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An Agent-based Route Choice Model

Author

Listed:
  • Shanjiang Zhu
  • David Levinson

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

  • Lei Zhang

Abstract

Travel demand emerges from individual decisions. These decisions, depending on individual objectives, preferences, experiences and spatial knowledge about travel, are both heterogeneous and evolutionary. Research emerging from fields such as road pricing and ATIS requires travel demand models that are able to consider travelers with distinct attributes (value of time (VOT), willingness to pay, travel budgets, etc.) and behavioral preferences (e.g. willingness to switch routes with potential savings) in a differentiated market (by tolls and the level of service). Traditional trip-based models have difficulty in dealing with the aforementioned heterogeneity and issues such as equity. Moreover, the role of spatial information, which has significant influence on decision-making and travel behavior, has not been fully addressed in existing models. To bridge the gap, this paper proposes to explicitly model the formation and spread- ing of spatial knowledge among travelers. An Agent-based Route Choice (ARC) model was developed to track choices of each decision-maker on a road network over time and map individual choices into macroscopic flow pattern. ARC has been applied on both SiouxFalls network and Chicago sketch network. Comparison between ARC and existing models (UE and SUE) on both networks shows ARC is valid and computationally tractable. To be brief, this paper specifically focuses on the route choice behavior, while the proposed model can be extended to other modules of travel demand under an integrated framework.

Suggested Citation

  • Shanjiang Zhu & David Levinson & Lei Zhang, 2007. "An Agent-based Route Choice Model," Working Papers 000089, University of Minnesota: Nexus Research Group.
    • Handle: RePEc:nex:wpaper:arc
    DOI: 10.3390/urbansci2030058
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    File URL: http://hdl.handle.net/11299/179820
    File Function: First version, 2007
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Agent-based model; route choice; traffic assignment; travel demand modeling;
    All these keywords.

    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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