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Stochastic Congestion and Pricing Model with Endogenous Departure Time Selection and Heterogeneous Travelers

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  • Wuping Xin
  • David Levinson

Abstract

In a stochastic roadway congestion and pricing model, one scheme (omniscient pricing) relies on the full knowledge of each individual journey cost and of early and late penalties of the traveler. A second scheme (observable pricing) is based on observed queuing delays only. Travelers are characterized by late-acceptance levels. The effects of various late-acceptance levels on congestion patterns with and without pricing are compared through simulations. The omniscient pricing scheme is most effective in suppressing the congestion at peak hours and in distributing travel demands over a longer time horizon. Heterogeneity of travelers reduces congestion when pricing is imposed, and congestion pricing becomes more effective when cost structures are diversified rather than identical. Omniscient pricing better reduces the expected total social cost; however, more travelers improve welfare individually with observable pricing. The benefits of a pricing scheme depend on travelers' cost structures and on the proportion of late-tolerant, late-averse, and late-neutral travelers in the population.

Suggested Citation

  • Wuping Xin & David Levinson, 2015. "Stochastic Congestion and Pricing Model with Endogenous Departure Time Selection and Heterogeneous Travelers," Mathematical Population Studies, Taylor & Francis Journals, vol. 22(1), pages 37-52, March.
  • Handle: RePEc:taf:mpopst:v:22:y:2015:i:1:p:37-52
    DOI: 10.1080/08898480.2013.836423
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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. 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.
    5. 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

    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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