Integrated optimization of capacitated train rescheduling and passenger reassignment under disruptions

Xin Hong, Lingyun Meng*, Andrea D'Ariano, Lucas P. Veelenturf, Sihui Long, Francesco Corman

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

8 Citations (Scopus)
2 Downloads (Pure)


During railway operations, unexpected events may influence normal traffic flows. This paper focuses on a train rescheduling problem for handling large disruptions, such as a rolling stock breakdown leading to a cancelled train service, where passenger reassignment strategies have to be considered. A novel mixed-integer linear programming formulation is established with consideration of train retiming, reordering, rerouting, and reservicing (addition of extra stops). The proposed mathematical formulation considers planning extra stops for non-canceled trains in order to transport the disrupted passengers, which were supposed to travel on the canceled train, to their pre-planned destination stations. Other constraints deal with limited seat capacity and track capacity, and mapping train rescheduling with passenger reassignment. A bi-objective function is optimized by a weighted-sum method to maximize the number of disrupted passengers reaching their destination stations and to minimize the weighted total train delay for all non-canceled trains at their destinations. A series of numerical experiments based on a part of the Beijing-Shanghai high-speed railway line is carried out to verify the effectiveness and efficiency of the proposed model and to perform a sensitivity analysis of various performance factors. The results show that an optimal reassignment plan of disrupted passengers is important to achieve real-time efficiency of traffic and re-ticketing. The impact of passenger reassignment on train rescheduling is influenced by the weights for objectives, duration of disruption, allowed additional dwell and running times, and relationship between passenger demand and total available train capacity.

Original languageEnglish
Article number103025
JournalTransportation Research Part C: Emerging Technologies
Publication statusPublished - 1 Apr 2021


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