Modeling the delay propagation dynamics in high-speed railway networks with a traffic-driven SIS epidemic model

Kai Sheng, Shuangyin Feng, Cunlai Pu and Shuxin Ding
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Kai Sheng: Signal and Communication Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing 100081, P. R. China2Traffic Management Laboratory for High-Speed Railway, National Engineering Research Center of System Technology for High-Speed Railway and Urban Rail Transit, Beijing 100081, P. R. China
Shuangyin Feng: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, P. R. China
Cunlai Pu: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, P. R. China
Shuxin Ding: Signal and Communication Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing 100081, P. R. China2Traffic Management Laboratory for High-Speed Railway, National Engineering Research Center of System Technology for High-Speed Railway and Urban Rail Transit, Beijing 100081, P. R. China

International Journal of Modern Physics C (IJMPC), 2025, vol. 36, issue 07, 1-14

Abstract: The propagation of delays is a prevalent issue in high-speed railway (HSR) networks. However, the underlying properties of delay propagation have not been fully explored, hindering the effective management of this problem. In this paper, considering the impact of traffic flow, we model the delay propagation as a traffic-driven susceptible–infected–susceptible (SIS) epidemic spreading process. We introduce a traffic frequency matrix to estimate the traffic rate and further derive the propagation dynamic equations using a continuous-time Markov chain method. We then derive the epidemic threshold of the model, which is proportional to the recovery rate and inversely proportional to the average path length. Finally, through simulation, we study the impacts of various factors including the infection rate, recovery rate, and the degree and number of initial delay stations. Our work provides some insights for the prediction and control of delay propagation in HSR networks.

Keywords: High-speed railway networks; delay propagation; complex networks; epidemic spreading (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0129183124502486

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