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A Method for the Route-Choice Problem in Public Transport Systems

Author

Listed:
  • Kjell Jansson

    (Department of Economics, Stockholm University, S-10691 Stockholm, Sweden)

  • Bosse Ridderstolpe

    (Teli Mobile Systems AB, Box 2102, S-42102 V. Frölunda, Sweden)

Abstract

Passengers in an urban transport system may often be in a position to choose among a set of parallel routes in order to reach their destinations. Their decisions depend in general on many variables, e.g., the in-vehicle travel times, walk time, transfer time, number of transfers, fares, the headways and the irregularity of the routes. Traditional methods used in urban transport planning do not solve the route-choice problem satisfactorily when passengers may choose between modes with differences in speed or fare structures, e.g., tram and bus. The purpose of this paper is to present a method for calculating the passenger waiting times and distribution for the route-choice problem more realistically than existing methods. An implementation of the new method can be found in the new VIPS II urban transport planning system. Large scale testing carried out by the Stockholm Transport Development Department has produced better results than with methods used previously.

Suggested Citation

  • Kjell Jansson & Bosse Ridderstolpe, 1992. "A Method for the Route-Choice Problem in Public Transport Systems," Transportation Science, INFORMS, vol. 26(3), pages 246-251, August.
    • Handle: RePEc:inm:ortrsc:v:26:y:1992:i:3:p:246-251
    DOI: 10.1287/trsc.26.3.246
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    Citations

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    Cited by:

    1. Cats, Oded & Koutsopoulos, Haris N. & Burghout, Wilco & Toledo, Tomer, 2013. "Effect of real-time transit information on dynamic path choice of passengers," Working papers in Transport Economics 2013:28, CTS - Centre for Transport Studies Stockholm (KTH and VTI).
    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. Alimo, Philip Kofi & Agyeman, Stephen & Zankawah, Sanusi Mumuni & Yu, Chunhui & Cheng, Lin & Ma, Wanjing, 2022. "Factors causing low demand for a suburban passenger train in Sekondi-Takoradi," Journal of Transport Geography, Elsevier, vol. 98(C).
    4. Larsen, Odd I. & Sunde, yvind, 2008. "Waiting time and the role and value of information in scheduled transport," Research in Transportation Economics, Elsevier, vol. 23(1), pages 41-52, January.
    5. Jansson, Kjell & Angell, Truls, 2012. "Is it possible to achieve both a simple and efficient public transport zone fare structure? Case study Oslo," Transport Policy, Elsevier, vol. 20(C), pages 150-161.
    6. Cats, Oded & West, Jens & Eliasson, Jonas, 2016. "A dynamic stochastic model for evaluating congestion and crowding effects in transit systems," Transportation Research Part B: Methodological, Elsevier, vol. 89(C), pages 43-57.
    7. Taoyuan Yang & Peng Zhao & Xiangming Yao, 2020. "A Method to Estimate URT Passenger Spatial-Temporal Trajectory with Smart Card Data and Train Schedules," Sustainability, MDPI, vol. 12(6), pages 1-13, March.

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