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The effect of crowding on public transit user travel behavior in a large-scale public transportation system through modeling daily variations

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  • Donghyung Yook
  • Kevin Heaslip

Abstract

In this paper, the crowding effect in a transit vehicle is modeled in a time-expanded network that considers the daily variation in passenger flows. The study models the daily variation of in-vehicle crowding in a real large-scale transit system. A transit assignment for this real network is modeled and implemented by constructing a crowding cost function that follows the valuation of crowding and by using the reliable shortest path finding method. The direct application of the crowding model to a real network for the Utah Transit Authority indicates that crowd modeling with multi-user classes could influence public transportation system planning and affect the revenues of transit agencies. Moreover, the addition of the disutility factor, crowding, does not always appear to cause an increase in disutility for transit users.

Suggested Citation

  • Donghyung Yook & Kevin Heaslip, 2015. "The effect of crowding on public transit user travel behavior in a large-scale public transportation system through modeling daily variations," Transportation Planning and Technology, Taylor & Francis Journals, vol. 38(8), pages 935-953, December.
    • Handle: RePEc:taf:transp:v:38:y:2015:i:8:p:935-953
    DOI: 10.1080/03081060.2015.1079391
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    References listed on IDEAS

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    1. Spiess, Heinz & Florian, Michael, 1989. "Optimal strategies: A new assignment model for transit networks," Transportation Research Part B: Methodological, Elsevier, vol. 23(2), pages 83-102, April.
    2. Jia Hao Wu & Michael Florian & Patrice Marcotte, 1994. "Transit Equilibrium Assignment: A Model and Solution Algorithms," Transportation Science, INFORMS, vol. 28(3), pages 193-203, August.
    3. Hamdouch, Younes & Lawphongpanich, Siriphong, 2008. "Schedule-based transit assignment model with travel strategies and capacity constraints," Transportation Research Part B: Methodological, Elsevier, vol. 42(7-8), pages 663-684, August.
    4. Sumalee, Agachai & Tan, Zhijia & Lam, William H.K., 2009. "Dynamic stochastic transit assignment with explicit seat allocation model," Transportation Research Part B: Methodological, Elsevier, vol. 43(8-9), pages 895-912, September.
    5. Hu Shao & William Lam & Mei Tam, 2006. "A Reliability-Based Stochastic Traffic Assignment Model for Network with Multiple User Classes under Uncertainty in Demand," Networks and Spatial Economics, Springer, vol. 6(3), pages 173-204, September.
    6. S. C. Wirasinghe & L. Kattan & M. M. Rahman & J. Hubbell & R. Thilakaratne & S. Anowar, 2013. "Bus rapid transit - a review," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 17(1), pages 1-31, March.
    7. Li, Zheng & Hensher, David A., 2011. "Crowding and public transport: A review of willingness to pay evidence and its relevance in project appraisal," Transport Policy, Elsevier, vol. 18(6), pages 880-887, November.
    8. Poon, M. H. & Wong, S. C. & Tong, C. O., 2004. "A dynamic schedule-based model for congested transit networks," Transportation Research Part B: Methodological, Elsevier, vol. 38(4), pages 343-368, May.
    9. Lam, W. H. K. & Gao, Z. Y. & Chan, K. S. & Yang, H., 1999. "A stochastic user equilibrium assignment model for congested transit networks," Transportation Research Part B: Methodological, Elsevier, vol. 33(5), pages 351-368, June.
    10. Joaquín de Cea & Enrique Fernández, 1993. "Transit Assignment for Congested Public Transport Systems: An Equilibrium Model," Transportation Science, INFORMS, vol. 27(2), pages 133-147, May.
    11. Sang Nguyen & Stefano Pallottino & Federico Malucelli, 2001. "A Modeling Framework for Passenger Assignment on a Transport Network with Timetables," Transportation Science, INFORMS, vol. 35(3), pages 238-249, August.
    12. Lisa Zorn & Elizabeth Sall & Daniel Wu, 2012. "Incorporating crowding into the San Francisco activity-based travel model," Transportation, Springer, vol. 39(4), pages 755-771, July.
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    Cited by:

    1. Daniel (Jian) Sun & Shukai Chen & Chun Zhang & Suwan Shen, 2016. "A bus route evaluation model based on GIS and super-efficient data envelopment analysis," Transportation Planning and Technology, Taylor & Francis Journals, vol. 39(4), pages 407-423, June.
    2. Liudan Jiao & Liyin Shen & Chenyang Shuai & Yongtao Tan & Bei He, 2017. "Measuring Crowdedness between Adjacent Stations in an Urban Metro System: a Chinese Case Study," Sustainability, MDPI, vol. 9(12), pages 1-14, December.
    3. Prasanta K. Sahu & Gajanand Sharma & Anirban Guharoy, 2018. "Commuter travel cost estimation at different levels of crowding in a suburban rail system: a case study of Mumbai," Public Transport, Springer, vol. 10(3), pages 379-398, December.

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