New Approach to Smooth Traffic Flow with Route Information Sharing

New Approach to Smooth Traffic Flow with Route Information Sharing

Tomohisa Yamashita (National Institute of Advanced Industrial Science and Technology (AIST), Japan) and Koichi Kurumatani (National Institute of Advanced Industrial Science and Technology (AIST), Japan)
Copyright: © 2009 |Pages: 16
DOI: 10.4018/978-1-60566-226-8.ch014
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Abstract

With maturation of ubiquitous computing technology, it has become feasible to design new systems to improve our urban life. In this chapter, the authors introduce a new application for car navigation in a city. Every car navigation system in operation today has the current position of the vehicle, the destination, and the currently chosen route to the destination. If vehicles in a city could share this information, they could use traffic information to improve traffic efficiency for each vehicle and the whole system. Therefore, this chapter proposes a cooperative car navigation system with route information sharing (RIS). In the RIS system, each vehicle transmits route information (current position, destination, and route to the destination) to a route information server, which estimates future traffic congestion using current congestion information and this information and feeds its estimate back to each vehicle. Each vehicle uses the estimation to re-plan their route. This cycle is then repeated. The authors’ purpose in this chapter is to confirm the effectiveness of the proposed cooperative car navigation system with multiagent simulation. To evaluate the effect of the RIS system, we introduce two indexes; individual incentive and social acceptability. In theor traffic simulation with three types of road networks, the authors observe that the average travel time of the drivers using the RIS system is substantially shorter than the time of other drivers. Moreover, as the number of the RIS drivers increases, the average travel time of all drivers decreases. As a result of simulation, this chapter confirms that a cooperative car navigation system with the RIS system generally satisfied individual incentive and social acceptability, and had a effect for the improvement of traffic efficiency.
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1. Introduction

With maturation of ubiquitous computing technology, particularly with advances in positioning and telecommunications systems, we are now in a position to design advanced assist systems for many aspects of our lives. However, most of the research we have seen to date has focused on aspects of supporting a single person. We believe a mass user support system (Kurumatani, 2004; Kurumatani, 2003) would have a large impact on society. The new concept would benefit not only society as a whole but would also benefit individuals. In particular, Nakashima (Nakashima, 2003) and Noda (Noda, 2003) have focused on technologies that might enhance urban social life, especially transportation support systems. This chapter reports on our recent multiagent simulation demonstrating the effectiveness of a new kind of car navigation system.

Many researchers have been trying to design better navigation systems, by examining the variety of traffic information available (Bazzan, & Klügl, 2005; Bazzan, Fehler, & Klügl, 2006; Klügl, Bazzan, & Wahle, 2003; Shiose, Onitsuka, & Taura, 2001). However, previous research efforts have revealed that individually optimizing performance with only traffic congestion information is difficult (Mahmassani & Jayakrishnan, 1991; Tanahashi, Kitaoka, Baba, Mori, Terada, & Teramoto, 2002; Yoshii, Akahane, & Kuwahara, 1996). A navigation system recommends the route for the shortest estimated travel time based on the current state of traffic congestion. However, if other drivers, using the same information, simultaneously choose the same route, traffic would become concentrated on the new route.

Active queue management algorithms for TCP (Transmission Control Protocol) traffic, e.g., Random Early Detection (Floyd & Jacobson, 1993) are similar to city traffic management. TCP is one of the core protocols of the Internet protocol suite. Vehicles in road transport system are similar to IP packets in Internet. However, these algorithms are unsuitable for traffic flow in road transportation systems for two reasons. One is a physical constraint: dropping vehicles like packets in TCP traffic is impossible. The other is a social constraint: such algorithms are problematic from the standpoint of fairness because the utilities of the vehicles that are randomly dropped (or stopped) suffer a big loss.

Car navigation systems were originally designed as electronic enhancements of maps automatically indicating the current position of the vehicle and a route to the destination. Japan roads now support the second generation of car navigation systems connected to VICS (Vehicle Information and Communication System) (Vehicle Information and Communication System Center, 1995). This new system can download traffic information and display it on the map. The system uses the information to avoid congested routes when it plans a route. What we suggest in this chapter is yet another generation of car navigation systems. VICS measures traffic volume with sensors located on roadsides, e.g., radar, optical and ultrasonic vehicle detectors and CCTV (Closed Circuit Television) cameras. The gathered information is transmitted using infrared beacon, radio wave beacon, and FM multiplex broadcasting. Each car just receives information from VICS, but does not return any.

If a car could transmit information by using a mobile phone or other short-range communication, we believe that we could design a far better navigation system. Every car navigation system in operation today has the current position of the vehicle, the destination, and the currently chosen route to the destination. If vehicles in a city could share this information, they could use traffic information to improve traffic efficiency for each vehicle and the whole system. Our idea is thus a cooperative car navigation system with route information sharing.

