Peter Sels

Improving the robustness in railway station areas

In order to improve the robustness of a railway system in station areas, this paper introduces an iterative approach to successively optimize the train routing through station areas and to enhance this solution by applying some changes to the timetable in a tabu search environment. We present our vision on robustness and describe how this vision can be used in practice. By introducing the spread of the trains in the objective function for the route choice and timetabling module, we improve the robustness of a railway system. Using a discrete event simulation model, the performance of our algorithms is evaluated based on a case study for the Brussels’ area. The computational results indicate an average improvement in robustness of 6.2% together with a decrease in delay propagation of about 25%. Furthermore, the effect of some measures like changing the train offer to further increase the robustness is evaluated and compared.

Robust railway station planning: An interaction between routing, timetabling and platforming

Abstract In this paper, we consider complex, busy stations whose limited capacity is one of the main reasons of delay propagation. Our goal is to improve, during the planning phase, the robustness of a complex station by fully exploiting the potential of the available capacity. The main feature of our approach is the interaction between routing decisions, timetabling and platform assignments. By altering one of these, slack can be created to allow improvements by the others as well. An objective function that maximizes the time span between any two trains is defined and the timing of the trains and the way how trains are routed to the platforms are optimized in the scope of this objective. By maximizing the spread of the trains, potential conflicts are avoided which is beneficial for – but not identical to – robustness. Using our approach, the robustness in the station zone of Brussels, Belgium’s main railway bottleneck, can be improved by 8%. Next to that, the amount of knock-on delay arising due to conflicts within this area can be halved. This performance of our approach is confirmed by a second case study based on the station zone of Antwerp.

Automated, Passenger Time Optimal, Robust Timetabling, Using Integer Programming

To design an optimal passenger train timetable one should choose a quality criterium or a combination of criteria. We consider the main quality criterium from a passenger perspective: journey time. This means that the expected time all passengers will spend when our timetable is put in practice is minimal, even taking into account typical train delays. From a train operator or rail infrastructure management company perspective, there are further concerns too, like the number of train units that has to take part in this schedule, their frequency, the number of drivers and other crew members. These factors are all related to cost to maintain the schedule but are here considered secondary and indeed, are here kept constant. We consider only the passenger criterium here. We analytically derive total stochastic expected passenger time as a closed formula, linearize it and use it as a goal function for optimizing the schedule using a mixed integer linear programming model. We applied this to all 224 current Belgian train relations, passing 550 train stations and calculated an optimal schedule in 3 hours. We believe this mathematically optimal approach is unique, in its detailed model of expected, stochastic passenger time, in its scale of implementation and in its use of actual current data from practice.

Automated platforming & routing of trains in all Belgian railway stations

We present a train platforming & routing expert system solving stations in seconds.It maximises the number of trains platformable and routable without conflicts.It also reports all conflicts existing in manual platforming plans.It is integrated in the Infrabel tool flow and works for all Belgian stations.Platforming planning is sped up & less error prone compared to current practice. Automatically generating train to platform assignments has been an active research area for some time, but systems implementing this research are still not readily available to practitioners. However, now, our train platforming model has been implemented as the tool Leopard inside Infrabel, the Belgian railway infrastructure manager. In practice, initial macroscopic timetables are often not yet feasible inside stations on the microscopic level. This means that a platforming tool must be able to handle cases where not all trains can be platformed or routed. Our model provides a platforming and routing plan for as many trains as possible and puts the remaining trains on a fictive platform. Contrary to the manually made platforming plans, the optimised platforming plans have no platform conflicts nor routing conflicts. Our model assigns as much trains as possible, given the timetable and the available infrastructure. Our tool can solve the platforming problems for all 530 stations in Belgium together in about 10 min. This means (i) it saves many man months of planning time compared to the still common manual practice to platforming and (ii) it achieves higher quality results leading to significantly less in-station train delays in practice.