Macroscopic multiple-station short-turning model in case of complete railway blockages - Ghaemi, Nadjla; Cats, Oded; Goverde, Rob M.P. (Transportation research. Part C, Emerging technologies, 2018)
•A macroscopic MILP model is developed to compute the disruption timetable and the transition plans given a certain disruption period.•Allowing for short-turning at multiple stations while taking the platform track occupation into consideration.•Accounting for the traffic on both sides of the disrupted location during the three phases.•Demonstrating how the disruption length affects the optimal periodic short-turning solution of the second phase. 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.