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An Agent-based Route Choice Model


  • Shanjiang Zhu
  • David Levinson

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

  • Lei Zhang


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

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    References listed on IDEAS

    1. Levinson, David, 2005. "Micro-foundations of congestion and pricing: A game theory perspective," Transportation Research Part A: Policy and Practice, Elsevier, vol. 39(7-9), pages 691-704.
    2. Arnott, Richard & Kraus, Marvin, 1998. "When are anonymous congestion charges consistent with marginal cost pricing?," Journal of Public Economics, Elsevier, vol. 67(1), pages 45-64, January.
    3. Bar-Gera, Hillel & Boyce, David, 2003. "Origin-based algorithms for combined travel forecasting models," Transportation Research Part B: Methodological, Elsevier, vol. 37(5), pages 405-422, June.
    4. 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.
    5. Azevedo, JoseAugusto & Santos Costa, Maria Emilia O. & Silvestre Madeira, Joaquim Joao E. R. & Vieira Martins, Ernesto Q., 1993. "An algorithm for the ranking of shortest paths," European Journal of Operational Research, Elsevier, vol. 69(1), pages 97-106, August.
    6. Estache, Antonio & Romero, Manuel & Strong, John, 2000. "The long and winding path to private financing and regulation of toll roads," Policy Research Working Paper Series 2387, The World Bank.
    7. F. H. Knight, 1924. "Some Fallacies in the Interpretation of Social Cost," The Quarterly Journal of Economics, Oxford University Press, vol. 38(4), pages 582-606.
    8. Xi Zou & David Levinson, 2006. "A Multi-Agent Congestion and Pricing Model," Working Papers 200605, University of Minnesota: Nexus Research Group.
    9. Zhang, Junyi & Timmermans, Harry & Borgers, Aloys & Wang, Donggen, 2004. "Modeling traveler choice behavior using the concepts of relative utility and relative interest," Transportation Research Part B: Methodological, Elsevier, vol. 38(3), pages 215-234, March.
    10. David Charypar & Kai Nagel, 2005. "Generating complete all-day activity plans with genetic algorithms," Transportation, Springer, vol. 32(4), pages 369-397, July.
    11. Georgina Santos & Laurent Rojey, 2004. "Distributional impacts of road pricing: The truth behind the myth," Transportation, Springer, vol. 31(1), pages 21-42, February.
    12. Yang, Hai & Huang, Hai-Jun, 2004. "The multi-class, multi-criteria traffic network equilibrium and systems optimum problem," Transportation Research Part B: Methodological, Elsevier, vol. 38(1), pages 1-15, January.
    13. de Palma, Andre & Myers, Gordon M & Papageorgiou, Yorgos Y, 1994. "Rational Choice under an Imperfect Ability to Choose," American Economic Review, American Economic Association, vol. 84(3), pages 419-440, June.
    14. Srinivasan, Karthik K. & Mahmassani, Hani S., 2003. "Analyzing heterogeneity and unobserved structural effects in route-switching behavior under ATIS: a dynamic kernel logit formulation," Transportation Research Part B: Methodological, Elsevier, vol. 37(9), pages 793-814, November.
    15. David Levinson, 1995. "An Evolutionary Transportation Planning Model: Structure and Application," Working Papers 199502, University of Minnesota: Nexus Research Group.
    16. Amos Tversky & Itamar Simonson, 1993. "Context-Dependent Preferences," Management Science, INFORMS, vol. 39(10), pages 1179-1189, October.
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    More about this item


    Agent-based model; route choice; traffic assignment; travel demand modeling;

    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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