Train Timetable Rescheduling for High-Density Urban Rail Traffic Management

Ruoyan Xu, Xuewu Dai*, Qunyan Xing, Zhiming Yuan, Miao Zhang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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Rapid rail transit plays an essential role in mega cities around the world. Urban rail features high-frequency service with less headways, and a small delay caused by unexpected events, such as such as extreme weather, device or infrastructure failures, could spread to other trains and result in major implications on the planned timetable. It also makes the train timetable rescheduling (TTR) very challenging when traffic is disturbance. This paper studies the train time table rescheduling algorithm for high-density urban rail systems, aiming to mitigate the impacts of delays on the overall railway operations. A Mixed Integer Linear Programming (MILP) model is developed, incorporating various critical constraints, including various headways between trains with different stop-skip plan at different stations. The MILP model is sloved by the Gorobi solver, to generate optimal rescheduled timetable and assistant dispatchers to make intelligent decisions to dynamically adjust train schedules.

Original languageEnglish
Title of host publication2023 IEEE Smart World Congress (SWC)
Place of PublicationPiscataway, NY
Number of pages5
ISBN (Electronic)9798350319804
ISBN (Print)9798350319811
Publication statusPublished - 28 Aug 2023
Event9th IEEE Smart World Congress, SWC 2023 - Portsmouth, United Kingdom
Duration: 28 Aug 202331 Aug 2023


Conference9th IEEE Smart World Congress, SWC 2023
Country/TerritoryUnited Kingdom

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