other options. This solution is valid for navigation applications, but not for safety-related applications. Thus, these errors in the positioning assignment in the map increase the difficulty and limitations of using positioning in many ADAS applications.

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4. DEVELOPING AND UPDATING DIGITAL MAPS 4.1. Development and Updating Levels Obtaining a complete and updated digital map, according to the requirements set by the applications that will serve, is located in 3 areas which, in turn, dictate the means of obtaining the information: -

Digital map development

This phase includes the collection and processing of all static information in the levels of accuracy and detail specified by applications. This phase involves large volumes of data that must be managed, if possible, automatically. Similarly, if using multiple measurement techniques, the means to exchange information and mix various data sources should be taken into account.

-

Digital map corrections

Any corrections or updates require an accuracy level similar to that used in the actual development of the map. This phase includes static information updates or amendments to the geometry, the positioning of road signs, new infrastructure, etc. -

Dynamic variables collection

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Dynamic variables include those related to traffic or weather conditions and the road surface. A more frequent transmission of information is required and it is not necessary to store all this information in the map permanently.

4.2. Means of Developing Digital Maps The development of digital road maps can be obtained by different means. The intention to cover the entire road network means that, for the development of the digital map, the procedure should be fast and, according to the above specifications, reliable and accurate. One possibility is the use of detailed topographic maps from which it is possible to obtain a three-dimensional reconstruction of the geometry. This solution is viable, although many details are ignored and it is too costly and slow. Other options are digitized conventional maps, or using aerial photographs (Bendafi et al, 2000; Miles and Chen, 2004). However, the performance of these methods is often insufficient when digital maps are required to include information such as speed limits, stop signs, yield signs, travel restrictions, lane marking type or shoulder type, even though high-quality imagery allows capturing painted stopping locations, lane configurations and the placement of traffic signs (but not their information). So, when it is required to combine accuracy and speed of measurement and access to diverse information (geometric signs, intersections, etc.), a solution suggested years ago was the use of datalog vehicles (e.g. Yerpez and Ferrandez, 1986), and is still very common today (eg EDMap Consortium, 2004). The use of floating vehicles may be employed to develop a digital map as well as for updating it, and can be used in combination with other measurement

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techniques. Since the accuracy of the information requested must correspond to that required by the map, specific vehicles with expensive instrumentation are often used for this purpose. To measure the road geometry of the onboard instrumentation in the datalog vehicle, traditionally two options can be chosen, inertial systems and satellite positioning. Thus, Drakopoulos and Örnek (2000) use speed measurements and a gyroscopic sensor to deduce the horizontal alignment. Measurements are taken every 16 metres and the angular precision is 1º, which can lead to significant errors. Subsequently, a distinction is made between straight lines and curves based on the variation of the yaw angle. Other authors have used GPS positioning to obtain the road geometry. Among these, the works of Castro et al (2006) are worthy of mention. The cruising speed is approximately 80 km/h with a 1 Hz sampling frequency, which gives points that are spaced 20 metres apart. Ben-Arieh et al (2004) also use a GPS receiver with a I Hz sampling frequency and a travel speed of 100 km/h. Both cases need filtering and signal post-processing operations to smooth and eliminate the erroneous points. In order to increase accuracy, Imran et al (2006) use differential GPS with a 0.1 Hz sampling frequency. Other works using a GPS positioning signal to integrate the road geometry information into a GIS can be found in the Transportation Research Board (2002), while the EDMap Consortium (2004) presents a comparison of results using a differential GPS receiver and a low-cost 2D inertial measurement unit. However, the most common option is to combine both solutions, whose main characteristics are listed in Table 6. This combination seeks to exploit the advantages of each of the systems, since the accumulation of errors of inertial systems prevent their use over long distances if positioning in the lane is required, and corrections using absolute positioning are necessary.

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Table 6. Comparison of the performance offered by two methods for obtaining a digital map with an instrumented vehicle. Inertial measurement systems

Global positioning systems (GPS)

Accuracy

Equipment-dependent. Cumulative error due to integration of the measured variables (dependent on the distance travelled) Measurements are influenced by vehicle dynamics

Level of accuracy dependent on absolute or differential operation (errors of up to 10 or 20 metres)

Robustness

Uninterrupted signal

Signal loss and deterioration in tunnels, urban areas, etc, which lead to uneven results

Reference

It has no absolute reference that has to be externally entered

It provides an absolute reference

Type of information

It enables the horizontal and vertical geometry of the entire road to be obtained

It enables the horizontal and vertical alignment coordinates to be obtained except superelevation ratio

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In this regard, we need to point out the difference between real-time processing through Kalman filters that are subject to errors at critical points, such as exits of long tunnels where the GPS signal was lost and the error accumulated by the inertial system is relevant, and postprocessing data where corrections can be established based on the results of subsequent points of the path where a greater degree of precision is guaranteed (Berdjag and Pomorski, 2004; Lahrech et al 2004, Zhang et al, 2005; Toledo Moreo et al, 2007). Regarding the acquisition of information of other attributes of the map, automated means (for example, through image processing) can be used as well as manual ones. However, despite the benefits floating vehicles can provide, both for developing and updating maps, a number of issues need to be solved before they become widely used, such as communication systems, centralized management of large volumes of information, personal data privacy and ownership of the data obtained with vehicle fleets or private vehicles, the cost of equipment (as accuracy provided by low-cost equipment is not always acceptable, as cited in Jimenez et al, 2009b) and to define who has to bear with it. Finally, it is relevant to indicate the need for harmonization of information in order to achieve greater interoperability, because information often comes from multiple sources including public and private sectors, or even private vehicles (Sandgren and Ottoson, 2003). Obvious examples are also found when merging road geometry information obtained by a datalog vehicle with information on street names or the location of “hotspots” which are derived from statistical studies, for example. In this data-merge it should be noted that each source of information does not provide the same data in an identical manner (some are recruited directly by some methods but indirectly by others, for example). Moreover, the confidence level is not constant. These facts lead to the need for independent certification and management centres of these maps that integrate all the information and evaluate their quality (Hermans et al, 2003; T'Siobbel and van Essen, 2004).

4.3. Effort to Develop Digital Maps Within the EDMap project (EDMap Consortium, 2004) the relative effort to develop maps has been compared considering the accuracy and level of detail required to serve some assistance systems, with the effort that is required to develop existing navigation maps. Since the specifications imposed by some systems are too restrictive, some simplifications according to the road type can be introduced (e.g., the feature of location of stop signs on highways is removed and Curve Warning Systems are not implemented in residential areas where speeds are low). Figure 3 shows the results, in which the substantial savings in effort, due to the simplifications made, can be seen. Since the development of accurate and detailed maps throughout the rural and urban roads network can be very costly and not always necessary, the possibility of developing "hybrid digital maps" has been raised, in which the level of detail and precision increases at critical sections, such as bifurcations or curves, but remains at a lower level in other simpler sections. This approach is valid for most systems and significantly reduces the development work of map construction.

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Figure 3. Relative effort to develop the digital map for certain ADAS applications compared to the effort required for the development of a navigation map (effort = 1).

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4.4. Digital Map Updating In addition to the speed in map development, updating the information was identified as essential for some applications (for example, in dynamic ISA systems that use variables such as weather or road surface conditions to set safe speeds). These transient effects must be captured and processed in real time. In traffic management, the location of congestion points is vital, and delays in the transmission of such information may cause it to lose much of its value. For such applications, and even for an efficient navigation system, monthly or quarterly updates are not sufficient. It is of great importance to identify what level of upgrade is required for each application and what technology can provide those results. Within the ACT-Map project (Bastiaensen, 2003) the requirements that must be met in the upgrade process are defined: -

To avoid duplication of data. It should be possible to update part of the digital map. It should be possible to filter data in response to various criteria. In the updating process, permanent, temporary and dynamic updates should be accepted

Permanent or long-term changes should be reported by the agencies responsible for carrying them out. However, this update is usually not immediate. For example, there are experiences that have enabled applications accessible via the Internet in which the changes can be introduced, but with limited success. Moreover, updating dynamic variables has been done traditionally by means of sensors in the infrastructure (detection loops, overhead sensors, closed circuit television, environmental sensors, etc), as well as police and citizens reporting. However, as noted above, anther

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possibility is the use of sensorized “floating vehicles” that provide feedback to a management centre that is responsible, after appropriate processing, of its distribution to other users (Huber et al, 1999; Venhovens et al, 1999; Baum, 2003). Although corrections can be used for static variables and dynamic updates, its use tends to focus on the latter, since the former involve a high degree of accuracy in the instrumentation of the vehicle, resulting in high costs. Thus, for the correction of static variables, specific datalog vehicles are often used, while for the updating of dynamic variables fleet vehicles or private ones that provide services outside the development of maps can be used (passenger or goods fleets, for example) without excessive additional instrumentation. One advantage is the fact that a wide area network infrastructure can be covered at low cost, but in order to obtain reliable and useful information, the need has been established for a market penetration of sensorized units of 1% - 5%, which is only achievable if the cost of instrumentation is very low. A high percentage of vehicles already have many of the sensors and equipment necessary to conduct this task, it being only necessary to establish the communication protocol with an information collection and processing centre (Laborczi and Zajicek, 2003). It should also be noted that, if there are multiple information reception centres, communication between them can improve the data provided to users. The independence between the vehicle and infrastructure enables the use of the system on any road and reduces the cost of the infrastructure network of detectors. Despite the advantages, the measurement of traffic variables by means of these vehicles presents certain difficulties, since in order to provide travel time measurements between two points, the vehicles need to complete that journey and not all do so because they change route before reaching the destination, and there is the additional problem of the small number of units that are available. Finally, apart from being used for updating the variables of the digital map, positioning information can be used to analyze vehicle movements which are useful in planning applications to implement measures or deploy new infrastructures.

