This paper investigates the challenges of railway traffic controllers in dealing with big disruptions and the kind of support tools that could help to improve their task in terms of performance, lead time and workload. The disruption handling process can be partitioned into three phases resembling a bathtub. For each phase the essential decision making process has been identified. Currently, the support to rail traffic controllers in case of severe disruptions is limited to predefined contingency plans that are not always feasible or applicable. In the literature, models and algorithms have been identified that could be used in the different parts of the three phases of the disruption handling process. This paper investigates the processes of disruption management in practice and the challenges that traffic controllers are facing during a disruption. The literature of models applicable to disruption management is reviewed and classified based on the three phases of the traffic state during disruptions. Finally, a rescheduling optimization model is applied to a case of complete blockage on a corridor of the Dutch railway network. The case study shows how a microscopic model could support the traffic controllers by providing real-time solutions for different phases of a disruption.

A B S T R A C TDisruptions such as rolling stock breakdown, signal failures, and accidents are recurrent events during daily railway operation. Such events disrupt the deployment of resources and cause delay to passengers. Obtaining a reliable disruption length estimation can potentially reduce the negative impact caused by the disruption. Different factors such as the location, cause of disruption, traffic density, etc. can determine the disruption length. The uncertainty inherent to the variability of each factor and the unavailability of sufficient data results in a wide distribution of disruption lengths from which a certain value should be selected as the length prediction. The rescheduling measure considered in this research is short-turning the trains that are heading to the disrupted area. To investigate the impact of the disruption length estimates on the rescheduling strategy and the resulting passengers delays, this research presents a framework consisting of three models: a disruption length model, short-turning model and passenger assignment model. The framework is applied to a part of the Dutch railway network. The results show the effects of short (optimistic) and long (pessimistic) estimates on the number of affected passengers, generalized travel time and number of passengers rerouting and transferring.

Currently railway traffic controllers use predefined solutions (contingency plans) to deal with a disruption. These plans are manually designed by expert traffic controllers and are specific to a certain location and timetable. With a slight change in the timetable or infrastructure, these plans might not be feasible and have to be updated. Instead traffic controllers can benefit from algorithms that can quickly compute an optimal solution given a disruption specification. This paper presents a Mixed Integer Linear Programming model to compute a disruption timetable when there is a complete blockage and no train can use part of the track for several hours. The model computes the optimal shortturning stations, routes and platform tracks. In this approach short-turning as a common practice in case of complete blockages is modelled at a microscopic level of operational and infrastructural detail to guarantee feasibility of the solution. To demonstrate the functionality and applicability of the model two case studies are performed on two Dutch railway corridors. In the first case, four experiments are presented to show how different priorities can change the optimal solution including the order of services and the choice of short-turning station. In the second case the performance of the model on a big station is investigated. It is shown that the model can compute the optimal solution in a short time.

In case of railway disruptions, traffic controllers are responsible for dealing with disrupted traffic and reduce the negative impact for the rest of the network. In case of a complete blockage when no train can use an entire track, a common practice is to short-turn trains. Trains approaching the blockage cannot proceed according to their original plans and have to short-turn at a station close to the disruption on both sides. This paper presents a Mixed Integer Linear Program that computes the optimal station and times for short-turning the affected train services during the three phases of a disruption. The proposed solution approach takes into account the interaction of the traffic between both sides of the blockage before and after the disruption. The model is applied to a busy corridor of the Dutch railway network. The computation time meets the real-time solution requirement. The case study gives insight into the importance of the disruption period in computing the optimal solution. It is concluded that different optimal short-turning solutions may exist depending on the start time of the disruption and the disruption length. For periodic timetables, the optimal short-turning choices repeat due to the periodicity of the timetable. In addition, it is observed that a minor extension of the disruption length may result in less delay propagation at the cost of more cancellations.

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