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A train rescheduling model integrating speed management during disruptions of high-speed traffic under a quasi-moving block system

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  • Xu, Peijuan
  • Corman, Francesco
  • Peng, Qiyuan
  • Luan, Xiaojie

Abstract

Chinese high-speed railways faced a fast development in recent years. Their performances are still confronted with disruptions unavoidably, which impact on the reliability of the traffic and passenger satisfaction. This paper presents a rescheduling model which is able to solve the critical problem of effective disruption management (namely, fast and dynamic train speed adaptation, supervision of braking and changing train sequence due to incidents, warnings or alarms), and consider in detail the signalling and safety systems based on a quasi-moving block system with variable headways. We integrate the modelling of efficient traffic management measures and the supervision of speed, braking and headway in one general job-shop model. We use a commercial solver with a custom-designed two-step method to speed up the procedure in order to solve instances from real-world high-speed networks in China quickly. Overall, the approach guarantees the resolution of the traffic control and speed management within few minutes of computation time. The output demonstrates that the proposed approach can achieve a reduction of train delays by 70% compared to the solution determined by keeping the order of the original timetable, and get the optimality for more than 90% of instances with a realistic case.

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  • Xu, Peijuan & Corman, Francesco & Peng, Qiyuan & Luan, Xiaojie, 2017. "A train rescheduling model integrating speed management during disruptions of high-speed traffic under a quasi-moving block system," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 638-666.
    • Handle: RePEc:eee:transb:v:104:y:2017:i:c:p:638-666
    DOI: 10.1016/j.trb.2017.05.008
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    3. 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.
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    5. Wang, Xuekai & D’Ariano, Andrea & Su, Shuai & Tang, Tao, 2023. "Cooperative train control during the power supply shortage in metro system: A multi-agent reinforcement learning approach," Transportation Research Part B: Methodological, Elsevier, vol. 170(C), pages 244-278.
    6. Annunziata Esposito Amideo & Stefano Starita & Maria Paola Scaparra, 2019. "Assessing Protection Strategies for Urban Rail Transit Systems: A Case-Study on the Central London Underground," Sustainability, MDPI, vol. 11(22), pages 1-21, November.
    7. 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.
    8. Sairong Peng & Xin Yang & Hongwei Wang & Hairong Dong & Bin Ning & Haichuan Tang & Zhipeng Ying & Ruijun Tang, 2019. "Dispatching High-Speed Rail Trains via Utilizing the Reverse Direction Track: Adaptive Rescheduling Strategies and Application," Sustainability, MDPI, vol. 11(8), pages 1-20, April.
    9. Jiateng Yin & Lixing Yang & Xuesong Zhou & Tao Tang & Ziyou Gao, 2019. "Balancing a one‐way corridor capacity and safety‐oriented reliability: A stochastic optimization approach for metro train timetabling," Naval Research Logistics (NRL), John Wiley & Sons, vol. 66(4), pages 297-320, June.
    10. Luan, Xiaojie & Wang, Yihui & De Schutter, Bart & Meng, Lingyun & Lodewijks, Gabriel & Corman, Francesco, 2018. "Integration of real-time traffic management and train control for rail networks - Part 1: Optimization problems and solution approaches," Transportation Research Part B: Methodological, Elsevier, vol. 115(C), pages 41-71.
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    12. Luan, Xiaojie & De Schutter, Bart & Meng, Lingyun & Corman, Francesco, 2020. "Decomposition and distributed optimization of real-time traffic management for large-scale railway networks," Transportation Research Part B: Methodological, Elsevier, vol. 141(C), pages 72-97.

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