4.5. Data Quality Measures From the data in a digital map it is necessary to assess whether the level of precision and detail are consistent with the specifications, because this fact conditions in which applications they may be employed. The evaluation must separately consider two aspects: -

Accuracy in measuring the road geometry Accuracy and detail of the information of other attributes included in the map

In assessing the accuracy of the road geometry data contained in the digital map, absolute and relative errors should be distinguished and the impact of each on the systems and services that use this map should be studied. The most common quality indicator of a map’s accuracy is the one that can be obtained by following the procedure included in the Transportation Research Board (2002) where the positioning of random points on the map is compared to the actual locations obtained by means that are more accurate than what is required. Unlike the previous method based on a comparison of Cartesian coordinates, Castro et al (2006) propose the use of the mean and standard deviation of the lane width measurement

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when measuring the same route using different lanes, as an indicator of accuracy, the main disadvantage of this procedure being that results are highly conditioned by the path followed by the instrumented vehicle. The nature of the error that occurs when working with inertial measurement error is different from that which appears with satellite positioning systems. Therefore, given the cumulative nature of the error, there is a need to establish a methodology that considers the uncertainty prior to the completion of the measurements. The methodology proposed by Jimenez et al (2009th) is based on the law of propagation of uncertainty (European cooperation for Accreditation, 1999) and applies to all variables which are obtained indirectly. The method also allows estimating the maximum distance that can be travelled without correcting the measurements of an inertial measurement system and without the accumulated error exceeding a certain preset tolerance given by a driving assistance application. Regarding other attributes included in the map, such as the number of lanes, speed limits, stop signs, etc, the following potential errors in the digital map can appear: -

Omission Error: an attribute exists in reality, but is not on the map Commission Error: an attribute does not exist in reality, but is included in the map, which is usually due to failure in updating the map after a change Classification Error: an attribute is misidentified or classified (e.g., confusion in the information contained in a traffic sign) Position Error: an attribute of the map is not located in the correct position on the map

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Thus, improving the quality of the map lies in increased accuracy and a reduction of errors in identifying the attributes listed in it.

CONCLUSION Intelligent Transport Systems offer solutions to mitigate the negative impacts of an increase in road transport. In this context, digital maps provide support for many of these systems and services, either as crucial to their development (primary sensor), or to improving overall performance (sensor side). However, these new applications beyond traditional navigation systems demand a higher level of precision and detail in the maps and greater precision in the positioning of vehicles in them. Thus, digital maps, among other data, should have the following information: -

-

Three-dimensional information of the geometry with a precision of one or two orders of magnitude higher than maps currently used for navigation, including road grade and superelevation rate. Information on signs (speed limits and other signs of interest), lanes, shoulders, etc. Information and positioning of other elements in order to improve location and facilitate real obstacle detection and identification of hotspots. In addition, there are applications that require dynamic information on road conditions and weather. Table 7 lists the specifications that the most relevant systems require to provide optimum performance.

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Table 7. Requirements of different applications for positioning vehicles on digital maps.

Positioning as a primary (P) /secondary (S) sensor

X

Op

D

P

Onboard traffic sign information

X

X

X

C

P-S

Intelligent speed adaptation systems (fixed limits)

X

X

X

C

P

Traffic signs

X

Singularities location

Intelligent navigation systems

Transversal road section

P

Accurate vertical alignment

C

Accurate horizontal alignment

Op

Basic geometry (Current situation) X

Lane positioning

X

High accuracy

Navigation systems

Low accuracy (Current situation) Copyright © 2010. Nova Science Publishers, Incorporated. All rights reserved.

Digital map information

Map information updating:: corrections (C) /dynamic (D)

Positioning

Intelligent speed adaptation systems (variable limits)

X

X

X

X

X

X

C

P

Intelligent speed adaptation systems (dynamic limits)

X

X

X

X

X

X

D

P

Curve speed warning system

X

X

X

C

P

Fuel consumption optimization system

X

X

X

C

P

X

Adaptive cruise control (longitudinal control)

X

X

X

Op

C

S

Lane departure warning systems

X

X

X

X

C

P-S

C

S

C

P

Intelligent lighting system

X

E-call

X

X Op

X

X

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Map information updating:: corrections (C) /dynamic (D)

Positioning as a primary (P) /secondary (S) sensor

X

D

P-S

X

X

X

X

X

C

P-S

X

X

X

C

P-S

X

C

P-S

-

P

X

Cooperative systems: overtaking manoeuvre

X

Cooperative systems: rear-end and frontal collisions

X

Traffic signs

Singularities location X

Accurate vertical alignment

X

Accurate horizontal alignment

X

Basic geometry (Current situation)

X

X

Cooperative systems: intersections

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Transversal road section

Autonomous driving

Digital map information

Lane positioning

High accuracy

Low accuracy (Current situation)

Positioning

27

Vehicle location

X

X

Traffic monitoring and management

X

X

Op

D

P

Fleet control and management

X

X

Op

D

P

User – driver information

X

X

D

P

On the negative side, it should be noted that some Technologies are currently expensive, inaccurate and unreliable, have a limited functionality, lack of integration or are intrusive. Regarding digital maps, this fact arises from two perspectives: -

Current digital maps do not provide the level of precision and detail that some applications demand, which slows their implementation Today's tools do not allow the development and updating of accurate digital maps containing all the information that may be of interest for supporting different systems and services.

For this reason, it is necessary to seek to match the requirements and possible technologies, and coordinate deployment between industry and public partners. Highways: Construction, Management, and Maintenance : Construction, Management, and Maintenance, Nova Science Publishers, Incorporated,

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Finally, it should be noted that the development of positioning means and increasingly more accurate digital maps and the incorporation of more detail in them, is a line of research that is open and active as evidenced by the number of large-scale projects that consider these tasks as final goals, as well as horizontal activities to serve other applications. This is proved by the fact that these activities are included in key international strategic research agendas, such as those of the European Road Transport Research Advisory Council and the eSafety Forum.

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Intelligent Transport Systems and Services. Stockholm (Sweden), 21 al 25 de September de 2009. Jiménez, F., Naranjo, J. E. (2009). Nuevos requerimientos de precisión en el posicionamiento de vehículos para aplicaciones ADAS. DYNA Ingeniería e Industria. Vol 84, nº 3, pp 245-250 (in Spanish). Jones, W. S. (1995). ITS Technologies in Public Transit: Deployment and Benefits. USDOT, Federal Highway Administration, ITS Joint Program Office. Washington, D.C.: November 1995. Kawashima, H. (1991). Present status of Japanese research programmes on vehicle information and intelligent vehicle systems. DRIVE-conference, 4-6 February, Brussels. Khan, M. S. (2003). Leveraging wireless connectivity for digital maps in automobiles. 10th World Congress and Exhibition on Intelligent Transport Systems and Services. Madrid: 16 – 20 November 2003. King, G. F., Mast, T. M. (1987). Excess travel: causes, extent and consequences. Transportation research record, Vol 1111, pp 126-134. Kloeden, C. N., Mclean, A. J. (2001). Rural speed and crash risk. In: Proceedings of the Road Safety research, policing and education. 19-20 November, Melbourne. Kwon, E., Kim, S., Betts, R. (2003). Route-based dynamic pre-emption of traffic signals for emergency vehicle operations. National research Council. Transportation research Board. Laborczi, P., Zajicek, J. (2003). How to generate a graph-based. Map from floating car data?. 10th World Congress and Exhibition on Intelligent Transport Systems and Services. Madrid: 16 – 20 November 2003. Lahrech, C., Boucher, Noyer, J. C. Fusion of GPS and odometer measurements formap-based vehicle navigation. Proceedings of the IEEE International Conference Industrial Technology, Hammamet, Tunisia, December 2004, pp. 944–948. Loewenau, J., Hummelsheim, K., Bendafi, H., Entenmann, V., Marquet, J., Lilli, F., Sabel, H. (2002). Final enhanced map database requirements, NextMAP project, Deliverable 2.2. NextMAP Consortium, 64 p. Lu, M., Wevers, K., van der Heijden, R., Heijer, T. (2004). ADAS applications for improving traffic safety. IEEE International Conference on Systems, Man and Cybernetics. Lyles, R. W., Lighthizer, D.R., Drakopoulos, A., Woods, S. (1986). Efficacy of jurisdictionwide traffic control device upgrading. Transportation Research Record, Vol 1068, pp 3441. Maher M J, Hughes, P. C., Smith, M. J., Ghali, M. O. (1993). Accident- and travel timeminimising routeing patterns in congested networks. Traffic engineering and control, Vol 34, pp 414-419. Maycock, G., Brocklebank. P. Hall, R. (1998). Road layout design standards and driver behaviour. TRL Report 332, Crowthorne, UK. McDonald, M., Keller, H., Klijnhout, J., Mauro, V., Hall, R., Spence, A., Hecht, C., Fakler, O. (2006). Intelligent transport systems in Europe. Opportunities for Future Research. World Scientific. Miles, J, C., Chen, K. (2004). ITS Handbook. PIARC 2ª ed. Naranjo, J. E., García-Rosa, R., González, C., de Pedro, T., Alonso, J., Vinuesa, J. (2007). Crossroad Cooperative Driving based on GPS and Wireless Communications”, Lecture Notes on Computer Science, vol 4739, pp. 1073-1080.