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Table of Contents
Preface
Ana Bazzan, Franziska Klügl
Acknowledgment
Ana Bazzan, Franziska Klügl
Chapter 1
Takeshi Takama
This study discusses adaptation effects and congestion in a multi-agent system (MAS) to analyse real transport and traffic problems. Both... Sample PDF
Adaptation and Congestion in a Multi-Agent System to Analyse Empirical Traffic Problems: Concepts and a Case Study of the Road User Charging Scheme at the Upper Derwent
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Chapter 2
Qi Han, Theo Arentze, Harry Timmermans, Davy Janssens, Geert Wets
Contributing to the recent interest in the dynamics of activity-travel patterns, this chapter discusses a framework of an agent-based modeling... Sample PDF
A Multi-Agent Modeling Approach to Simulate Dynamic Activity-Travel Patterns
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Chapter 3
Michael Balmer, Marcel Rieser, Konrad Meister, David Charypar, Nicolas Lefebvre, Kai Nagel
Micro-simulations for transport planning are becoming increasingly important in traffic simulation, traffic analysis, and traffic forecasting. In... Sample PDF
MATSim-T: Architecture and Simulation Times
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Chapter 4
Ulf Lotzmann
In this chapter an agent-based traffic simulation approach is presented which sees agents as individual traffic participants moving in an artificial... Sample PDF
TRASS: A Multi-Purpose Agent-Based Simulation Framework for Complex Traffic Simulation Applications
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Chapter 5
Paulo A.F. Ferreira, Edgar F. Esteves, Rosaldo J.F. Rossetti, Eugénio C. Oliveira
Trading off between realism and too much abstraction is an important issue to address in microscopic traffic simulation. In this chapter the authors... Sample PDF
Applying Situated Agents to Microscopic Traffic Modelling
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Chapter 6
Andreas Schadschneider, Hubert Klüpfel, Tobias Kretz, Christian Rogsch, Armin Seyfried
Multi-Agent Simulation is a general and powerful framework for understanding and predicting the behaviour of social systems. Here the authors... Sample PDF
Fundamentals of Pedestrian and Evacuation Dynamics
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Chapter 7
Rex Oleson, D. J. Kaup, Thomas L. Clarke, Linda C. Malone, Ladislau Bölöni
The “Social Potential”, which the authors will refer to as the SP, is the name given to a technique of implementing multi-agent movement in... Sample PDF
"Social Potential" Models for Modeling Traffic and Transportation
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Chapter 8
Sabine Timpf
In this chapter, the authors present a methodology for simulating human navigation within the context of public, multi-modal transport. They show... Sample PDF
Towards Simulating Cognitive Agents in Public Transport Systems
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Chapter 9
Kurt Dresner, Peter Stone, Mark Van Middlesworth
Fully autonomous vehicles promise enormous gains in safety, efficiency, and economy for transportation. In previous work, the authors of this... Sample PDF
An Unmanaged Intersection Protocol and Improved Intersection Safety for Autonomous Vehicles
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Chapter 10
Heiko Schepperle, Klemens Böhm
Current intersection-control systems lack one important feature: They are unaware of the different valuations of reduced waiting time of the... Sample PDF
Valuation-Aware Traffic Control: The Notion and the Issues
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Chapter 11
Charles Desjardins, Julien Laumônier, Brahim Chaib-draa
This chapter studies the use of agent technology in the domain of vehicle control. More specifically, it illustrates how agents can address the... Sample PDF
Learning Agents for Collaborative Driving
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Chapter 12
Kagan Tumer, Zachary T. Welch, Adrian Agogino
Traffic management problems provide a unique environment to study how multi-agent systems promote desired system level behavior. In particular, they... Sample PDF
Traffic Congestion Management as a Learning Agent Coordination Problem
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Chapter 13
Matteo Vasirani, Sascha Ossowski
The problem of advanced intersection control is being discovered as a promising application field for multiagent technology. In this context... Sample PDF
Exploring the Potential of Multiagent Learning for Autonomous Intersection Control
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Chapter 14
Tomohisa Yamashita, Koichi Kurumatani
With maturation of ubiquitous computing technology, it has become feasible to design new systems to improve our urban life. In this chapter, the... Sample PDF
New Approach to Smooth Traffic Flow with Route Information Sharing
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Chapter 15
Denise de Oliveira, Ana L.C. Bazzan
In a complex multiagent system, agents may have different partial information about the system’s state and the information held by other agents in... Sample PDF
Multiagent Learning on Traffic Lights Control: Effects of Using Shared Information
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Chapter 16
Tamás Máhr, F. Jordan Srour, Mathijs de Weerdt, Rob Zuidwijk
While intermodal freight transport has the potential to introduce efficiency to the transport network,this transport method also suffers from... Sample PDF
The Merit of Agents in Freight Transport
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Chapter 17
Lawrence Henesey, Jan A. Persson
In analyzing freight transportation systems, such as the intermodal transport of containers, often direct monetary costs associated with... Sample PDF
Analyzing Transactions Costs in Transport Corridors Using Multi Agent-Based Simulation
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Chapter 18
Shawn R. Wolfe, Peter A. Jarvis, Francis Y. Enomoto, Maarten Sierhuis, Bart-Jan van Putten
Today’s air traffic management system is not expected to scale to the projected increase in traffic over the next two decades. Enhancing... Sample PDF
A Multi-Agent Simulation of Collaborative Air Traffic Flow Management
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