Railway timetable rescheduling with flexible stopping and flexible short-turning during disruptions

• Published in: Transportation research. Part B: methodological Vol. 123; pp. 149 - 181
• Main Authors: Zhu, Yongqiu, Goverde, Rob M.P.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.05.2019; Elsevier Science Ltd
• Subjects: Cycle time; Disruption management; Flexible short-turning; Flexible stopping; Integer programming; Linear programming; Mixed integer; Passengers; Railway stations; Railways; Rescheduling; Time dependence; Timetable rescheduling; Timetables; Travel demand; Flexible stopping; Disruption management; Timetable rescheduling; Railways; Flexible short-turning
• DOI: 10.1016/j.trb.2019.02.015
• ISSN: 0191-2615

•A new timetable rescheduling model is proposed, which includes flexible stopping and flexible short-turning as well as retiming, reordering, and cancelling trains.•The model deals with all phases of a disruption.•Adjusted train running times due to saved (extra) decelerations and accelerations are explicitly considered when skipping (adding) stops.•Station capacity is considered by ensuring that each train corresponding to passenger boarding/alighting stops at a platform track while the minimum headway times are taken into account.•Rolling stock circulations at the short-turning and terminal stations of trains are included.•Dispatching decisions are optimized with the objective of minimizing passenger delays. Railway operations are vulnerable to unexpected disruptions that should be handled in an efficient and passenger-friendly way. To this end, we propose a timetable rescheduling model where flexible stopping (i.e. skipping stops and adding stops) and flexible short-turning (i.e. full choice of short-turn stations) are innovatively integrated with three other dispatching measures: retiming, reordering, and cancelling. The Mixed Integer Linear Programming model also ensures that each train serving a station is ensured with a platform track. To consider the rescheduling impact on passengers, the weight of each decision is estimated individually according to the time-dependent passenger demand. The objective is minimizing passenger delays. A case study is carried out for hundreds of disruption scenarios on a subnetwork of the Dutch railways. It is found that (1) applying a mix of flexible stopping and flexible short-turning results in less passenger delays; (2) shortening the recovery duration mitigates the post-disruption consequence by less delay propagation but is at the expense of more cancelled train services during the disruption; and (3) the optimal rescheduling solution is sensitive to the disruption duration, but some steady behaviour is observed when the disruption duration increases by the timetable cycle time.