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Learning-based framework for transit assignment modeling under information provision

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  • Mohamed Wahba
  • Amer Shalaby

Abstract

The modeling of service dynamics has been the focus of recent developments in the field of transit assignment modeling. The emerging focus on dynamic service modeling requires a corresponding shift in transit demand modeling to represent appropriately the dynamic behaviour of passengers and their responses to Intelligent Transportation Systems technologies. This paper presents the theoretical development of a departure time and transit path choice model based on the Markovian Decision Process. This model is the core of the MIcrosimulation Learning-based Approach to TRansit Assignment. Passengers, while traveling, move to different locations in the transit network at different points in time (e.g. at stop, on board), representing a stochastic process. This stochastic process is partly dependent on the transit service performance and partly controlled by the transit rider’s trip choices. This can be analyzed as a Markovian Decision Process, in which actions are rewarded and hence passengers’ optimal policies for maximizing the trip utility can be estimated. The proposed model is classified as a bounded rational model, with a constant utility term and a stochastic choice rule. The model is appropriate for modeling information provision since it distinguishes between individual’s experience with the service performance and information provided about system dynamics. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • Mohamed Wahba & Amer Shalaby, 2014. "Learning-based framework for transit assignment modeling under information provision," Transportation, Springer, vol. 41(2), pages 397-417, March.
    • Handle: RePEc:kap:transp:v:41:y:2014:i:2:p:397-417
    DOI: 10.1007/s11116-013-9510-5
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    References listed on IDEAS

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    1. Martin L. Hazelton & David P. Watling, 2004. "Computation of Equilibrium Distributions of Markov Traffic-Assignment Models," Transportation Science, INFORMS, vol. 38(3), pages 331-342, August.
    2. Watling, David, 1996. "Asymmetric problems and stochastic process models of traffic assignment," Transportation Research Part B: Methodological, Elsevier, vol. 30(5), pages 339-357, October.
    3. Gary A. Davis & Nancy L. Nihan, 1993. "Large Population Approximations of a General Stochastic Traffic Assignment Model," Operations Research, INFORMS, vol. 41(1), pages 169-178, February.
    4. Ettema, Dick & Tamminga, Guus & Timmermans, Harry & Arentze, Theo, 2005. "A micro-simulation model system of departure time using a perception updating model under travel time uncertainty," Transportation Research Part A: Policy and Practice, Elsevier, vol. 39(4), pages 325-344, May.
    5. Mohamed Wahba & Amer Shalaby, 2011. "Large-scale application of MILATRAS: case study of the Toronto transit network," Transportation, Springer, vol. 38(6), pages 889-908, November.
    6. Mark D. Hickman & David H. Bernstein, 1997. "Transit Service and Path Choice Models in Stochastic and Time-Dependent Networks," Transportation Science, INFORMS, vol. 31(2), pages 129-146, May.
    7. Agostino Nuzzolo & Francesco Russo & Umberto Crisalli, 2001. "A Doubly Dynamic Schedule-based Assignment Model for Transit Networks," Transportation Science, INFORMS, vol. 35(3), pages 268-285, August.
    8. G. E. Cantarella & E. Cascetta, 1995. "Dynamic Processes and Equilibrium in Transportation Networks: Towards a Unifying Theory," Transportation Science, INFORMS, vol. 29(4), pages 305-329, November.
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    Cited by:

    1. S. Mahmassani, Hani & F. Hyland, Michael, 2016. "Gap-based transit assignment algorithm with vehicle capacity constraints: Simulation-based implementation and large-scale applicationAuthor-Name: Verbas, Ömer," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 1-16.
    2. Oded Cats & Zafeira Gkioulou, 2017. "Modeling the impacts of public transport reliability and travel information on passengers’ waiting-time uncertainty," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 6(3), pages 247-270, September.
    3. Wei Nai & Zan Yang & Dan Li & Lu Liu & Yuting Fu & Yuao Guo, 2024. "Urban Day-to-Day Travel and Its Development in an Information Environment: A Review," Sustainability, MDPI, vol. 16(6), pages 1-29, March.
    4. Chenfeng Xiong & Lei Zhang, 2017. "Dynamic travel mode searching and switching analysis considering hidden model preference and behavioral decision processes," Transportation, Springer, vol. 44(3), pages 511-532, May.

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