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Integrated railway timetable rescheduling and dynamic passenger routing during a complete blockage

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  • Zhan, Shuguang
  • Wong, S.C.
  • Shang, Pan
  • Peng, Qiyuan
  • Xie, Jiemin
  • Lo, S.M.

Abstract

Trains normally run as scheduled in a non-disrupted situation. However, due to external and/or internal factors, trains may deviate from their original timetable during daily operations. To this end, the involved dispatchers are required to reschedule disrupted trains to efficiently transport delayed passengers to their destinations as soon as possible. In this study, we focus on train rescheduling in a seriously disrupted situation where a track segment is completely blocked for a relatively long period of time, e.g., two hours. In this situation, trains cannot pass the disrupted segment, meaning that passengers will be unable to travel as scheduled. We simultaneously rescheduled trains and passenger routes from both the operator’s and passengers’ perspectives. This integrated train rescheduling and passenger rerouting problem was formulated with an Integer Linear Programming model based on a space-time network. We decomposed the integrated model into two subproblems, a train rescheduling problem and a passenger routing problem, using the alternating direction method of multipliers (ADMM) algorithm. Both subproblems could be further decomposed into a series of shortest path problems for trains or passengers, and solved by a dynamic programming algorithm. Finally, we tested our models and algorithms on both a small hypothetical railway network and a part of the Chinese high-speed railway network.

Suggested Citation

  • Zhan, Shuguang & Wong, S.C. & Shang, Pan & Peng, Qiyuan & Xie, Jiemin & Lo, S.M., 2021. "Integrated railway timetable rescheduling and dynamic passenger routing during a complete blockage," Transportation Research Part B: Methodological, Elsevier, vol. 143(C), pages 86-123.
    • Handle: RePEc:eee:transb:v:143:y:2021:i:c:p:86-123
    DOI: 10.1016/j.trb.2020.11.006
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    3. Zhan, Shuguang & Xie, Jiemin & Wong, S.C. & Zhu, Yongqiu & Corman, Francesco, 2024. "Handling uncertainty in train timetable rescheduling: A review of the literature and future research directions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 183(C).
    4. Wang, Entai & Yang, Lixing & Yin, Jiateng & Zhang, Jinlei & Gao, Ziyou, 2024. "Passenger-oriented rolling stock scheduling in the metro system with multiple depots: Network flow based approaches," Transportation Research Part B: Methodological, Elsevier, vol. 180(C).
    5. Yidong Wang & Rui Song & Shiwei He & Zilong Song, 2022. "Train Routing and Track Allocation Optimization Model of Multi-Station High-Speed Railway Hub," Sustainability, MDPI, vol. 14(12), pages 1-21, June.
    6. Shang, Pan & Xiong, Yufan & Guo, Jifu & Xian, Kai & Yu, Yun & Xu, Han, 2024. "A modeling framework to integrate frequency - and schedule-based passenger assignment approaches for coordinated path choice and space-time trajectory estimation based on multi-source observations," Transportation Research Part B: Methodological, Elsevier, vol. 183(C).
    7. Feng, Tao & Lusby, Richard M. & Zhang, Yongxiang & Tao, Siyu & Zhang, Bojian & Peng, Qiyuan, 2024. "A branch-and-price algorithm for integrating urban rail crew scheduling and rostering problems," Transportation Research Part B: Methodological, Elsevier, vol. 183(C).
    8. Xie, Jiemin & Zhan, Shuguang & Wong, S.C. & Wen, Keyu & Qiang, Lixia & Lo, S.M., 2022. "High-speed rail services for elderly passengers: Ticket-booking patterns and policy implications," Transport Policy, Elsevier, vol. 125(C), pages 96-106.
    9. Meloni, Carlo & Pranzo, Marco & Samà, Marcella, 2021. "Risk of delay evaluation in real-time train scheduling with uncertain dwell times," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    10. Liu, Enze & Barker, Kash & Chen, Hong, 2022. "A multi-modal evacuation-based response strategy for mitigating disruption in an intercity railway system," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    11. Zhan, Shuguang & Wang, Pengling & Wong, S.C. & Lo, S.M., 2022. "Energy-efficient high-speed train rescheduling during a major disruption," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    12. Luo, Jie & Wen, Chao & Peng, Qiyuan & Qin, Yong & Huang, Ping, 2023. "Forecasting the effect of traffic control strategies in railway systems: A hybrid machine learning method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 621(C).
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