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Stochastic congestion and pricing model with endogenous departure time selection and heterogeneous travelers

Author

Listed:
  • Wuping Xin
  • David Levinson

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

Abstract

This paper proposes a stochastic congestion and pricing model that combines a bottleneck model with stochastic queuing to study roadway congestion and pricing. Employing this model, two pricing schemes are developed: one is omniscient pricing for which the transportation administrative agency is assumed to be aware of each and every traveler's cost structure (i.e., their detailed valuation of journey cost as well as early and late penalties), and the other is observable pricing, for which only queuing delay is considered. Travelers are characterized by their late-acceptance level and the effects of various compositions of late-averse, late-tolerant and late-neutral travelers on congestion patterns with and without pricing are discussed. Numerical simulation indicates that omniscient pricing scheme is most effective in suppressing peak hour congestion and distributing demands over longer time horizon. Also, congestion pricing is found to be more effective when travelers have diversified cost structures than identical cost structures, and congestion is better reduced with heterogeneous traveler composition than with single composition. This is consistent with earlier studies in the literature. In addition, the simulation results indicate that omniscient pricing in general reduces Expected Total Social Cost

Suggested Citation

  • Wuping Xin & David Levinson, 2006. "Stochastic congestion and pricing model with endogenous departure time selection and heterogeneous travelers," Working Papers 000029, University of Minnesota: Nexus Research Group.
    • Handle: RePEc:nex:wpaper:stochasticpricing
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    File URL: http://hdl.handle.net/11299/180051
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    Cited by:

    1. Janusch, Nicholas, 2016. "A note on the distortionary effects of revenue-neutral tolls in a bottleneck congestion game," Transportation Research Part A: Policy and Practice, Elsevier, vol. 92(C), pages 95-103.
    2. Yao, Tao & Wei, Mike Mingcheng & Zhang, Bo & Friesz, Terry, 2012. "Congestion derivatives for a traffic bottleneck with heterogeneous commuters," Transportation Research Part B: Methodological, Elsevier, vol. 46(10), pages 1454-1473.
    3. Xiao, Yu & Coulombel, Nicolas & Palma, André de, 2017. "The valuation of travel time reliability: does congestion matter?," Transportation Research Part B: Methodological, Elsevier, vol. 97(C), pages 113-141.
    4. Liu, Qiumin & Jiang, Rui & Liu, Ronghui & Zhao, Hui & Gao, Ziyou, 2020. "Travel cost budget based user equilibrium in a bottleneck model with stochastic capacity," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 1-37.
    5. Yao, Tao & Friesz, Terry L. & Wei, Mike Mingcheng & Yin, Yafeng, 2010. "Congestion derivatives for a traffic bottleneck," Transportation Research Part B: Methodological, Elsevier, vol. 44(10), pages 1149-1165, December.
    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.

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    More about this item

    Keywords

    Agent-based Model; Game Theory; Congestion; Queueing; Traffic Flow; Congestion Pricing; Road Pricing; Value Pricing;
    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
    • R42 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government and Private Investment Analysis; Road Maintenance; Transportation Planning
    • R48 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government Pricing and Policy
    • D10 - Microeconomics - - Household Behavior - - - General
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • 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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