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A knowledge graph-based approach for exploring railway operational accidents

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  • Liu, Jintao
  • Schmid, Felix
  • Li, Keping
  • Zheng, Wei

Abstract

Drawing lessons from past accidents is an essential way to improve the operational safety of railways. Various railway operational accidents and their related hazards constitute a causation network due to the interactions among the hazards. Some useful lessons can be captured from such a network. In this paper, a new knowledge graph-based approach to explore railway operational accidents is proposed, aiming to reveal the potential rules of accidents by depicting accidents and hazards in a heterogeneous network. This work serves as an extension and complement to classical homogeneous network-based accident analyses. Its originality is to apply the knowledge graph theory to railway operational accident analysis, by means of some topological indicators adapting to the heterogeneous structural features of knowledge graphs. To facilitate the construction of the accident knowledge graph, a modelling method is developed. The outcomes of the knowledge graph-based analysis provide railway operators with the decision-making basis for the investment of accident prevention efforts. An application on real railway operational accidents in the UK is presented. The results show the effectiveness of the proposed approach in terms of discovering the latent features of the corresponding railway operational accidents and assisting in formulating targeted preventive measures.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:207:y:2021:i:c:s0951832020308437
    DOI: 10.1016/j.ress.2020.107352
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    References listed on IDEAS

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    Cited by:

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    3. Yan, Dongyang & Li, Keping & Zhu, Qiaozhen & Liu, Yanyan, 2023. "A railway accident prevention method based on reinforcement learning – Active preventive strategy by multi-modal data," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
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    6. 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).
    7. Valcamonico, Dario & Baraldi, Piero & Zio, Enrico & Decarli, Luca & Crivellari, Anna & Rosa, Laura La, 2024. "Combining natural language processing and bayesian networks for the probabilistic estimation of the severity of process safety events in hydrocarbon production assets," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    8. Singh, Prashant & Pasha, Junayed & Moses, Ren & Sobanjo, John & Ozguven, Eren E. & Dulebenets, Maxim A., 2022. "Development of exact and heuristic optimization methods for safety improvement projects at level crossings under conflicting objectives," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    9. Mei Liu & Boning Li & Hongjun Cui & Pin-Chao Liao & Yuecheng Huang, 2022. "Research Paradigm of Network Approaches in Construction Safety and Occupational Health," IJERPH, MDPI, vol. 19(19), pages 1-22, September.
    10. Xia, Liqiao & Liang, Yongshi & Leng, Jiewu & Zheng, Pai, 2023. "Maintenance planning recommendation of complex industrial equipment based on knowledge graph and graph neural network," Reliability Engineering and System Safety, Elsevier, vol. 232(C).

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