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Approaches to a real-world Train Timetabling Problem in a railway node

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  • Cacchiani, Valentina
  • Furini, Fabio
  • Kidd, Martin Philip

Abstract

We consider the Train Timetabling Problem (TTP) in a railway node (i.e. a set of stations in an urban area interconnected by tracks), which calls for determining the best schedule for a given set of trains during a given time horizon, while satisfying several track operational constraints. In particular, we consider the context of a highly congested railway node in which different Train Operators wish to run trains according to timetables that they propose, called ideal timetables. The ideal timetables altogether may be (and usually are) conflicting, i.e. they do not respect one or more of the track operational constraints. The goal is to determine conflict-free timetables that differ as little as possible from the ideal ones.

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  • 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.
    • Handle: RePEc:eee:jomega:v:58:y:2016:i:c:p:97-110
    DOI: 10.1016/j.omega.2015.04.006
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    17. Yin, Jiateng & Tang, Tao & Yang, Lixing & Gao, Ziyou & Ran, Bin, 2016. "Energy-efficient metro train rescheduling with uncertain time-variant passenger demands: An approximate dynamic programming approach," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 178-210.
    18. Liang, Jinpeng & Zang, Guangzhi & Liu, Haitao & Zheng, Jianfeng & Gao, Ziyou, 2023. "Reducing passenger waiting time in oversaturated metro lines with passenger flow control policy," Omega, Elsevier, vol. 117(C).
    19. Zhou, Yu & Wang, Yun & Yang, Hai & Yan, Xuedong, 2019. "Last train scheduling for maximizing passenger destination reachability in urban rail transit networks," Transportation Research Part B: Methodological, Elsevier, vol. 129(C), pages 79-95.
    20. Li, Wenqing & Ni, Shaoquan, 2022. "Train timetabling with the general learning environment and multi-agent deep reinforcement learning," Transportation Research Part B: Methodological, Elsevier, vol. 157(C), pages 230-251.
    21. 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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