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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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    References listed on IDEAS

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    3. Corman, Francesco & D’Ariano, Andrea & Marra, Alessio D. & Pacciarelli, Dario & Samà, Marcella, 2017. "Integrating train scheduling and delay management in real-time railway traffic control," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 105(C), pages 213-239.
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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.
    9. Allahverdi, Ali, 2016. "A survey of scheduling problems with no-wait in process," European Journal of Operational Research, Elsevier, vol. 255(3), pages 665-686.
    10. Corman, F. & D’Ariano, A. & Pacciarelli, D. & Pranzo, M., 2012. "Optimal inter-area coordination of train rescheduling decisions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(1), pages 71-88.
    11. 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.
    12. 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.
    13. 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.
    14. Li, Shukai & Liu, Ronghui & Yang, Lixing & Gao, Ziyou, 2019. "Robust dynamic bus controls considering delay disturbances and passenger demand uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 123(C), pages 88-109.
    15. 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.
    16. 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.
    17. Lamorgese, Leonardo & Mannino, Carlo & Natvig, Erik, 2017. "An exact micro–macro approach to cyclic and non-cyclic train timetabling," Omega, Elsevier, vol. 72(C), pages 59-70.
    18. Veelenturf, L.P. & Kidd, M.P. & Cacchiani, V. & Kroon, L.G. & Toth, P., 2014. "A railway timetable rescheduling approach for handling large scale disruptions," ERIM Report Series Research in Management ERS-2014-010-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    19. 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.
    20. 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).
    21. Erfan Hassannayebi & Seyed Hessameddin Zegordi & Mohammad Reza Amin-Naseri & Masoud Yaghini, 2018. "Optimizing headways for urban rail transit services using adaptive particle swarm algorithms," Public Transport, Springer, vol. 10(1), pages 23-62, May.
    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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