Applying Situated Agents to Microscopic Traffic Modelling

Applying Situated Agents to Microscopic Traffic Modelling

Paulo A.F. Ferreira (University of Porto, Portugal), Edgar F. Esteves (University of Porto, Portugal), Rosaldo J.F. Rossetti (University of Porto, Portugal) and Eugénio C. Oliveira (University of Porto, Portugal)
Copyright: © 2009 |Pages: 16
DOI: 10.4018/978-1-60566-226-8.ch005
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Trading off between realism and too much abstraction is an important issue to address in microscopic traffic simulation. In this chapter the authors bring this discussion forward and propose a multi-agent model of the traffic domain where integration is ascribed to the way the environment is represented and agents interoperate. While most approaches still deal with drivers and vehicles indistinguishably, in the proposed framework vehicles are merely moveable objects whereas the driving role is played by agents fully endowed with cognitive abilities and situated in the environment. The authors discuss on the role of the environment dynamics in supporting a truly emergent behaviour of the system and present an extension to the traditional car-following and lane-change models based on the concept of situated agents. A physical communication model is proposed to base different interactions and some performance issues are also identified, which allows for more realistic representation of drivers’ behaviour in microscopic models.
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Using simulation is imperative for planning and realising the correct relation among parameters of any application domain. In the traffic engineering domain, however, most analyses are carried out on an individual basis as an attempt to reduce the number of variables observed and to simplify the process of finding out their correlations. This brings about the issue of how different standpoints from which the domain is viewed could be coupled in the same model and simulation environment in order to allow for wider analysis perspectives (Barceló, 1991; Grazziotin et al., 2004; Rossetti & Bampi, 1999). This is not a recent concern, though. The basic general framework for a fully transportation theory identifies two different concepts, borrowed from Economics, which encompass all aspects related to demand formulation and supply dynamics within the framework, including multi-modal selection and activities planning (McNally, 2000).

Arguably, realistic models are the first instrument to allow the integration of different analysis perspectives in virtually any application. However, modelling is not an easy task and abstraction is often necessary in order to make thinks feasible. The autonomous agent metaphor has been increasingly used in this way and offers a great deal of abstraction while important cognitive and behavioural characteristics of the system entities are preserved. Also, advances in engineering environments for multi-agent systems (MAS) have fostered the idea of overall system behaviour that emerges from the interaction of microscopically modelled entities.

Some examples of MAS applied to the field of traffic and transportation engineering can be found in the literature (e.g. Bouchefra, Reynaud, & Maurin, 1995; Roozemond, 1999; Davidsson et al., 2005; Oliveira & Duarte, 2005, to mention some) and are further discussed elsewhere in this book. However, most of the applications are concerned with the control system, even though it is possible to recognise an increasing interest in the representation of the driver elements and the way they interact and communicate (e.g. Burmeister, Haddadi, & Matylis, 1997; Rossetti et al., 2000; Rossetti et al., 2002; Dia 2002). To increase complexity, transportation systems have recently evolved so quickly as Intelligent Transportation Systems (ITS) start to make part of everyone’s daily life. According to Chatterjee & McDonald (1999), the underlying concept of ITS is to ensure productivity and efficiency by making better use of existing transportation infrastructures. Now, a wide range of novel technologies is presented to the user and start to directly affect the way individuals perceive their surrounding environment and ultimately make decisions, which must also be taken into account.

In very basic terms, the moving element in this whole picture is the vehicle that moves from one point to another throughout the network. Disregarding the importance of pedestrians in the first stage of this work, we consider bicycles, motorcycles, automobiles, trucks and buses as examples of moving elements. However, they are actually moving objects steered by their drivers and sometimes occupied by many other passengers that are people with a trip purpose. Also, their decision concerning how the trip will be carried out in most cases seeks to minimize some individual sense of cost. Therefore, we make a clear distinction between travellers and vehicles, as we shall see later on in this chapter.

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List of Reviewers
Table of Contents
Ana Bazzan, Franziska Klügl
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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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