During railway disruptions, most passengers may not be able to find preferred alternative train services due to the current way of handling disruptions that does not take passenger responses into account. To offer better alternatives to passengers, this paper proposes a novel passenger-oriented timetable rescheduling model, which integrates timetable rescheduling and passenger reassignment into a Mixed Integer Linear Programming model with the objective of minimizing generalized travel times: in-vehicle times, waiting times at origin/transfer stations and the number of transfers. The model applies the dispatching measures of re-timing, re-ordering, cancelling, flexible stopping and flexible short-turning trains, handles rolling stock circulations at both short-turning and terminal stations of trains, and takes station capacity into account. To solve the model efficiently, an Adapted Fix-and-Optimize (AFaO) algorithm is developed. Numerical experiments were carried out to a part of the Dutch railways. The results show that the proposed passenger-oriented timetable rescheduling model is able to shorten generalized travel times significantly compared to an operator-oriented timetable rescheduling model that does not consider passenger responses. By allowing only 10 min more train delay than an optimal operator-oriented rescheduling solution, the passenger-oriented model is able to shorten the generalized travel times over all passengers by thousands of minutes in all considered disruption scenarios. With a passenger-oriented rescheduled timetable, more passengers continue their train travels after a disruption started, compared to a rescheduled timetable from the operator-oriented model. The AFaO algorithm obtains high-quality solutions to the passenger-oriented model in up to 300 s.

在铁路中断期间，由于当前处理中断的方式并未考虑乘客的响应，大多数乘客可能无法找到首选的火车服务。为了给乘客提供更好的替代选择，本文提出了一种新颖的面向乘客的时间表重新安排模型，该模型将时间表重新安排和乘客重新分配整合到混合整数线性规划模型中，目的是最大程度地减少广义旅行时间：车内时间，等待时间起点/中转站和中转次数。该模型应用了重新定时，重新排序，取消，灵活停车和灵活短途火车的调度措施，处理列车的短途转车和终点站的机车车辆流通，并考虑了车站的容量。为了有效地求解模型，开发了自适应修正和优化（AFaO）算法。对荷兰铁路的一部分进行了数值实验。结果表明，与不考虑乘客响应的面向运营商的时间表重新安排模型相比，所提出的面向乘客的时间表重新安排模型能够显着缩短广义出行时间。与最佳的面向运营商的重新计划解决方案相比，通过仅允许10分钟的火车延误，以乘客为导向的模型就可以在所有考虑的中断情况下将所有乘客的一般旅行时间缩短数千分钟。通过以乘客为导向的时间表，在中断开始后，更多的乘客继续火车旅行，与面向运营商的模型重新安排的时间表相比。AFaO算法可在300 s内获得面向乘客模型的高质量解决方案。