Risk Oriented Dispatching of Railway Operation under the Consideration of Random Disturbances in Dynamic Circumstances

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With the increasing traffic demand and limited infrastructure expansion, railway networks are often operated close to the full capacity, especially in heavily used areas. As a result, the basic timetable is quite susceptible to the operational disturbances, and thereby the propagation and accumulation of delays significantly degrade the service level for customers. To solve this problem, extensive researches have been conducted by focusing on the predefined robust timetables and the real time dispatching algorithm development. However, it has been widely recognized that excessive robust timetables may deteriorate the operating capacity of the railway network and the addition of recovery time and buffer time can be hardly implemented in the congested area. Moreover, most of the conventional dispatching algorithms ignore the further potential random disturbances during the dispatching process, which yield non-implementable dispatching solutions and, as consequences, inferior punctuality and repetitive dispatching actions. To this end, this project aims to develop a new algorithm for real-world dispatching process with the consideration of risk-oriented random disturbances in dynamic circumstances. In the procedure of this project, an operational risk map will be firstly produced: by simulating considerable amount of timetables with random disturbances in a Monte Carlo scheme and calculating the corresponding total weighted waiting time, different levels of risk will be assigned to each block section in the studied railway network. Within a rolling time horizon framework, conflicts are detected with the inclusion of risk-oriented random disturbances in each block, and the near optimal dispatching solutions are calculated by using Tabu search algorithm. Finally, validation experiments will be conducted in a demonstrator using the available software PULEIV to evaluate the effectiveness and advantage of the proposed dispatching algorithm by three related metrics: number of relative reordering, total weighted waiting time and capacity. The proposed algorithms are expected to be capable of automatically producing near-optimal and robust dispatching solutions with sufficient punctuality and capacity achieved.