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Route Choice on Transit Networks with Online Information at Stops

Author

Listed:
  • Guido Gentile

    (Dipartimento di Idraulica Trasporti e Strade, Università degli Studi di Roma “La Sapienza,” via Eudossiana, 18-00184 Roma, Italy)

  • Sang Nguyen

    (Département d’Informatique et de Recherche Opérationnelle, Université de Montréal, CP 6128, Succ Centre-Ville, Montréal, Quebéc, Canada H3C 3J7)

  • Stefano Pallottino

    (Dipartimento di Informatica, Università di Pisa)

Abstract

Passengers on a transit network with common lines are often faced with the problem of choosing between either to board the arriving bus or to wait for a faster one. Many assignment models are based on the classical assumption that at a given stop passengers board the first arriving carrier of a certain subset of the available lines, often referred to as the attractive set. In this case, it has been shown that, if the headway distributions are exponential, then an optimal subset of lines minimizing the passenger travel time can be easily determined. However, when online information on future arrivals of buses are posted at the stop, it is unlikely that the above classical assumption holds. In this case, passengers may choose to board a line that offers the best combination of displayed waiting time and expected travel time to their destination once boarded. In this paper, we propose a general framework for determining the probability of boarding each line available at a stop when online information on bus waiting times is provided to passengers. We will also show that the classical model without online information may be interpreted as a particular instance of the proposed framework, this way achieving an extension to general headway distributions. The impact of the availability of information regarding bus arrivals and that of the regularity of transit lines on the network loads, as well as on the passenger travel times, will be illustrated with small numerical examples.

Suggested Citation

  • Guido Gentile & Sang Nguyen & Stefano Pallottino, 2005. "Route Choice on Transit Networks with Online Information at Stops," Transportation Science, INFORMS, vol. 39(3), pages 289-297, August.
  • Handle: RePEc:inm:ortrsc:v:39:y:2005:i:3:p:289-297
    DOI: 10.1287/trsc.1040.0109
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    References listed on IDEAS

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    Cited by:

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    4. Caspar Chorus & Theo Arentze & Harry Timmermans, 2007. "Information impact on quality of multimodal travel choices: conceptualizations and empirical analyses," Transportation, Springer, vol. 34(6), pages 625-645, November.
    5. 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.
    6. Trozzi, Valentina & Gentile, Guido & Bell, Michael G.H. & Kaparias, Ioannis, 2013. "Dynamic user equilibrium in public transport networks with passenger congestion and hyperpaths," Transportation Research Part B: Methodological, Elsevier, vol. 57(C), pages 266-285.
    7. Valentina Trozzi & Guido Gentile & Ioannis Kaparias & Michael Bell, 2015. "Effects of Countdown Displays in Public Transport Route Choice Under Severe Overcrowding," Networks and Spatial Economics, Springer, vol. 15(3), pages 823-842, September.
    8. 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.
    9. Tarun Rambha & Stephen D. Boyles & S. Travis Waller, 2016. "Adaptive Transit Routing in Stochastic Time-Dependent Networks," Transportation Science, INFORMS, vol. 50(3), pages 1043-1059, August.
    10. YalçInkaya, Özgür & Mirac Bayhan, G., 2009. "Modelling and optimization of average travel time for a metro line by simulation and response surface methodology," European Journal of Operational Research, Elsevier, vol. 196(1), pages 225-233, July.
    11. Khani, Alireza, 2019. "An online shortest path algorithm for reliable routing in schedule-based transit networks considering transfer failure probability," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 549-564.
    12. Wen, Jian & Nassir, Neema & Zhao, Jinhua, 2019. "Value of demand information in autonomous mobility-on-demand systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 121(C), pages 346-359.
    13. Liu, Yang & Blandin, Sebastien & Samaranayake, Samitha, 2019. "Stochastic on-time arrival problem in transit networks," Transportation Research Part B: Methodological, Elsevier, vol. 119(C), pages 122-138.
    14. Nassir, Neema & Hickman, Mark & Ma, Zhen-Liang, 2019. "A strategy-based recursive path choice model for public transit smart card data," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 528-548.
    15. Paulsen, Mads & Rasmussen, Thomas Kjær & Nielsen, Otto Anker, 2021. "Impacts of real-time information levels in public transport: A large-scale case study using an adaptive passenger path choice model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 155-182.
    16. Padma Seetharaman, 2017. "Modelling risk aversion using a disaggregate stochastic process model in congested transit networks," Public Transport, Springer, vol. 9(3), pages 549-569, October.
    17. Li, Qianfei & (Will) Chen, Peng & (Marco) Nie, Yu, 2015. "Finding optimal hyperpaths in large transit networks with realistic headway distributions," European Journal of Operational Research, Elsevier, vol. 240(1), pages 98-108.
    18. 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.
    19. Kumar, Pramesh & Khani, Alireza, 2022. "Planning of integrated mobility-on-demand and urban transit networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 166(C), pages 499-521.

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