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Understanding railway operational accidents using network theory

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  • Liu, Jintao
  • Schmid, Felix
  • Zheng, Wei
  • Zhu, Jiebei

Abstract

Learning from past accidents in railway operations is valuable for ensuring the future safety of railway operations. Railway operational accidents are of different types, such as collisions and derailments. Different types of railway operational accidents are related to each other due to the interactions between hazards leading to accidents. It is useful to explore the nature of accidents as a set. In this paper, a new network theory-based approach to understanding railway operational accidents is proposed, which aims to reveal latent patterns of hazards from an overall high-level perspective. This approach serves as a complement to conventional network theory-based analyses. Its originality is in the customization of a topological analysis for studying accidents, with several tailored indicators adapting to the characteristics of railway operational accidents. It also provides a practical way to extract and construct the accident causation network from numerous accident investigation reports. The outcomes of this approach could assist railway operators in formulating more targeted accident prevention strategies and approaches. The method has been applied to real railway operational accidents in the UK. The results show that the proposed approach is effective and practical in terms of capturing important causes of accidents and revealing latent rules of railway operational accidents.

Suggested Citation

  • Liu, Jintao & Schmid, Felix & Zheng, Wei & Zhu, Jiebei, 2019. "Understanding railway operational accidents using network theory," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 218-231.
    • Handle: RePEc:eee:reensy:v:189:y:2019:i:c:p:218-231
    DOI: 10.1016/j.ress.2019.04.030
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    References listed on IDEAS

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    5. Catelani, Marcantonio & Ciani, Lorenzo & Guidi, Giulia & Patrizi, Gabriele, 2021. "An enhanced SHERPA (E-SHERPA) method for human reliability analysis in railway engineering," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    6. Liu, Jintao & Schmid, Felix & Li, Keping & Zheng, Wei, 2021. "A knowledge graph-based approach for exploring railway operational accidents," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    7. Zhang, Hengqi & Geng, Hua & Zeng, Huarong & Jiang, Li, 2023. "Dynamic risk evaluation and control of electrical personal accidents," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    8. Cuiping Ren & Bianbian Chen & Fengjie Xie & Xuan Zhao & Jiaqian Zhang & Xueyan Zhou, 2022. "Understanding Hazardous Materials Transportation Accidents Based on Higher-Order Network Theory," IJERPH, MDPI, vol. 19(20), pages 1-13, October.
    9. Bhardwaj, U. & Teixeira, A.P. & Guedes Soares, C., 2022. "Casualty analysis methodology and taxonomy for FPSO accident analysis," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    10. Lam, C.Y. & Tai, K., 2020. "Network topological approach to modeling accident causations and characteristics: Analysis of railway incidents in Japan," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    11. Zhang, Hengqi & Geng, Hua, 2023. "A methodology to identify and assess high-risk causes for electrical personal accidents based on directed weighted CN," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    12. Liu, Jintao & Chen, Keyi & Duan, Huayu & Li, Chenling, 2024. "A knowledge graph-based hazard prediction approach for preventing railway operational accidents," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
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    14. Liu, Yanyan & Li, Keping & Yan, Dongyang, 2024. "Quantification analysis of potential risk in railway accidents: A new random walk based approach," Reliability Engineering and System Safety, Elsevier, vol. 242(C).

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