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Naranjo, J. E., Jiménez, F., Armingol, J. M., de la Escalera, A. (2008) “Entornos inteligentes basados en redes inalámbricas: aplicaciones al transporte, automóvil inteligente/ conectado y seguridad vial”. Círculo de Innovación en las Tecnologías de la Información y las Comunicaciones. Informe de Vigilancia Tecnológica. (in Spanish). Naranjo, J. E., Jiménez, F., Aparicio, F., Zato, J. (2009). Comparative study of GPS and inertial systems for high precision positioning on highways. The Journal of Navigation. Vol 62, nº 2, pp 351–363. Naranjo, J. E., Bouraoui, L., García, R., Parent, M., Sotelo, M. A. Interoperable Control Architecture for Cybercars and Dual-Mode Cars, IEEE Transactions on Intelligent Transportation Systems, Vol 10, nº 1, pp 146-154, March 2009. Njord, J., Peters, J., Freitas, M., Warner, B., Allred, K.C., Bertini, R., Bryant, R., Callan, R., Knopp, M., Knowlton, L., López, C., Warne, T. (2006). Safety applications of intelligent transportation systems in Europe and Japan. Federal Highway Administration. U.S. Department of Transportation. Noronha, V., Goodchild, M. F. (2000). Map accuracy and location expression in transportation – reality and prospects. Transportation Research. Part C, Vol 8, pp 53-69. Organisation for Economic Co-operation and Development (2003). Road Safety. Impact of New Technologies. OECD Publications (France). Pérez, J. I., Moreno, A. (coords.) (2009). La contribución de las TICs a la sostenibilidad del transporte en España. Real Academia de la Ingeniería. (in Spanish). Pilli-Sihvola, Y., Jukka, L. (1995). Weather-Controlled Road and Investment Calculations. Finnish National Road Administration. Southeastern Region, Kouvola: December 1995. Quimby, A., Maycock, G., Palmer, C., Grayson, G. (1999). Drivers’ speed choice – an in depth study. TRL report 326. London, UK. Peltola, H. (1991). Seasonally changing speed limits. Finnra PTRC 1991 Proceedings of the Summer Annual Meeting, pp 193-198. Pomerleau, D., Jochem, T., Thorpe, C., Batavia, P., Pape, D., Hadden, J., McMilan, N., Brown, N., Everson, J. (1999). Run-off-road collision avoidance using IVHS countermeasures. Final report. Washington: National Highway Traffic Safety Administration (NHTSA). Prestl, W., Sauer, T. Steinle, J., Tschernoster, O. (2000). The BMW active cruise control ACC. SAE paper nº 2000-01-0344. Quddus, M. A., Ochieng, W. Y., Noland, R. B. (2006). Integrity of map-matching algorithms. Transportation Research. Part C, Vol 14, pp 283-302. Ran, B., et al. (2002). Evaluation of Variable Message Signs in Wisconsin: Driver Survey. University of Wisconsin Madison, WI: May 2002. Regan, M. A., Oxley, J. A., Godley, S. T., Tingvall, C. (2001). Intelligent Transport Systems: Safety and human factors issues. Clayton, Australia: Monash University Accident Research Centre. Reichart, G., Friedmann, S., Dorrer, C., Rieker, H., Drechsel, E., Wermuth, G. (1998). Potentials of BMW Driver Assistance to Improve Fuel Economy. FISITA World Automotive Congress, París, 27 September-1 October 1998. Revue, A., Nashashibi, F., Laurgeau, C. (2003). Contribution of GIS to danger prevention in curved roads. 10th World Congress and Exhibition on Intelligent Transport Systems and Services. Madrid: 16 – 20 November 2003.

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Rohr, S. N., Lind, R. C., Myers, R. J., Bauson, W. A., Kosiak, W. K., Yen, H. (2000). An integrated approach to automotive safety systems. SAE paper nº 2000-01-0346 Rumar, K. (1985). The role of perceptual and cognitive filters in observed behaviour. In Human behaviour and traffic safety (Evans, L., Schwing, R. C.) Plenum Press, New Cork. Rutley, K. S. (1972). Advisory speed signs for bends. TRRL Report LR 461. Transport and Research, Crowthorne, Berkshire. Salusjarvi, M. (1988). The speed limit experiments on public roads in Finland. Proceedings Road and Traffic Safety in two continents. Linköping, Sweden. Sandgren, U., Ottoson, C. (2003). Harmonisation of European Road Data – How to create large scale interoperability between national databases. 10th World Congress and Exhibition on Intelligent Transport Systems and Services. Madrid: 16 – 20 November 2003. Shah, V., et al (2003). An Assessment of the Potential of ATIS to Reduce Travel Disutility in the Washington, D.C. Region. 82 Annual Meeting of the Transportation Research Board, Washington, D.C. January 2003. Shladover, S. E., Tan, S.-K. (2006). Analysis of vehicle positioning accuracy requirements for communication-based cooperative collision warning. Journal of Intelligent Transportation Systems. Vol 10, nº 3, pp 131-140. Solomon, D. (1964). Accidents on main rural highways related to speed, driver and vehicle. Bureau of Public Roads, Department of Commerce, Washington. Stoneman, B. (1992). The effects of dynamic route guidance in London, Research Report 348, Transport Research Laboratory, Crowthorne, Berkshire. Sussman, J. M. (2005). Perspectives on Intelligent Transportation Systems. Springer. Tan, H.-S-, Huang, J. (2006). DGPS-based vehicle-to-vehicle cooperative collision warning: engineering feasibility viewpoints. IEEE Transactions on Intelligent Transportation Systems. Vol 7, nº 4, pp 415-427. Toledo-Moreo, R., Zamora-Izquierdo, M. A., Úbeda-Miñarro, B., Gómez-Skarmeta, A. F. (2007). High-Integrity IMM-EKF based road vehicle navigation with low-cost GPS/SBAS/INS. IEEE Transactions on Intelligent Transportation Systems, vol 8, pp 491-511. Transportation Research Board (2002). Collecting, Processing and integrating GPS data into GIS. Transportation Research Board, Washington D. C. T’Siobbel, S. (2003). The road to safety maps. 10th World Congress and Exhibition on Intelligent Transport Systems and Services. Madrid: 16 – 20 November 2003. T’Siobbel, S. et al. (2004). MapandADAS subproject. Safety Digital Maps requirements. Deliverable 12.31. Bruselas, MapandADAS Consortium. T’Siobbel, S., van Essen, R. (2004). The map enabled ADAS future. FISITA World Automotive Congress, Barcelona, 23-27 May 2004. Venhovens, P. J. T., Bernasth, J. H. Löwenau, J. P., Rieker, H. G., Schraut, M. (1999). The application of advanced vehicle navigation in BMW driver assistance systems. SAE paper nº 1999-01-0490. Wevers, K., Lu, M. (2007). Digital maps, driving systems and traffic safety: the data chain for in-vehicle map databases. Proceedings of the 6th European Congress and Exhibition on Intelligent Transport Systems and Services. (Aalborg 18 – 20 June 2007).

