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An Agent-Based Approach to Travel Demand Modeling: An Exploratory Analysis

Author

Listed:
  • Lei Zhang
  • David Levinson

    () (Nexus (Networks, Economics, and Urban Systems) Research Group, Department of Civil Engineering, University of Minnesota)

Abstract

The 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.

Suggested Citation

  • Lei Zhang & David Levinson, 2004. "An Agent-Based Approach to Travel Demand Modeling: An Exploratory Analysis," Working Papers 200405, University of Minnesota: Nexus Research Group.
  • Handle: RePEc:nex:wpaper:agent
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/11299/179905
    File Function: First version, 2007
    Download Restriction: no

    References listed on IDEAS

    as
    1. Bhanu Yerra & David Levinson, 2005. "The emergence of hierarchy in transportation networks," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 39(3), pages 541-553, September.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Lei Zhang & David Levinson, 2005. "Road Pricing with Autonomous Links," Working Papers 200506, University of Minnesota: Nexus Research Group.
    2. Mosahar Tarimoradi & M. H. Fazel Zarandi & Hosain Zaman & I. B. Turksan, 0. "Evolutionary fuzzy intelligent system for multi-objective supply chain network designs: an agent-based optimization state of the art," Journal of Intelligent Manufacturing, Springer, vol. 0, pages 1-29.
    3. Shanjiang Zhu & David Levinson & Lei Zhang, 2007. "An Agent-based Route Choice Model," Working Papers 000089, University of Minnesota: Nexus Research Group.
    4. Farooq, Bilal & Miller, Eric J. & Chingcuanco, Franco & Giroux-Cook, Martin, 2013. "Microsimulation framework for urban price-taker markets," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 6(1), pages 41-51.
    5. 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.
    6. 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.

    More about this item

    Keywords

    Travel forecasting; Agent-based model; Travel behavior; Trip distribution; Traffic assignment; Shortest path algorithm; Activity-based model;

    JEL classification:

    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
    • 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; Information and Knowledge; Communication; Belief; Unawareness
    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games

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