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An importance order analysis method for causes of railway signaling system hazards based on complex networks

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  • Jintao Liu
  • Keping Li
  • Wei Zheng
  • Jiebei Zhu

Abstract

The importance order of causes of a railway signaling system hazard is important for the arrangement of control measures for eliminating or controlling the system hazard. In order to obtain a reasonable importance order, a novel importance order analysis method based on complex networks is proposed in this article. In this article, to characterize the causes from a topological perspective, a comprehensive topology characteristic is proposed. It is built on the top of three typical topology characteristics with the weighted Euclidean distance. Then, an influence coefficient matrix is proposed to measure the causal influence between causes. Based on the proposed topology characteristic and matrix, an algorithm is designed to identify the importance order of causes. To verify the effectiveness of the proposed method, a case study on a Chinese high-speed railway signaling system hazard is performed. The results show that the proposed method is feasible for the arrangement of control measures to eliminate or control system hazards and performs better than the traditional complex network-based methods in terms of avoiding conflicting and rough results.

Suggested Citation

  • Jintao Liu & Keping Li & Wei Zheng & Jiebei Zhu, 2019. "An importance order analysis method for causes of railway signaling system hazards based on complex networks," Journal of Risk and Reliability, , vol. 233(4), pages 567-579, August.
    • Handle: RePEc:sae:risrel:v:233:y:2019:i:4:p:567-579
    DOI: 10.1177/1748006X18807785
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    References listed on IDEAS

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    4. Vaurio, Jussi K., 2010. "Ideas and developments in importance measures and fault-tree techniques for reliability and risk analysis," Reliability Engineering and System Safety, Elsevier, vol. 95(2), pages 99-107.
    5. Zhou, Jin & Xu, Weixiang & Guo, Xin & Ding, Jing, 2015. "A method for modeling and analysis of directed weighted accident causation network (DWACN)," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 437(C), pages 263-277.
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    Cited by:

    1. Zhou, Jian-Lan & Lei, Yi, 2020. "A slim integrated with empirical study and network analysis for human error assessment in the railway driving process," Reliability Engineering and System Safety, Elsevier, vol. 204(C).

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