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Investigating the coupling relationships of railway safety risks using the N-K model and complex network theory

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  • Jiaxu Chen
  • Lin Zhao
  • Jinghui Liu
  • Gaolei Wang
  • Zhan Guo

Abstract

To quantitatively analyze the coupling relationships between railway safety risk factors, identify key factors contributing to railway accidents, and develop scientific strategies for accident prevention, this study introduces a complex network-based N-K model to investigate the coupling relationships of railway safety risk factors. First, we identified 18 railway safety risk factors by analyzing case data from railway accidents. The occurrence probabilities and coupling values of these risk factors were then calculated using the N-K model. Subsequently, based on the constructed railway safety risk complex network, reachability and centrality analyses were performed to determine the key factors of railway safety risk. Results indicate that the occurrence of railway accidents is directly proportional to the risk coupling value; the greater the number of coupling factors, the higher the risk value. The coupling of personnel factors and equipment factors is particularly prone to leading to railway accidents. Conversely, effective management of the coupling between personnel and equipment factors can significantly reduce the likelihood of accidents. Inadequate maintenance and unsafe human behavior were identified as critical factors contributing to railway accidents and should be prioritized in prevention efforts.

Suggested Citation

  • Jiaxu Chen & Lin Zhao & Jinghui Liu & Gaolei Wang & Zhan Guo, 2025. "Investigating the coupling relationships of railway safety risks using the N-K model and complex network theory," PLOS ONE, Public Library of Science, vol. 20(9), pages 1-19, September.
    • Handle: RePEc:plo:pone00:0332948
    DOI: 10.1371/journal.pone.0332948
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    References listed on IDEAS

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    1. Wang, Yangpeng & Li, Shuxiang & Lee, Kangkuen & Tam, Hwayaw & Qu, Yuanju & Huang, Jingyin & Chu, Xianghua, 2023. "Accident risk tensor-specific covariant model for railway accident risk assessment and prediction," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
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