Latest revision as of 10:47, 15 February 2021

Abstract

Timely predicting and controlling the traffic congestion in a station caused by an emergency is an important task in railway emergency management. However, traffic forecasting in an emergency is subject to a dynamic service network, with uncertainty surrounding elements such as the capacity of the transport network, schedules, and plans. Accurate traffic forecasting is difficult. This paper proposes a practical time&ndash

space network model based on a train diagram for predicting and controlling the traffic congestion in a station caused by an emergency. Based on the train diagram, we constructed a symmetric time&ndash

space network for the first time by considering the transition of the railcar state. On this basis, an improved A* algorithm based on the railcar flow route was proposed to generate feasible path sets and a dynamic railcar flow distribution model was built to simulate the railcar flow distribution process in an emergency. In our numerical studies, these output results of our proposed model can be used to control traffic congestion.

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The different versions of the original document can be found in:

• http://dx.doi.org/10.3390/sym11060780 under the license https://creativecommons.org/licenses/by

• http://dx.doi.org/10.3390/sym11060780 under the license http://creativecommons.org/licenses/by/3.0/

• https://www.mdpi.com/2073-8994/11/6/780/pdf under the license https://creativecommons.org/licenses/by/4.0

• https://www.mdpi.com/2073-8994/11/6/780,

https://dblp.uni-trier.de/db/journals/symmetry/symmetry11.html#QuH19,

https://www.mdpi.com/2073-8994/11/6/780/pdf,

https://doi.org/10.3390/sym11060780,

https://academic.microsoft.com/#/detail/2950009606 under the license cc-by

• https://www.mdpi.com/2073-8994/11/6/780,

https://doaj.org/toc/2073-8994

• https://www.mdpi.com/2073-8994/11/6/780/pdf,

http://dx.doi.org/10.3390/sym11060780

under the license https://creativecommons.org/licenses/by/4.0/