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Erschienen in: Neural Computing and Applications 7/2024

06.12.2023 | Original Article

A train dispatching model in case of segment blockages by integrating the prediction of delay propagation

verfasst von: Han Yang, Wenfeng Hu, Shan Ma, Tao Peng

Erschienen in: Neural Computing and Applications | Ausgabe 7/2024

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Abstract

In the high-speed railway system, trains’ original timetable is often disturbed by some emergencies including geological disasters and equipment failures, which brings great influence to passengers. This paper proposes a real-time high-speed train dispatching model in case of segment blockages, where a railway network is considered. The model includes the following two parts. First, if the trains are not cancelled or decelerated after a blockage occurs, the scope of the affected trains and stations is roughly estimated via the prediction of delay propagation model. Second, with the overall delay as the objective function, this paper constructs a mixed integer nonlinear programming (MINLP) model by considering the following three adjustment strategies: cancellation, delayed departure and deceleration, where the safe headway of the train operation is guaranteed by the moving blocking principle. Furthermore, to reduce the computation complexity, the solution of the model is only considered within the scope obtained in the first stage. The model is verified by using a small railway network with Nanjing as the hub station, which shows that the model is useful for reducing the effect of a disruption on original timetable, especially in comparison with the First Scheduled First Served (FSFS) rule used in practice.

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Metadaten
Titel
A train dispatching model in case of segment blockages by integrating the prediction of delay propagation
verfasst von
Han Yang
Wenfeng Hu
Shan Ma
Tao Peng
Publikationsdatum
06.12.2023
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 7/2024
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-023-09243-z

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