An Agent-Based Approach to Travel Demand Modeling: An Exploratory Analysis
AbstractThe paper develops an agent-based travel demand model. In this model, travel demands emerge 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 sketch transportation network with nearly 1000 trip generators and sinks, followed by a discussion of possible calibration procedures. The 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 the studies on individual travel behaviors. Future research directions are identified, as are the relationship between the agent-based and activity-based approaches for travel forecasting.
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Bibliographic InfoPaper provided by University of Minnesota: Nexus Research Group in its series Working Papers with number 200405.
Date of creation: 2004
Date of revision:
Publication status: Published in Transportation Research Record: Journal of the Transportation Research Board #1898 pp. 28-38
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Web page: http://nexus.umn.edu
More information through EDIRC
Travel forecasting; Agent-based model; Travel behavior; Trip distribution; Traffic assignment; Shortest path algorithm; Activity-based model;
Find related papers by JEL classification:
- R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion
- R48 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government Pricing and Policy
- D10 - Microeconomics - - Household Behavior - - - General
- D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information
- C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Bhanu Yerra & David Levinson, 2005.
"The Emergence of Hierarchy in Transportation Networks,"
200507, University of Minnesota: Nexus Research Group.
- Bhanu Yerra & David Levinson, 2005. "The emergence of hierarchy in transportation networks," The Annals of Regional Science, Springer, vol. 39(3), pages 541-553, 09.
- Lei Zhang & David Levinson & Shanjiang Zhu, 2007. "Agent-Based Model of Price Competition and Product Differentiation on Congested Networks," Working Papers 200809, University of Minnesota: Nexus Research Group.
- Lei Zhang & David Levinson, 2005. "Road Pricing with Autonomous Links," Working Papers 200506, University of Minnesota: Nexus Research Group.
- Shanjiang Zhu & David Levinson & Lei Zhang, 2007. "An Agent-based Route Choice Model," Working Papers 000089, University of Minnesota: Nexus Research Group.
- McDonnell, Simon & Zellner, Moira, 2011. "Exploring the effectiveness of bus rapid transit a prototype agent-based model of commuting behavior," Transport Policy, Elsevier, vol. 18(6), pages 825-835, November.
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