Developing and operating seamless, attractive and efficient public transport services in a liberalized market requires significant coordination between involved actors, which is both an organizational and technical challenge. During a journey, passengers often transfer between different transport services. A delay of one train or a bus service can potentially cause the passenger to miss the transfer to the subsequent service. If those services are provided by different operators and those are not coordinated and the information about the services are scattered, the passengers will suffer. In order to incorporate the passenger perspective in the re-scheduling of railway traffic and associated public transport services, the passenger flow needs to be assessed and quantified. We therefore perform a survey of previous research studies that propose and apply computational re-scheduling support for railway traffic disturbance management with a passenger-oriented objective. The analysis shows that there are many different ways to represent and quantify the effects of delays on passengers, i.e.“passenger inconvenience”. In the majority of the studies, re-scheduling approaches rely on historic data on aggregated passenger flows, which are independent of how the public transport services are re-scheduled. Few studies incorporate a dynamic passenger flow model that reacts based on how the transport services are re-scheduled. None of the reviewed studies use real-time passenger flow data in the decision-making process. Good estimations of the passenger flows based on historic data are argued to be sufficient since access to large amounts of passenger flow data and accurate prediction models is available today.

One of the crucial factors in achieving a high punctuality in railway traffic systems, is the ability to effectively reschedule the trains when disturbances occur. The railway traffic rescheduling problem is a complex task to solve both from a practical and a computational perspective. Problems of practically relevant sizes have typically a very large search space, making them time-consuming to solve even for state-of-the-art optimization solvers. Though competitive algorithmic approaches are a widespread topic of research, not much research has been done to explore the opportunities and challenges in parallelizing them. This paper presents a parallel algorithm to efficiently solve the real-time railway rescheduling problem on a multi-core parallel architecture. We devised (1) an effective way to represent the solution space as a binary tree and (2) a novel sequential heuristic algorithm based on a depth-first search (DFS) strategy that quickly traverses the tree. Based on that, we designed a parallel algorithm for a multi-core architecture, which proved to be 10.5 times faster than the sequential algorithm even when run on a single processing core. When executed on a parallel machine with 8 cores, the speed further increased by a factor of 4.68 and every disturbance scenario in the considered case study was solved within 6 s. We conclude that for the problem under consideration, though a sequential DFS approach is fast in several disturbance scenarios, it is notably slower in many other disturbance scenarios. The parallel DFS approach that combines a DFS with simultaneous breadth-wise tree exploration, while being much faster on an average, is also consistently fast across all scenarios.