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Rescheduling of Railway Rolling Stock with Dynamic Passenger Flows

Author

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  • Leo Kroon

    (Rotterdam School of Management, Erasmus University Rotterdam, 3000 DR Rotterdam, The Netherlands; and Netherlands Railways, 3500 HA Utrecht, The Netherlands)

  • Gábor Maróti

    (Faculty of Economics and Business Administration, VU University Amsterdam, 1081 HV Amsterdam, The Netherlands; and Netherlands Railways, 3500 HA Utrecht, The Netherlands)

  • Lars Nielsen

    (Quintiq Applications, 5232 CD ‘s-Hertogenbosch, The Netherlands)

Abstract

In this paper we describe a real-time rolling stock rescheduling model for disruption management of passenger railways. Large-scale disruptions, e.g., due to malfunctioning infrastructure or rolling stock, usually result in the cancellation of train services. As a consequence, the passenger flows change, because passengers will look for alternative routes to get to their destinations. Our model takes these dynamic passenger flows into account. This is in contrast with most traditional rolling stock rescheduling models that consider the passenger flows either as static or as given input. Furthermore, we describe an iterative heuristic for solving the rolling stock rescheduling model with dynamic passenger flows. The model and the heuristic were tested on realistic problem instances of Netherlands Railways, the major operator of passenger trains in the Netherlands. The computational results show that the average delay of the passengers can be reduced significantly by taking into account the dynamic behavior of the passenger flows on the detour routes, and that the computation times of the iterative heuristic are appropriate for an application in real-time disruption management.

Suggested Citation

  • Leo Kroon & Gábor Maróti & Lars Nielsen, 2015. "Rescheduling of Railway Rolling Stock with Dynamic Passenger Flows," Transportation Science, INFORMS, vol. 49(2), pages 165-184, May.
    • Handle: RePEc:inm:ortrsc:v:49:y:2015:i:2:p:165-184
    DOI: 10.1287/trsc.2013.0502
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    References listed on IDEAS

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    7. Wagenaar, Joris & Kroon, Leo & Fragkos, Ioannis, 2017. "Rolling stock rescheduling in passenger railway transportation using dead-heading trips and adjusted passenger demand," Transportation Research Part B: Methodological, Elsevier, vol. 101(C), pages 140-161.
    8. Mo, Pengli & D’Ariano, Andrea & Yang, Lixing & Veelenturf, Lucas P. & Gao, Ziyou, 2021. "An exact method for the integrated optimization of subway lines operation strategies with asymmetric passenger demand and operating costs," Transportation Research Part B: Methodological, Elsevier, vol. 149(C), pages 283-321.
    9. Chan, Ho-Yin & Chen, Anthony & Li, Guoyuan & Xu, Xiangdong & Lam, William, 2021. "Evaluating the value of new metro lines using route diversity measures: The case of Hong Kong's Mass Transit Railway system," Journal of Transport Geography, Elsevier, vol. 91(C).
    10. Evelien van der Hurk & Haris N. Koutsopoulos & Nigel Wilson & Leo G. Kroon & Gábor Maróti, 2016. "Shuttle Planning for Link Closures in Urban Public Transport Networks," Transportation Science, INFORMS, vol. 50(3), pages 947-965, August.
    11. Leng, Nuannuan & Corman, Francesco, 2020. "The role of information availability to passengers in public transport disruptions: An agent-based simulation approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 133(C), pages 214-236.
    12. Haahr, Jørgen T. & Wagenaar, Joris C. & Veelenturf, Lucas P. & Kroon, Leo G., 2016. "A comparison of two exact methods for passenger railway rolling stock (re)scheduling," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 91(C), pages 15-32.
    13. Tatsuki Yamauchi & Mizuyo Takamatsu & Shinji Imahori, 2023. "Optimizing train stopping patterns for congestion management," Public Transport, Springer, vol. 15(1), pages 1-29, March.
    14. Chen, Zebin & Li, Shukai & D’Ariano, Andrea & Yang, Lixing, 2022. "Real-time optimization for train regulation and stop-skipping adjustment strategy of urban rail transit lines," Omega, Elsevier, vol. 110(C).
    15. Mo, Baichuan & Koutsopoulos, Haris N. & Shen, Zuo-Jun Max & Zhao, Jinhua, 2023. "Robust path recommendations during public transit disruptions under demand uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 169(C), pages 82-107.
    16. Chang Han & Leishan Zhou & Bin Guo & Yixiang Yue & Wenqiang Zhao & Zeyu Wang & Hanxiao Zhou, 2023. "An Integrated Strategy for Rescheduling High-Speed Train Operation under Single-Direction Disruption," Sustainability, MDPI, vol. 15(17), pages 1-31, August.
    17. Altazin, Estelle & Dauzère-Pérès, Stéphane & Ramond, François & Tréfond, Sabine, 2020. "A multi-objective optimization-simulation approach for real time rescheduling in dense railway systems," European Journal of Operational Research, Elsevier, vol. 286(2), pages 662-672.

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