Sofie Burggraeve

An iterative approach for reducing the impact of infrastructure maintenance on the performance of railway systems

Planned infrastructure works reduce the available capacity of a railway system and make it more vulnerable to conflicts and delay propagation. The starting point of this paper is a published timetable that needs to be adapted due to the temporary unavailability of some resources. Since the timetable is in operation, changed arrival or departure times and cancelations have an impact on the passengers who need to adapt their travel behavior. In the light of passenger service, a trade-off is made between these inconveniences and the delays that occur in practice due to the reduced capacity. Taking the robustness of the adapted railway timetable into account is a new approach to rescheduling in case of a planned infrastructure unavailability. In this paper, an algorithm that adjusts the train routing and the train schedule to the planned maintenance interventions and keeps the level of passenger service as high as possible is presented. To avoid large inconveniences, the developed algorithm tries to minimize the number of cancelations. Computational results show that by allowing small modifications to the routing and the timetable, the robustness of the resulting solution can improve by more than 10 percent and only few trains need to be canceled.

Optimization of supplements and buffer times in passenger robust timetabling

Abstract This paper proposes an iterative approach to construct a passenger robust railway routing plan and timetable from scratch that also takes supplement and buffer time allocation into account on the signaling level. Each iteration is based on four pillars, which are subsequently executed. First a routing plan that optimizes infrastructure usage is constructed. Secondly, a timetable is constructed that optimizes buffer times between trains while taking passenger numbers into account. Thirdly, simulation is used to evaluate the passenger robustness of the routing plan and the timetable. Finally, the simulation outcome is used to make a new supplement assignment for the next iteration. The algorithm is tested on a case study for the complex station area of Brussels (Belgium). The resulting timetable and routing plan are much more passenger robust than the existing combinations of timetables and routing plans for this case, developed during research or implemented in practice.