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White, C. E., Bernstein, D., Kornhauser, A. L. (2000). Some map matching algorithms for personal navigation assistants. Transportation Research. Part C, Vol 8, pp 91-108. Widmann, G. R., Daniels, M. K., Hamilton, L., Humm, L., Riley, B., Schiffmann, J. K., Schneker, D. E., Wishon, W. H. (2000). Comparison of lidar-based and radar-based adaptative cruise control systems. SAE paper nº 2000-01-0345. Wördenweber, B. (2001). Driver assistance through lighting. 17th International Technical Conference on the Enhanced Safety of Vehicles. Amsterdam, 4-7 June 2001. Yerpez, J., Ferrandez, F. (1986). Road characteristics and safety. Identification of the part played by road factors in accident generation. INRETS, Arcueil. Zhang, P., Gu, J., Milios, E., Huynh, P. (2005) Navigation with IMU/GPS/digital compass with unscented kalman filter. Proceedings of the IEEE International Conference of Mechatronics Automation, Niagara Falls, Canada, July 2005, pp. 1497–1502.

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Chapter 2

FINANCING HIGHWAY CONSTRUCTION: REVOLVING LOAN SYSTEMS AS PAY-AS-YOU-USE Jay Eungha Ryu Political Science and the Voinovich School of Leadership and Public Affairs, Ohio University, Bentley Annex 237 Athens, Ohio

ABSTRACT

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Deteriorating conditions of national highway systems have been partly attributed to the lack of financial resources. In response to the worsening highway infrastructures and attendant negative impacts on the national economy, federal legislations allowed state governments to use federal highway assistance funds as the equity capitalization funds for the State Infrastructure Bank (SIB) programs. The key innovations in SIB programs are revolving loan fund and leveraged loan fund structures. Those loan funding structures stretch scarce financial resources to expedite highway constructions. This chapter investigates the revolving loan systems (RLS) as viable PAY-AS-YOU-USE financing alternatives to traditional tax revenues. Elaborate computer simulations tested potential fiscal impacts of RLS loan interest rates, RLS loan shares, RLS borrowing interest rates, RLS borrowing shares, maturities of loans and borrowings, and their interactions. The simulation results reveal that RLS loan interest rates, RLS borrowing shares, interactions of RLS borrowing shares and loan interest rates, and most of all, maturities of loans and borrowings are the most critical parameters to maximize the amount of annual loans made to potential highway construction projects. This chapter further suggests three major caveats in implementing RLS programs: minimizing stimulating unnecessary investment projects, reinforcing marketing of RLS programs, and assessing project and credit capacities of the entities that borrow from RLS programs.



Email: [email protected]

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FISCAL STRINGENCIES AND NATIONAL HIGHWAY SYSTEMS Numerous empirical studies conducted by public sector economists have indicated that public capital stocks have significant impacts on jurisdictional economies. A one percent increase in public capital stocks typically stimulates the jurisdictional economies by somewhere between 0.1 to 0.4 percent (Aschauer 1990; Munnell 1990; Moomaw and Williams 1991; Mullen, Williams, and Moomaw 1996). Thus, constructing public infrastructures and maintaining them in good conditions are directly related to healthy economies. Unfortunately, national highway systems have been significantly deteriorating during the recent decades. One fundamental reason for the worsening highway conditions has been the lack of financial resources needed to repair highway systems as well as construct new ones. To make things worse, postponing investment in national highway systems results in substantially increased future costs for repairing. For instance, deferring one dollar in highway resurfacing for two years will require four dollars for repairing the damages caused by the delay in spending (U. S. GAO 1996). Trend data of state and local highway finances indicate that federal transportation grants, one of the major revenue sources for highway projects, have been consistently declining since 1960 up to the late 1990s (Fisher 2003). As a result, the share of state and local highway expenditures relative to state and local general expenditures substantially declined from the early 1960s to the late 1970s and then leveled off until the late 1990s (Fisher 2003). To address the shortage in financial resources needed for national highway systems, the federal government attempted to develop innovative ways of financing for highway systems. The common mechanism of the various financing alternatives was the concept of leveraging federal funds for state and local highway projects. The purpose of leveraging was to stretch scarce financial resources to expedite various state and local highway projects (Ryu 2006, 2007). This chapter investigates how the mechanism of leveraging augments financial resources through simulations. The simulations will show what parameters of the leveraging systems bring about the most bang for your buck. In the next section, one example of current leveraging systems implemented by multiple states will be introduced first.

STATE INFRASTRUCTURE BANK (SIB) PROGRAMS Throughout the 1970s and the 1980s, many states initiated state bond banks to support credit-poor local communities to borrow at lower interest rates from the credit market for their infrastructure projects. One limitation of bond banks was that once borrowers repaid their principals to investors, there would be no balance in the bond banks. To address the limitation, several states attempted to develop State Infrastructure Bank (SIB) programs in the 1980s (Humphrey and Maurice 1986; Ryu 2006). In essence, the critical aspect of SIB programs was to keep their positive fund balance on a more permanent basis. There were two key mechanisms in the SIB programs for such purposes. First, under a basic revolving loan fund system, SIB programs can loan their fund balance or capitalization equity funds to public or private projects. Project-based revenues can then be used to repay original principals and interest payments. Multiple rounds of loans and repayments will perpetuate future rounds of loans. Alternatively, under a leveraged revolving

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fund system, SIB programs can borrow directly from the credit market (U. S. GAO 1996; Ryu 2006). For instance, Title VI of the Clean Water Act amendments of 1987 allowed state revolving fund structures to borrow from the credit market by using federal assistance funds as collateral (Holcombe 1992; Ryu 2006). Earlier experiences of states that implemented SIB programs reported a couple of common limitations. SIB programs lacked sufficient capitalization equity funds to initialize loan revolving. The capitalization equity funds were financed from various revenue sources such as dedicated tax revenues, state general funds, or federal grants. However, many state legislatures prohibited the use of federal assistance funds as SIB capitalization funds (U. S. GAO 1996). Three federal legislations ultimately cleared the barriers so that SIB programs could be a viable financing alternative to traditional tax revenues. The National Highway System Designation Act of 1995 (NHS; P. L. 104-59) allowed states to use federal funds for two options: as SIB capitalization equity funds or as collateral to borrow from the credit market. Ten pilot SIB states were authorized to use up to 10 percent of federal assistance funds during 1996 and 1997. As part of the 1997 appropriation for the federal Department of Transportation (DOT), Congress additionally appropriated $150 million for SIB programs and opened SIB programs for all states. The Transportation Equity Act for the 21st Century of 1998 (TEA-21; P. L. 105-178) further lifted the ten percent limit over federal funds and authorized state SIB programs to capitalize their equity funds with federal funds provided in FY 1998 though FY 2003 (Ryu 2007; Yusuf and Liu 2008). The Safe, Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for Users (SAFETEA-LU; P. L. 109-59) further extended the authority of state SIB programs to capitalize their equity funds through FY 2009 with federal funds provided in FY 2005 (Yusuf and Liu 2008). Empirical studies show that one dollar federal assistance fund deposited into SIB equity funds stretch statewide spending on highway-related projects by about five to seven dollars (Ryu 2006, 2007). In addition, SIB programs enable substantial savings in borrowing costs to local project entities that borrow from SIB programs. Total SIB loan agreements and dollars to various state capital projects have grown more than 100 times from 1997 to 2006 (Yusuf and Liu 2008).

REVOLVING LOAN SYSTEMS (RLS) AS PAY-AS-YOU-USE The two mechanisms of leveraging, e.g., the revolving loan fund system and the leveraged revolving fund system, which will be referred to as Revolving Loan Systems (RLS) in this chapter, can be defined as a Pay-As-You-Use (Pay-Use) financing method. Infrastructure capital projects are frequently financed from long-term debt. Since initial costs of construction need a substantial amount of upfront expenditures, project entities often rely on bond issuance. This will somehow smooth peaks and valleys in revenue streams that might be caused by construction projects. In addition, it will also enhance intergenerational equity because future users of the capital facilities will pay for their use. In contrast, many states use a Pay-As-You-Go (Pay-Go) method for capital projects to be financed from state general fund revenues (Wang, Hou, and Duncombe 2007). However, a Pay-Go financing method is mostly confined to relatively small amounts of capital project costs, such as maintenance or repairing

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Jay Eungha Ryu

of capital infrastructures (Yusuf and Liu 2008). For these reasons, RLS programs can be a viable financing alternative to traditional tax revenues. This is especially true for constructing large-scale capital facilities such as highway systems that require substantial amounts of upfront construction costs. The next section shows the mechanisms of typical RLS programs that are developed based on currently existing SIB programs. It shows details of simulation processes and their results so that practitioners operating similar revolving loan programs can replicate them. More importantly, it also shows what parameters of RLS programs can maximize the streams of financial resources for highway constructions.

