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Optimal Real-Time Traffic Control in Metro Stations

Author

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  • Carlo Mannino

    (Dip. Informatica e Sistemistica, Sapienza Università di Roma, Rome 00185, Italy)

  • Alessandro Mascis

    (Bombardier Transportation Italy S.P.A., 17047 Vado Ligure (SV), Italy)

Abstract

Train movements across railway stations are still operated by human dispatchers. Motivated by an application provided by Azienda Trasporti Milanesi (ATM), the major Italian municipal transport company, we developed a real-time automated traffic control system to operate trains in metro stations. The system optimally controls the trains in a metro station by identifying a suitable routing and by establishing an optimum schedule of the performed operations. For each candidate routing an instance of the blocking, no-wait job-shop scheduling problem with convex costs is solved to optimality by branch and bound. A new, effective lower bound is developed to speed up the enumeration process. Computational testing in a real environment proved that the algorithm is able to solve relevant practical instances within the very tight time limit imposed by the application. The system has been in operation in the Milan metro since July 2007. To our knowledge, this is the first example of successful application of optimization methods to real-time traffic control in metro stations.

Suggested Citation

  • Carlo Mannino & Alessandro Mascis, 2009. "Optimal Real-Time Traffic Control in Metro Stations," Operations Research, INFORMS, vol. 57(4), pages 1026-1039, August.
  • Handle: RePEc:inm:oropre:v:57:y:2009:i:4:p:1026-1039
    DOI: 10.1287/opre.1080.0642
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    8. Cacchiani, Valentina & Furini, Fabio & Kidd, Martin Philip, 2016. "Approaches to a real-world Train Timetabling Problem in a railway node," Omega, Elsevier, vol. 58(C), pages 97-110.
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    10. Li, Shukai & Liu, Ronghui & Gao, Ziyou & Yang, Lixing, 2021. "Integrated train dwell time regulation and train speed profile generation for automatic train operations on high-density metro lines: A distributed optimal control method," Transportation Research Part B: Methodological, Elsevier, vol. 148(C), pages 82-105.
    11. Rajnish Kumar & Goutam Sen & Samarjit Kar & Manoj Kumar Tiwari, 2018. "Station Dispatching Problem for a Large Terminal: A Constraint Programming Approach," Interfaces, INFORMS, vol. 48(6), pages 510-528, November.
    12. 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.
    13. Vansteenwegen, Pieter & Dewilde, Thijs & Burggraeve, Sofie & Cattrysse, Dirk, 2016. "An iterative approach for reducing the impact of infrastructure maintenance on the performance of railway systems," European Journal of Operational Research, Elsevier, vol. 252(1), pages 39-53.
    14. Meng, Lingyun & Zhou, Xuesong, 2014. "Simultaneous train rerouting and rescheduling on an N-track network: A model reformulation with network-based cumulative flow variables," Transportation Research Part B: Methodological, Elsevier, vol. 67(C), pages 208-234.
    15. Yuan, Yin & Li, Shukai & Yang, Lixing & Gao, Ziyou, 2022. "Real-time optimization of train regulation and passenger flow control for urban rail transit network under frequent disturbances," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 168(C).
    16. Li, Shukai & Dessouky, Maged M. & Yang, Lixing & Gao, Ziyou, 2017. "Joint optimal train regulation and passenger flow control strategy for high-frequency metro lines," Transportation Research Part B: Methodological, Elsevier, vol. 99(C), pages 113-137.
    17. David Canca & Eva Barrena & Gilbert Laporte & Francisco A. Ortega, 2016. "A short-turning policy for the management of demand disruptions in rapid transit systems," Annals of Operations Research, Springer, vol. 246(1), pages 145-166, November.
    18. Xu, Xiaoming & Li, Keping & Yang, Lixing, 2015. "Scheduling heterogeneous train traffic on double tracks with efficient dispatching rules," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 364-384.
    19. Li, Shukai & Zhou, Xuesong & Yang, Lixing & Gao, Ziyou, 2018. "Automatic train regulation of complex metro networks with transfer coordination constraints: A distributed optimal control framework," Transportation Research Part B: Methodological, Elsevier, vol. 117(PA), pages 228-253.
    20. Allahverdi, Ali & Aydilek, Harun & Aydilek, Asiye, 2018. "No-wait flowshop scheduling problem with two criteria; total tardiness and makespan," European Journal of Operational Research, Elsevier, vol. 269(2), pages 590-601.
    21. Huang, Yeran & Mannino, Carlo & Yang, Lixing & Tang, Tao, 2020. "Coupling time-indexed and big-M formulations for real-time train scheduling during metro service disruptions," Transportation Research Part B: Methodological, Elsevier, vol. 133(C), pages 38-61.
    22. Lucas P. Veelenturf & Martin P. Kidd & Valentina Cacchiani & Leo G. Kroon & Paolo Toth, 2016. "A Railway Timetable Rescheduling Approach for Handling Large-Scale Disruptions," Transportation Science, INFORMS, vol. 50(3), pages 841-862, August.
    23. Sartor, Giorgio & Mannino, Carlo & Nygreen, Thomas & Bach, Lukas, 2023. "A MILP model for quasi-periodic strategic train timetabling," Omega, Elsevier, vol. 116(C).

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