A Multi-Agent Modeling Approach to Simulate Dynamic Activity-Travel Patterns

A Multi-Agent Modeling Approach to Simulate Dynamic Activity-Travel Patterns

Qi Han (Eindhoven University of Technology, The Netherlands), Theo Arentze (Eindhoven University of Technology, The Netherlands), Harry Timmermans (Eindhoven University of Technology, The Netherlands), Davy Janssens (Hasselt University, Belgium) and Geert Wets (Hasselt University, Belgium)
Copyright: © 2009 |Pages: 21
DOI: 10.4018/978-1-60566-226-8.ch002
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Abstract

Contributing to the recent interest in the dynamics of activity-travel patterns, this chapter discusses a framework of an agent-based modeling approach focusing on the dynamic formation of (location) choice sets. Individual travelers are represented as agents, each with their cognition of the environment, habits, and activity-travel patterns. Agents learn through their experiences with the transport systems, changes in the environments and from their social network. Conceptually, agents are assumed to have an aspiration level associated with choice sets that in combination with evaluation results determine whether the agent will start exploring or persist in habitual behavior; an activation level of each (location) alternative that determines whether or not the alternative is included in the choice set in the next time step, and an expected (utility) function to evaluate each (location) alternative given current beliefs. Each of these elements is dynamic. Based on principles of reinforcement learning, Bayesian learning, and social comparison theories, the framework specifies functions for experience-based learning, extended and integrated with social learning.
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Introduction

So-called activity-based models have rapidly gained interest in the transportation research community. These models predicts and simulate in a coherent fashion multiple facets of activity-travel behavior including which activities are conducted, when, where, for how long, with whom, and the transport mode involved. To the extent that these models have been actively implemented, some type of simulation is used to implement predicted activity-travel in time and space. The majority of these simulations are based on Monte Carlo simulations; others use agents such as Albatross (Arentze and Timmermans, 2001, 2003a, 2004a) and Aurora (e.g., Joh, et al. 2006). An overview of these developments is given in Timmermans, et al. (2002). In addition to these comprehensive models, agent-based simulations have also been suggested for particular facets of activity-travel choice (e.g., Charypar and Nagel, 2003; Balmer, et al. 2004; Rosetti and Liu, 2004; Hertkort and Wagner, 2005; Rindsfuser and Klügl, 2005).

Although the theoretical underpinnings of these agent-based models differ, they have in common the assumption that individuals will choose within their choice sets the alternative they prefer, sometimes subject to a set of constraints (Ben-Akiva and Boccara, 1995; Pellegrini, et al., 1997, Cascetta and Papola, 2001). In most of these models, however, the construction and composition of individual choice sets is not explicitly modeled. Choice sets are typically assumed given or derived on the basis of some arbitrary rule (Swait and Ben-Akiva, 1987; Thill and Horowitz, 1997; Swait, 2001). The delineation of choice sets is particularly important in large-scale micro-simulation systems, which are receiving increasing attention in activity-based travel-demand modeling and integrated land-use – transportation systems. As expected, knowing the choice set from which an alternative is selected significantly decreases the complexity and may improve the performance of these large-scale systems (Shocker, et al., 1991). In this context, the choice set refers to the set of discrete alternatives known by the individual, which is a subset of the universal choice set that consists of all alternatives available to the decision maker. Known means that the individual knows the attributes that are potentially relevant for evaluation under specific contextual conditions in the activity-travel decision-making process. Note that this definition differs from commonly used terminology in marketing, where a distinction is made between awareness set, evoked set, consideration set and choice set (Timmermans and Golledge, 1990). We can refine our framework along these lines, but that is beyond the goal of the present chapter.

As a part of the FEATHERS model (Arentze, et al., 2006; Janssens, et al., 2006), an extension and elaboration of Aurora (Joh et al., 2006), an agent-based system which incorporates different types of dynamics and learning discussed in Arentze and Timmermans (2003) is developed. This chapter discusses the conceptual framework that addresses one type of dynamic: the formation and dissolution of personal choice sets, which lays the foundation for the longer-term dynamics of the FEATHERS models. It should be stated from the outset that the discussion below mainly concerns location choice set, but the basic mechanism can be applied as building blocks for multiple facets of activity-travel patterns, including person choice set, mode choice set, and so on.

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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
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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
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Towards Simulating Cognitive Agents in Public Transport Systems
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Kurt Dresner, Peter Stone, Mark Van Middlesworth
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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
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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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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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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
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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
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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
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Analyzing Transactions Costs in Transport Corridors Using Multi Agent-Based Simulation
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Chapter 18
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