Train Timetable Rescheduling for High-Density Urban Rail Traffic Management

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.