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MECHANISMS OF REVOLVING LOAN SYSTEMS (RLS) Table 1 shows the mechanism of an imaginary RLS program. It shows how $10 million can be stretched by about three (in present value) to six (in current value) times the opening RLS fund balance (i.e., $10 million), which will be defined as the leverage ratio based on Ryu (2007). Some assumptions will be made on the base case scenario for highway construction projects. The going market interest rate will be set at 9 percent because the market interest rate tends to be about four percent higher than the inflation rate (Holcombe 1992). The RLS loan interest rate is set at 7 percent because federal legislations require the former to be lower than the market interest rate. The share of total available RLS funds to be used as collateral for borrowing is set at 30 percent, the share of total available RLS funds to be loaned is set at 80 percent, and maturities of all loans and borrowings are set at 20 years.1 Later on, these parameters will be changed to see what factors are the most crucial in replenishing the streams of revenues in RLS. The opening RLS fund balance (Column A) in Table 1 is the equity fund to capitalize RLS programs. In typical SIB programs, the capitalization equity fund was financed from federal assistance funds. Many states also used state funds for the equity fund balance. In Table 1, it is assumed that an imaginary RLS program has $10 million as start-up funds. At this moment, there are no other funding sources (Column B). Therefore, the total available funds are still $10 million (C = A + B). The borrowed fund (Column D) is the amount of dollars that this RLS program can borrow from the credit market using the total available funds as collateral. The NHS of 1995 allowed SIB programs to use federal assistance funds as collateral for further borrowing (U. S. GAO 1996). In actuality, there were not many SIB programs that borrowed directly from the credit market through bond issuance (U. S. DOT 2002). However, Table 1 shows what happens in the RLS fund balance in conjunction with a leveraged revolving fund system. As assumed above, 30 percent of the total available RLS funds were used as collateral. In addition, RLS programs can enter into a borrowing agreement similar to a typical mortgage contract because they can use the 30 percent of their fund balance as securities. Thus, the interest rate at which RLS programs can borrow is set at 7 percent, which is lower than the going market interest rate of 9 percent. RLS programs’ borrowing is further assumed to be made at the beginning of

1

Most SIB programs did not issue bonds frequently, were not subjected to the requirement to maintain minimum reserve funds, and the maturities of loans varied from 30 to 10 years or less (U. S. DOT 2002; Ryu 2007).

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each year and annual mortgage payments are also made at the beginning of the same year.2 The annual mortgage payment (Column E) is $264,653 that will be paid at the beginning of year 1 through to the beginning of year 20. The subtotal is the sum of total available funds and annual payments minus annual mortgage payments (F = C + D – E). At the end of year 1, 80 percent of the subtotal will be loaned to various highway construction projects (Column G).3 RLS loan repayments and interest payments are assumed to be made at the end of year 2 (i.e., one year after loans are made). Since parts of the loan and interest will be repaid at the end of year 2, interest payments from the RLS loans made to highway projects will be zero in year 1 (Column H and Column I). The ending RLS fund balance will be the subtotal minus total loans made plus the sum of loan repayments and interest payments (J = F – G + H + I). The ending fund balance of this RLS program in year 1 is the opening RLS fund balance for year 2. Although all the procedures in year 1 will be repeated in year 2, there are a couple of caveats for year 2. Annual mortgage payments (Column E) in year 2 will be the sum of the annual payments made in year 1 (e.g., $264,653) and the mortgage payment associated with the second year borrowing of $764,121 (Column D). This will be the same for the entire year beginning from year 2. RLS loan repayments (Column H) and loan interest payments (Column I) are made for the RLS loan distributed at the end of year 1 (e.g., $10,188,278 in Column G). An equal amount of RLS loans is assumed to be paid annually for the entire maturity. Thus, the loan repayments (Column H) in year 3 are the sum of the repayments in year 2 and the repayments of RLS loans made at the end of year 2. Ryu (2007) assumed that equal annual payments of interests over the original principal would be made over the entire maturity period for a simulation of SIB programs. It is assumed in this simulation, however, that interest payments will be made over the balance of principal for each RLS loan much similar to the interest payment schedules found in straight serial bonds. This is a reasonable assumption because revolving loan programs such as SIB programs were generally supposed to save borrowing costs for credit-poor project entities (U. S. DOT 2002; Yusuf and Liu 2008). Thus, the RLS interest payments in year 2 will be the product of the loans made in year 1 (e.g., $10,188,278) and the RLS interest rate of 0.07. The RLS interest payments in year 3 will be the sum of the interest payments made in year 2 (e.g., $713,179) plus the product of 0.07 and the RLS loans made in year 2 (e.g., $2,383,303) subtracted by the loan repayment made in year 2 (e.g., $509,414). The same formula will be applied to all remaining years.4 2

This way, RLS programs can retain a slightly higher amount of fund balance. RLS programs can also make immediate loans at the beginning of year 1. However, the result from this scenario will not make any big difference. In addition, we can assume that the RLS programs might need some time for appraising applications for RLS loans. In that case, RLS loans are likely to be made at the end of each year. 4 Formulas for interest payments from year 2 to year 4, for instance, are shown as the following: Year 2: $10,188,278 * 0.07 Year 3: ($10,188,278 - $509,414) * 0.07 + $2,383,303 * 0.07 Year 4: [$10,188,278 – ($509,144 * 2)] * 0.07 + [$2,383,303 – ($628,579 - $509,144) ] * 0.07 + $1,587,006 * 0.07 Therefore, the formula for interest payment in Year 3 will be the interest payment in Year 2 plus the difference between the interest payment in Year 3 and that in Year 2. By rearranging these computations, the interest payment for Year 3 can be computed as shown in the text. When repeated for the remaining years, the same pattern of formula emerged. 3

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Table 1. Mechanisms of Revolving Loan System (RLS) Structures.

Loans Made (G)

Loan Repayment (H)

Interest Payment (I)

Ending RLS Fund Balance (J)

PV of Loans Made (K)

12,735,347

10,188,278

0

0

2,547,069

10,188,278

332,062

2,979,128

2,383,303

509,414

713,179

1,818,419

2,186,516

545,526

380,187

1,983,758

1,587,006

628,579

844,352

1,869,682

1,335,751

1,869,682

560,905

429,669

2,000,918

1,600,734

707,929

911,442

2,019,554

1,236,061

0

2,019,554

605,866

483,117

2,142,304

1,713,843

787,966

973,938

2,190,365

1,214,130

2,190,365

0

2,190,365

657,109

541,086

2,306,389

1,845,111

873,658

1,038,749

2,373,685

1,199,195

7

2,373,685

0

2,373,685

712,106

603,906

2,481,885

1,985,508

965,914

1,106,751

2,569,042

1,183,893

8

2,569,042

0

2,569,042

770,713

671,896

2,667,858

2,134,286

1,065,189

1,178,123

2,776,883

1,167,528

9

2,776,883

0

2,776,883

833,065

745,387

2,864,561

2,291,649

1,171,903

1,252,959

2,997,775

1,150,101

10

2,997,775

0

2,997,775

899,332

824,724

3,072,383

2,457,906

1,286,486

1,331,342

3,232,304

1,131,688

11

3,232,304

0

3,232,304

969,691

910,268

3,291,727

2,633,381

1,409,381

1,413,341

3,481,068

1,112,369

12

3,481,068

0

3,481,068

1,044,320

1,002,396

3,522,992

2,818,394

1,541,050

1,499,021

3,744,670

1,092,220

13

3,744,670

0

3,744,670

1,123,401

1,101,500

3,766,571

3,013,257

1,681,970

1,588,435

4,023,719

1,071,317

Year

Opening RLS Fund Balance (A)

Other Funding Sources (B)

Total Available (C)

Borrowed Fund (D)

Annual Payment (E)

Subtotal (F)

1

10,000,000

0

10,000,000

3,000,000

264,653

2

2,547,069

0

2,547,069

764,121

3

1,818,419

0

1,818,419

4

1,869,682

0

5

2,019,554

6

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Subtotal (F)

Loans Made (G)

Loan Repayment (H)

Interest Payment (I)

Ending RLS Fund Balance (J)

PV of Loans Made (K)

1,207,989

4,022,846

3,218,277

1,832,633

1,681,625

4,318,827

1,049,733

1,295,648

1,322,288

4,292,187

3,433,750

1,993,547

1,778,620

4,630,604

1,027,538

4,630,604

1,389,181

1,444,838

4,574,947

3,659,958

2,165,234

1,879,434

4,959,658

1,004,798

0

4,959,658

1,487,897

1,576,097

4,871,458

3,897,167

2,348,232

1,984,065

5,306,589

981,578

5,306,589

0

5,306,589

1,591,977

1,716,537

5,182,028

4,145,622

2,543,090

2,092,491

5,671,986

957,942

19

5,671,986

0

5,671,986

1,701,596

1,866,648

5,506,934

4,405,547

2,750,371

2,204,668

6,056,426

933,948

20

6,056,426

0

6,056,426

1,816,928

2,026,933

5,846,420

4,677,136

2,970,649

2,320,530

6,460,463

909,655

64,090,113

Sum of present value of total annual loans

Year

Opening RLS Fund Balance (A)

Other Funding Sources (B)

Total Available (C)

Borrowed Fund (D)

Annual Payment (E)

14

4,023,719

0

4,023,719

1,207,116

15

4,318,827

0

4,318,827

16

4,630,604

0

17

4,959,658

18

Sum of total annual loans

32,134,240

42

Jay Eungha Ryu

These entire procedures will be perpetuated due to the revolving nature of RLS funds. The surplus of RLS funds can be loaned to various highway projects. Table 1 indicates that the original corpus of the equity funds (e.g., $10 million) is stretched to as much as $64,090,113 in the total amount of loans. When discounted by the going market interest rate of 9 percent, the total loans made amount to $32,134,240 with the leverage ratio of about 3.2.

