Agent-Based Approach to Travel Demand Modeling

Journal of the Transportation Research Board, No. 1898, pp. 28-38, 2004

9 Pages Posted: 5 Sep 2007

See all articles by Lei Zhang

Lei Zhang

Oregon State University

David Matthew Levinson

affiliation not provided to SSRN

Abstract

An agent-based travel demand model is developed in which travel demand emerges from the interactions of three types of agents in the transportation system: node, arc, and traveler. Simple local rules of agent behaviors are shown to be capable of efficiently solving complicated transportation problems such as trip distribution and traffic assignment.A unique feature of the agent-based model is that it explicitly models the goal, knowledge, searching behavior, and learning ability of related agents. The proposed model distributes trips from origins to destinations in a disaggregate manner and does not require path enumeration or any standard shortest-path algorithm to assign traffic to the links. A sample 10-by-10 grid network is used to facilitate the presentation. The model is also applied to the Chicago, Illinois, sketch transportation network with nearly 1,000 trip generators and sinks, and possible calibration procedures are discussed. Agent-based modeling techniques provide a flexible travel forecasting framework that facilitates the prediction of important macroscopic travel patterns from microscopic agent behaviors and hence encourages studies on individual travel behaviors. Future research directions are identified, as is the relationship between the agent-based and activity-based approaches for travel forecasting.

Keywords: agent-based, travel demand modeling

Suggested Citation

Zhang, Lei and Levinson, David Matthew, Agent-Based Approach to Travel Demand Modeling. Journal of the Transportation Research Board, No. 1898, pp. 28-38, 2004. Available at SSRN: https://ssrn.com/abstract=1010239

Lei Zhang

Oregon State University ( email )

Bexell Hall 200
Corvallis, OR 97331
United States

David Matthew Levinson (Contact Author)

affiliation not provided to SSRN

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