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On-Line Timetable Rescheduling in a Transit Line

Author

Listed:
  • Francisco A. Ortega

    (Department of Applied Mathematics I, University of Seville, Seville 41012, Spain)

  • Miguel A. Pozo

    (Department of Applied Mathematics II, University of Seville, Seville 41092, Spain)

  • Justo Puerto

    (Department of Statistics and Operational Research, University of Seville, Seville 41092, Spain)

Abstract

Public transportation systems in metropolitan areas carry a high density of daily traffic, heterogeneously distributed, and exposed to the negative consequences derived from service disruptions. Breakdowns, accidents, strikes, etc., require on-line operation adjustments to address these incidents and thus reduce their side effects, such as passenger extra-waiting times, complaints, potential operational dangers, etc. The Vehicle Rescheduling Problem consists of defining a new schedule for a set of previously scheduled trips, given that one/several trips cannot be carried out. This paper addresses the rescheduling problem in a transit line that has suffered a fleet size reduction (also denoted as Reduced Fleet Rescheduling Problem). We present different modeling possibilities depending on the assumptions that must be included in the modelization and we show that the problem can be rapidly solved using a reformulation that will be proven to have the integrality property. We test our results in a testbed of random instances outperforming previous results in the literature. We also include a real-world case of the commuter trains of Madrid, Spain to illustrate our solutions.

Suggested Citation

  • Francisco A. Ortega & Miguel A. Pozo & Justo Puerto, 2018. "On-Line Timetable Rescheduling in a Transit Line," Transportation Science, INFORMS, vol. 52(5), pages 1106-1121, October.
    • Handle: RePEc:inm:ortrsc:v:52:y:2018:i:5:p:1106-1121
    DOI: 10.1287/trsc.2017.0807
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    References listed on IDEAS

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    3. Guang Yang & Feng Zhang & Cheng Gong & Shiwen Zhang, 2019. "Application of a Deep Deterministic Policy Gradient Algorithm for Energy-Aimed Timetable Rescheduling Problem," Energies, MDPI, vol. 12(18), pages 1-19, September.
    4. Yin, Jiateng & Pu, Fan & Yang, Lixing & D’Ariano, Andrea & Wang, Zhouhong, 2023. "Integrated optimization of rolling stock allocation and train timetables for urban rail transit networks: A benders decomposition approach," Transportation Research Part B: Methodological, Elsevier, vol. 176(C).
    5. Kuo, Yong-Hong & Leung, Janny M.Y. & Yan, Yimo, 2023. "Public transport for smart cities: Recent innovations and future challenges," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1001-1026.
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    7. Blanco, Víctor & Conde, Eduardo & Hinojosa, Yolanda & Puerto, Justo, 2020. "An optimization model for line planning and timetabling in automated urban metro subway networks. A case study," Omega, Elsevier, vol. 92(C).

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