This section investigates what parameters maximize the leverage ratios. Previous studies on SIB programs reported that the SIB loan rate is the most critical factor for the viability of SIB programs. Federal legislations require that SIB loan rates must be lower than the going market interest rate as far as SIB programs use federal assistance funds as the SIB equity funds. In this case, however, the corpus of the SIB equity fund might be damaged if the SIB loan rate is too low (Ryu 2007). This will detract from the perpetual nature of SIB programs. Figure 1 shows how different RLS loan rates influence total annual loans made from RLS programs. As RLS loan interest rate decreases, the stream of annual loans also decreases. One notable finding is that even at the lowest rate of one percent the leverage ratio was about 1.9 (not reported in Figure 1). Thus, maintaining higher RLS loan interest rates will benefit RLS programs but RLS programs can still support various highway projects even with very low loan interest rates. The interest rates of previous SIB loans ranged between three to six percent (U. S. DOT 2002; Yusuf and Liu 2008). Many SIB programs also relied on floating loan interest rates that adjust SIB loan interest rate depending on market conditions (U. S. DOT 2002). Although Figure 1 is constructed based on the assumption of fixed loan rates, it implies that RLS loans with floating loan interest rates will be fluctuating between two boundaries; e.g., the base case scenario with the loan rate of seven percent and that with one percent. 2,500,000 Base Case

2,000,000 Loans Made

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PARAMETERS MAXIMIZING RLS LOANS

RLS r = 0.05

1,500,000 1,000,000

RLS r = 0.03

500,000 RLS r = 0.01

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 18 19 20 Year

Figure 1. Revolving Loan System (RLS) Loan Interest Rate and Annual Loans.

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2,500,000 2,000,000

Base Case

Loans Made

RLS Borrowing Rate = 0.05 RLS Borrowing Rate = 0.03

1,500,000

RLS Borrowing Rate = 0.01

1,000,000 500,000 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Year

Figure 2. Revolving Loan System (RLS) Borrowing Interest Rate and Annual Loans. 4,000,000 Base Case

3,500,000 Loans Made

3,000,000 Loan Share = 0.6

2,500,000 2,000,000

Loan Share = 0.4

1,500,000 1,000,000

Loan Share = 0.2

500,000 -

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2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 18 19 20 Year

Figure 3. Share of Total Revolving Loan System (RLS) Fund to Be Loaned and Annual Loans.

In slight contrast, the interest rate at which RLS programs can borrow from the credit market does not substantially influence the stream of annual RLS loans. Figure 2 shows that as the RLS borrowing interest rate declines, the streams of annual loans decrease as well. However, the difference is not substantial. Similarly, Figure 3 suggests that the share of RLS loans to be made from the RLS fund balance does not significantly impact the annual loan streams, either. Ryu (2007) showed that except for the loan share of 20 percent, annual loan streams began to increase for later project years. The difference in this simulation from Ryu (2007) is that the loan and borrowing maturity is set at 20 years. More critically, this simulation assumed that RLS loan interest payments are required only on the balance of the remaining principal balance much like serial bonds. This assumption is supposed to provide more benefits to borrowing project entities while the annual loan streams will be slightly diminishing. The choice between the two observations will be policy discretionary choices for RLS administrators.

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Jay Eungha Ryu 4,000,000 Base Case

3,500,000

Loans Made

3,000,000 Borrowing Share = 0.7

2,500,000 2,000,000

Borrowing Share = 0.5

1,500,000 1,000,000

Borrowing Share = 0.1

500,000 2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 18 19 20 Year

Figure 4 shows that the borrowing of RLS fund balance shares to be used as collateral significantly affects annual loan streams. For instance, when the share of total available RLS funds (Column C in Table 1) is set at 70 percent or 50 percent, annual loan streams substantially increase. Ryu (2007), with different assumptions, reported that the difference in borrowing shares in SIB programs does not influence annual loan streams. With different RLS structures as in this chapter, however, practitioners might take advantage of varying borrowing shares to maximize annual loan streams. There is one more interesting finding from the simulation results. Although RLS borrowing interest rates do not significantly affect annual loan streams, there is an interaction effect between RLS borrowing shares and borrowing interest rates. As shown in Figure 5, when the RLS borrowing share is set at a fairly high rate of 70 percent, the change in RLS borrowing interest rate results in substantial variation in annual loan streams. In particular, if RLS programs can borrow at a very low interest rate of three or one percent, the annual loan streams show observable increases. However, there is one caveat with this scenario along with Figure 4 and Figure 5. Earlier experiences with SIB programs indicate that many state legislatures were reluctant to open up venues for debt financing. Therefore, only a couple of SIB states actually relied on the leveraged revolving fund system through borrowing directly from the credit market (U. S. DOT 2002). Obtaining legislative supports for additional borrowing will be one of significant challenges for RLS programs. 4,500,000

4,000,000

3,500,000

Second Case

3,000,000

Loans Made

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Figure 4. Share of Revolving Loan System (RLS) Balance to Be Used as Collateral and Annual Loans.

RLS Borrowing Rate = 0.05

2,500,000

2,000,000

RLS Borrowing Rate = 0.03 1,500,000

RLS Borrowing Rate = 0.01

1,000,000

500,000

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

Year

Figure 5. Interaction of Revolving Loan System (RLS) Borrowing Share (0.7) and Borrowing Rate. Highways: Construction, Management, and Maintenance : Construction, Management, and Maintenance, Nova Science Publishers, Incorporated,

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6,000,000 5,000,000

Loans Made

Base Case

4,000,000 3,000,000 2,000,000 Maturity = 5

1,000,000 2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 18 19 20 Year

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Figure 6. Maturity of All Loans and Annual Loans.

Finally, Figure 6 shows that the maturity of loans and borrowings is what most substantially influences the annual streams of total loans made to highway projects. Previous SIB experiences indicated that longer maturity periods of SIB loans will damage the corpus of SIB fund balances. Figure 6 partially confirms this concern. When the maturity of all RLS loans and borrowings are greatly shortened to five years, annual loan streams make sudden jumps across most of the 20 years. The leverage ratio, not reported in Figure 6, is as large as about 6.6. When the periods of RLS mortgage borrowings are shorter, mortgage-related payments grow much larger than those in the base case scenario. However, the increase in mortgage-related payments is offset by the huge increase in RLS loan repayments and interest payments.1 Thus, administrators of RLS programs are recommended to loan RLS funds to highway projects that can be completed in shorter time periods. Overall, RLS programs can significantly stretch scarce financial resources for highway constructions. Another interesting finding (not reported in the above simulations) is that whenever there are additional sources of funds to be used as RLS capitalization equity funds (e.g., Column B in Table 1), leverage ratios significantly jumped. For instance, the leverage ratio increases to about six with inclusion of $1 million for additional capitalization equity funds. However, the mechanisms of RLS programs are not without limitations. In the next section, some caveats for successful implementation of RLS programs are discussed.

SUGGESTIONS FOR SUCCESSFUL IMPLEMENTATION OF RLS PROGRAMS Minimizing Stimulating Unnecessary Investment Projects RLS programs can stretch scarce financial resources to expedite various statewide highway construction projects. However, there are some caveats regarding increased public 1

Although the formulas for interest payments under the five year scenario are much more complicated than those shown in Footnote 4, similar patterns in the formulas emerged for computer simulations.

