Unexpected disruptions commonly occur in the railway network, causing delays, and extra cost for operators and inconvenience for passengers by missing their connection and facing overcrowded trains. This paper presents a passenger-centric approach for timetable rescheduling in case of disruption. We study a railway system in which passengers are free to choose their itinerary and compete over limited train capacity. We explicitly model the passengers’ decisions using a choice model. We propose a multi-objective algorithmic approach to solve the problem. Service punctuality, operating cost, and passengers’ inconvenience are selected as objectives. Computational experiments are performed on the Swiss and Dutch railway networks. The results demonstrate the performance of the algorithm in finding high-quality solutions in a computationally efficient manner.@en

Unexpected disruptions occur for many reasons in railway networks and cause delays, cancelations, and, eventually, passenger inconvenience. This research focuses on the railway timetable rescheduling problem from a macroscopic point of view in case of large disruptions. The originality of our approach is to integrate three objectives to generate a disposition timetable: the passenger satisfaction, the operational costs and the deviation from the undisrupted timetable. We formulate the problem as an Integer Linear Program that optimizes the first objective and includes ε-constraints for the two other ones. By solving the problem for different values of ε, the three-dimensional Pareto frontier can be explored to understand the trade-offs among the three objectives. The model includes measures such as canceling, delaying or rerouting the trains of the undisrupted timetable, as well as scheduling emergency trains. Furthermore, passenger flows are adapted dynamically to the new timetable. Computational experiments are performed on a realistic case study based on a heavily used part of the Dutch railway network. The model is able to find optimal solutions in reasonable computational times. The results provide evidence that adopting a demand-oriented approach for the management of disruptions not only is possible, but may lead to significant improvement in passenger satisfaction, associated with a low operational cost of the disposition timetable.@en

Unexpected disruptions occur for many reasons in railway networks and cause delays, cancelations, and, eventually, passenger inconvenience. This research focuses on the railway timetable rescheduling problem from a macroscopic point of view in case of large disruptions. The originality of our approach is to integrate three objectives to generate a disposition timetable: the passenger satisfaction, the operational costs and the deviation from the undisrupted timetable. We formulate the problem as an Integer Linear Program that optimizes the first objective and includes ε-constraints for the two other ones. By solving the problem for different values of ε, the three-dimensional Pareto frontier can be explored to understand the trade-offs among the three objectives. The model includes measures such as canceling, delaying or rerouting the trains of the undisrupted timetable, as well as scheduling emergency trains. Furthermore, passenger flows are adapted dynamically to the new timetable. Computational experiments are performed on a realistic case study based on a heavily used part of the Dutch railway network. The model is able to find optimal solutions in reasonable computational times. The results provide evidence that adopting a demand-oriented approach for the management of disruptions not only is possible, but may lead to significant improvement in passenger satisfaction, associated with a low operational cost of the disposition timetable.@en

We propose a novel algorithm for the capacitated passenger assignment problem in public transportation where exogenous priority lists define the order in which passengers are assigned. Separating explicitly theses rules from the assignment procedure allows for a great deal of flexibility to model various priority rules. When the actual rules are endogenous, the framework can easily be embedded in a fixed-point specification. Computational experiments are performed on a realistic case study based on the morning rush hours of the timetable of Canton Vaud, Switzerland. The algorithm is able to assign the demand in very low computational times. The results provide evidences that the ordering of the passengers does not have a significant impact on aggregate performance indicators (such as average delay and level of unsatisfied demand), but that the variability at the individual passenger level is substantial. Thanks to its flexibility, our framework can easily be implemented by a railway operator who wishes to evaluate the effects of different policies in terms of passenger priorities.@en