Abstract: In case of railway disruption, traffic controllers are faced with the challenge of reducing delay propagation. One of the applied measures is to short-turn trains. Currently, predefined plans are used to manage traffic in case of disruption. These manually designed plans may not cover all possible disruption scenarios, and the solutions depend on the experience of the designer and might not be optimal. Also, some of these plans are infeasible because they disregard the time required for short-turning. This paper introduces a macroscopic rescheduling model formulated as a Mixed Integer Linear Programme that aims to support traffic controllers in case of complete blockages. The model computes the optimal short-turning pattern, which indicates the arrival train, departure service and platform track taking into account the short-turning time. In addition to the optimal short-turning pattern, the main contribution of the model is that it provides the possibility of short-turning at two stations instead of one. The model is applied on a part of Dutch railway network. The results show that the delay can be decreased by including two short-turning stations at the expense of more service cancellations. The traffic controllers should decide what is the best trade-off between cancelling and delaying services.@en

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.@en

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 short-turning 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.@en

Disruptions 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.@en

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.@en

Railway operations such as any other public transport modes, follows a timetable. However, unplanned events can disrupt the operation. In such occasions the traffic controllers have to deal with the disrupted traffic. Due to the complexity of coordinating the timetable, rolling stock, and crew, the traffic controllers face difficulties handling the traffic. With the advancement of digitalization and computational power, rescheduling algorithms can provide support to the traffic controllers. This thesis aims at providing support to the traffic controllers through rescheduling algorithms in case of complete blockages. A common practice in railway operations in case of complete blockage is to short-turn train services that are heading towards a blockage. This measure assigns the services that are approaching a station before the blockage to the scheduled services in the opposite direction. In this way, the disrupted area can be better isolated from the rest of the network. The main objective of this thesis is to optimize the short-turnings of the train services. @en