An improved network model for railway traffic

Author:

Li Keping1,Ma Xin1,Shao Fubo1

Affiliation:

1. State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, P. R. China

Abstract

In railway traffic, safety analysis is a key issue for controlling train operation. Here, the identification and order of key factors are very important. In this paper, a new network model is constructed for analyzing the railway safety, in which nodes are regarded as causation factors and links represent possible relationships among those factors. Our aim is to give all these nodes an importance order, and to find the in-depth relationship among these nodes including how failures spread among them. Based on the constructed network model, we propose a control method to ensure the safe state by setting each node a threshold. As the results, by protecting the Hub node of the constructed network, the spreading of railway accident can be controlled well. The efficiency of such a method is further tested with the help of numerical example.

Publisher

World Scientific Pub Co Pte Lt

Subject

Computational Theory and Mathematics,Computer Science Applications,General Physics and Astronomy,Mathematical Physics,Statistical and Nonlinear Physics

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A propagation model of ship collision causation based on complex network dynamics theory;Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023);2024-02-20

2. Railway accident causation analysis: Current approaches, challenges and potential solutions;Accident Analysis & Prevention;2023-06

3. A methodology to identify and assess high-risk causes for electrical personal accidents based on directed weighted CN;Reliability Engineering & System Safety;2023-03

4. An importance order analysis method for causes of railway signaling system hazards based on complex networks;Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability;2018-10-24

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