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investment expenditures especially through cheaper credits available to public project entities in particular. The U. S. economy has been experiencing one of the worst economic downturns since the Great Depression. To address the sluggish economy, the Federal Reserve Bank has pumped substantial amounts of dollars into the private market system through increased money circulation (Krugman 2009). Although increased money circulation might stimulate the sagging U. S. economy, critiques of the measures are concerned about their potential threats (Woods 2009). The going market interest rate primarily reflects the true state of demand for and supply of capitals. When the Fed artificially lowers the interest rate by increasing cheaper credits, however, businesses might get a wrong signal that it is the time for their long-term investment. The increase in cheap credits might misdirect businesses into long-term capital projects that will ultimately turn out to be unnecessary projects (Woods 2009). RLS programs are typically involved in loaning and borrowing at cheaper interest rates. RLS programs operated by state governments might cause similar kinds of distortion in the state economy (i.e., stimulating unnecessary public highway projects). Thus, maintaining an adequate level of loaning and borrowing would be crucial so as not to distort the state economy. However, choosing an adequate level of borrowing and loaning seems quite challenging. A practically viable alternative to make such a judgment would be to assess the trends of revenues and expenditures for highway systems. For instance, Figure 7 shows the trends of revenues and expenditures of stateadministered highways per state from 1970 to 2006, measured in 2006 constant dollars. 2500

Dollar (in Millions)

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2000

user revenue

1500

other state revenue federal fund total revenue

1000

total expenditure forecast revenue

500

0 1965

1970

1975

1980

1985

1990

1995

2000

2005

2010

Year

Note: Forecast revenues are constructed based on ARIMA (3, 1, 0). Data source: Federal Highway Administration, Highway Statistics (various versions). Figure 7. Revenues and Expenditures of State-administered Highways per State from 1970 to 2006 (in 2006 constant dollars).

Between the early 1970s and the mid-1980s, total revenues for state-administered highways were substantially declining. For the same period, highway user revenues and federal funds were declining, which might be the main reason for the decline in the total Highways: Construction, Management, and Maintenance : Construction, Management, and Maintenance, Nova Science Publishers, Incorporated,

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revenues. Since the mid-1980s up to the early part of 2000s, the total revenues and attendant total expenditures for state-administered highways have been growing. Since the early 2000s, the total revenues and expenditures have shown a declining pattern due possibly to economic recessions. For the same period, user revenues and federal funds also show a dip or a declining pattern. The decline in the total revenues is significantly attributable to the sluggish national economy and as such, it might be a deviation from the projected trend of the total revenues that might have been collected if there was not such a sudden economic downturn. Figure 7 also shows a forecast trend line of total revenues for state-administered highways for years 2003 through 2006, which was estimated using the actual data from 1970 to 2002. A yearly average gap between the forecast line and the actual observations of total revenues for state-administered highways is about $ 52 million. As of 2005, there were about 38 states operating SIB programs for highway projects, which is a program similar to a typical RLS program (Yusuf and Liu 2008). The total loan amount rendered by the SIB programs was about $5 billion. This means that a state SIB program loaned approximately $ 132 million to statewide highway projects in 2005. Although Figure 7 covers revenues and expenditures for state-administered highways while the SIB loans were allocated to broader highway projects, the average actual loan amount per state far exceeds the revenue gap of $ 52 million estimated for state-administered highways.2 This indicates that a SIB program might have loaned more than what might be an economically neutral level of credit circulation. Therefore, RLS administrators need to first assess whether RLS loan programs might stimulate overly excessive and unnecessary statewide highway projects by providing cheaper loans before designing and implementing RLS programs.

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Maximizing Marketing of RLS Programs Many states have already been implementing RLS programs such as SIB programs with substantially increased, in recent cases excessive, loan amounts, as reported above. However, entities that plan to launch highway projects are still not aware of RLS programs that can provide them with cheaper credits. An earlier experience with SIB programs casts relevant advice for implementing RLS programs. A survey conducted by the U. S. DOT (2002) indicates that an effective marketing program might enhance SIB activities. Outreach efforts through mailings, workshops, and meeting with possible loan applicants as well as external stakeholders would improve the potential of RLS programs as a viable revenue alternative. In general, small project entities might not have sufficient information sources or resources to collect and thoroughly review the availability and adequacy of RLS programs for their potential highway projects. As clearly shown in above simulations, the shorter maturity of capital projects will maximize the amount of RLS loans, other things being equal. It is highly likely that small entities might be more interested in highway projects that they can complete quickly. Therefore, enhanced marketing efforts for smaller project entities with some further support services would maximize the loan circulation from RLS programs. However,

2

This implies that SIB programs might have added up extra funds in their programs (e.g., Column B in Table 1) or might have initiated additional rounds SIB programs in different years.

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maximization of loan circulation might stimulate unnecessary highway projects as mentioned above. For this reason, a third suggestion is presented.

Assessing Project and Credit Capacities of Borrowing Entities

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It is highly likely that project entities might borrow from RLS programs if RLS programs offer cheaper credits even when the entities do not have project capacities and credit capacities. The entities might assume that they could expand their project capacities through extra credits they can easily obtain from RLS programs. The entities might start new highway projects only because they can obtain cheap credits. Thus, RLS loans need to be limited to the project entities with fundamental project capacities or clear and reasonable plans for highway projects. In addition, if RLS loans are distributed to the project entities with poor credit capacities that likely default on debt repayment, the permanent feature of RLS programs will be significantly damaged. Therefore, credit capacities of borrowing project entities should also be carefully assessed before offering RLS loans. Yusuf and Liu (2008) assert that there should be discrimination among various project entities from an equity perspective as well. If all project entities can benefit from equally lower credit costs regardless of their credit worthiness, poorly credited entities might be preferentially treated. Under this condition, RLS loans at equally lower interest rates would be de facto and unfair subsidies to those entities. Of course, administrative costs for assessing credit capacities of borrowing entities might be additionally incurred for RLS programs. However, the administrative costs would be much smaller than the expected damage that might be caused by offering RLS loans to project entities with poor project and credit capacities.

CONCLUSION This chapter introduced the mechanism of RLS programs as a viable financing alternative to traditional tax revenues for highway construction. It also showed which parameters of typical RLS programs can maximize the streams of loans that could be made to various highway capital projects. In particular, the suggestions derived based on computer simulations will provide useful information to the administrators who have already been in charge of similar RLS programs such as SIB programs. RLS programs can stretch scarce financial resources needed for highway construction several times the original corpus of capitalization equity funds. However, there are some caveats for successful design and implementation of RLS programs. The availability of cheap credits should not induce unnecessary future highway capital projects. One way around this caveat will be to thoroughly assess the project and credit capacity of project entities that apply for RLS loans. Despite some limits and caveats, RLS programs might function as a viable financing option for highway construction if well managed. Then, the procedures of computer simulations in this chapter will greatly inform RLS administrators of how to improve RLS structures.

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REFERENCES Aschauer, David Alan. 1990. Highway Capacity and Economic Growth. Economic Perspectives (Federal Reserve Bank of Chicago) 14(5): 14-24. Fisher, Ronald. 2003. The Changing State-Local Fiscal Environment: A 25-Year Retrospective. In State and Local Finances Under Pressure, edited by D. L. Sjoquist, 929. Northampton, MA: Edward Elgar. Holcombe, Randall G. 1992. Revolving Fund Finance: The Case of Wastewater Treatment. Public Budgeting and Finance 12(3): 50-65. Humphrey, Nancy P., and Diane R. Maurice. 1986. Infrastructure Bond Bank Initiatives: Policy Implications and Credit Concerns. Public Budgeting and Finance 6(3): 38-56. Krugman, Paul. 2009. The Return of Depression Economics and the Crisis of 2008. New York, NY: Norton. Moomaw, Ronald L., and Martin Williams. 1991. Total Factor Productivity Growth in Manufacturing: Further Evidence from the States. Journal of Regional Science 31(1): 1734. Mullen, John K., Martin Williams, and Ronald L. Moomaw. 1996. Public Capital Stock and Interstate Variations in Manufacturing Efficiency. Journal of Policy Analysis and Management 15(1): 51-67. Munnell, Alicia H. 1990. Why Has Productivity Growth Declined? Productivity and Public Investment. New England Economic Review Vol. January/February: 3-22. Ryu, Jay Eungha. 2006. Fiscal Impacts of An Innovative Highway Financing Alternative on State Highway Expenditures: The Case of Federal Assistance Funds in the State Infrastructure Bank (SIB) Programs. Public Works Management and Policy 11(1): 33-48. Ryu, Jay Eungha. 2007. Federal Highway Assistance Funds in the State Infrastructure Bank Programs: Mechanisms, Merits, and Modifications. Public Budgeting and Finance 27(4): 43-65. U. G. General Accounting Office. 1996. State Infrastructure Banks: A Mechanism to Expand Federal Transportation Financing. Washington, D. C.: GAO U. S. Department of Transportation, Federal Highway Administration. 2002. State Infrastructure Bank Review. Washington, D. C.: Department of Transportation, Federal Highway Administration. Wang, Wen, Yilin Hou, and William Duncombe. 2007. Determinants of Pay-as-You-Go Financing of Capital Projects: Evidence from the State. Public Budgeting and Finance 27(4): 18-42. Woods, Thomas E. Jr. 2009. Meltdown: A Free-Market Look at Why the Stock Market Collapsed, the Economy Tanked, and Government Bailouts Will Make Things Worse. Washington, D. C.: Regnery Publishing, Inc. Yusuf, Juita-Elena, and Gao Liu. 2008. State Infrastructure Banks and Intergovernmental Subsidies for Local Transportation Investment. Public Budgeting and Finance 28(4): 7189.

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In: Highways: Construction, Management... Editor: Samantha R. Jones, pp. 51-61

ISBN: 978-1-61728-862-3 © 2010 Nova Science Publishers, Inc.

Chapter 3

THE BALANCED VEHICULAR TRAFFIC MODEL Florian Siebel and Sebastian-Andre-Weg

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ABSTRACT The balanced vehicular traffic model is a macroscopic traffic model that describes traffic flow on highways as a hyperbolic system of partial differential equations. As many other macroscopic traffic models, it is based on an equilibrium velocity or fundamental diagram. Nevertheless, due to a generalized source term in the velocity equation, which can be motivated by a finite reaction time of drivers, flow values will be scattered in the flow-density diagram for medium to high traffic densities as proposed by the three-phase traffic theory of B.S. Kerner. Moreover, stable and metastable steady state solutions form an inverted  in the flow-density diagram. Coupling conditions at general junctions can be formulated, which allow traffic simulations on general highway networks.

INTRODUCTION The efficient traffic management of highways requires a detailed understanding of traffic dynamics, in particular about the process of jam formation. Macroscopic traffic models describe traffic flow on highways in analogy to fluid dynamics and are based on averaged quantities. Typical quantities are traffic density , i.e., the number of vehicles per unit length; average velocity of vehicles v; and traffic flow f, i.e., the number of vehicles passing a given location per unit time. Macroscopic traffic models in general rely on the fundamental diagram or equilibrium flow curve. The fundamental diagram f=u() describes a functional relation between traffic flow f and traffic density  with an equilibrium velocity u(). The existence of such an equilibrium velocity follows from the assumption that vehicles in steady flows adapt a velocity according to the distance to the leading vehicle d=1/ (e.g., as measured between the front end of a leading vehicle and the front end of the following vehicle). However, 

Correspondence: Sebastian-Andre-Weg8, D-82362 Weilheim Germany; Email: [email protected]

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Florian Siebel and Sebastian-Andre-Weg

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experimental results for steady flows [1] indicate a multi-valued relation between the equilibrium velocity and the traffic density, the flow-density relation having the shape of an inverted . Based on a detailed analysis of the traffic dynamics on highways, B.S. Kerner developed his three-phase traffic theory. In contrast to other traffic theories, three-phase traffic theory distinguishes not only between two phases of traffic, i.e., free and congested traffic, but instead between free flow, synchronized flow and wide moving jams. Wide moving jams involve characteristic constants. In particular, the downstream jam front of a wide, i.e., spatially extended, moving jam travels upstream with a typical speed of about 15 kph. In contrast, synchronized flow does not show this characteristic feature. The downstream front of synchronized flow is often fixed at a highway bottleneck, e.g., at locations with changes of the number of lanes or at entries and exits. According to the fundamental hypothesis of threephase traffic theory, it is assumed that in synchronized flow at each given time-independent speed of the preceding vehicle, there is an infinite number of distances or space gaps to the preceding vehicle at which a following vehicle can move with this time-independent speed. Thus the hypothetical states of synchronized flow cover a two-dimensional region in the flow-density plane, as opposed to a functional relation assumed within the fundamental diagram approach to traffic modelling. A detailed introduction into three-phase traffic theory can be found in the textbooks [2]-[4]. In this chapter we will summarize the motivation and the main results of the balanced vehicular traffic model that may be used to link macroscopic traffic models relying on the fundamental diagram to three-phase traffic theory. For more details on the balanced vehicular traffic model, the reader should refer to references [5]-[7].

THE FUNDAMENTAL DIAGRAM IN MACROSCOPIC TRAFFIC MODELS In his seminal work, Greenshields [8] experimentally established a relation between the equilibrium velocity u of cars on a highway and the traffic density . Such an equilibrium velocity (or fundamental diagram) was later used by Lighthill and Whitham [9] and Richards [10] in their famous macroscopic model of kinematic waves for vehicular traffic flow. Due to the conservation of the number of vehicles on a highway without entries and exits, traffic dynamics has to fulfil the continuity equation of fluid flow, i.e., the equation

(1) where t describes the time coordinate and x the space coordinate along the highway. Under the assumption that the velocity v corresponds to the equilibrium velocity v(t,x)=u[(t,x)], the continuity equation can be solved, leading to interesting dynamics of jam formation. However, it is widely accepted that the Lighthill-Whitham-Richards model does not realistically describe the observed traffic dynamics (see e.g. [4]). In particular, the model does not describe non-equilibrium velocities and accelerations towards the equilibrium. There are many traffic models which therefore add a dynamic equation for the velocity v (e.g. [11]).

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53

Here we particularly focus on the Aw-Rascle model [12], [13] with a source term [14], which we will denote as Aw-Rascle-Greenberg model in the following. Let us assume, that drivers approach the equilibrium velocity u() with a relaxation time T in such an (anticipatory) way that v(t+T,y)=u[(t+T,y)] with a space coordinate y as measured by the driver, y=x-vt. To first order in T, this leads to

(2)

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Hence, the velocity exponentially approaches the equilibrium velocity with a typical decay time T. This model differs from the widely studied optimal velocity model [15] by the time derivative of the equilibrium velocity u on the left-hand side. The right-hand side of equation (2) can be interpreted as a force term of a generalized potential U, dU/dv=1/T (v-u), which is schematically illustrated in Figure 1.

Figure 1. Generalized potential U for the Aw-Rascle-Greenberg model (equation (2)). The average velocity v is driven towards the minimum of the generalized potential for v=u.

The equilibrium velocity u hence is an attractor for the velocity v. Note that equation (2) depends not only on the velocity v, but through the equilibrium velocity u also on the density , which is determined by the dynamic equation (1). Equation (2) can be rewritten in the space coordinate of the road x to recover the velocity equation of the Aw-Rascle-Greenberg model [12]-[14] in non-conservative form:

(3) The system of partial differential equations (1) and (3) has the property that the characteristic speeds 1=v+u’() and 2=v, i.e. the speeds at which information propagates, are bounded by the velocity v (for reasonable equilibrium velocities u’()1 and for velocities v close to equilibrium. Again, we can introduce a generalized potential U with dU/dv = (v-u). This generalized potential is schematically depicted in Figure 2.

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Figure 2. Generalized potential U for the balanced vehicular traffic model (equation (6)) for a sufficiently large density >1. The average velocity v is driven towards one of the minima vh or vj. The equilibrium velocity u corresponds to a local maximum of the generalized potential.

In the general case the general potential does not have to be symmetrical with respect to the vertical axis as shown in Figure 2 (see e.g. the parameterization of  in [6]). In order to fix the balanced vehicular traffic model, one has to prescribe an equilibrium velocity u() and an effective relaxation coefficient (,v) for arbitrary traffic densities  and velocities v, leading to a generalized potential U. Note that the situation of a generalized potential U with three local extrema according to Figure 2 is only found for sufficiently large traffic densities >1. For densities 1. However, this picture is misleading, as we will explain below. Figure 3 shows the flow-density diagram of the balanced vehicular traffic model for a suitable parameterization of the equilibrium velocity u() and the effective relaxation coefficient (,v) (see [6]). In this diagram we distinguish between four regions I to IV which are bounded by the homogeneous steady state solutions for which the right hand side of equation (6) vanishes. Apart from the equilibrium flow branch characterized by u(), there are two additional steady state solution branches for densities above a threshold density 1=19 [1/km/lane], the high-flow branch vh() and the jam line vj(). The high-flow branch

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Florian Siebel and Sebastian-Andre-Weg

corresponds to the minimum on the right of the generalized potential U, the jam line to the minimum on the left of U in Figure 2. In agreement with the Aw-Rascle-Greenberg model, the characteristic speeds of the balanced vehicular traffic model are 1=v+u’() and 2=v. Moreover, steady state solutions travel with a velocity that corresponds to the derivative of the steady flow curve with respect to density f’(). Since for stability, allowable speeds have to be included within the speed range supported by the characteristic speeds (8)

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we obtain a further condition (the subcharacteristic condition) for the linear stability of steady state solutions [19]. As a consequence [6], the high-flow branch of steady state solutions is linearly stable only for densities 1