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A method for modeling and analysis of directed weighted accident causation network (DWACN)

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  • Zhou, Jin
  • Xu, Weixiang
  • Guo, Xin
  • Ding, Jing

Abstract

Using complex network theory to analyze accidents is effective to understand the causes of accidents in complex systems. In this paper, a novel method is proposed to establish directed weighted accident causation network (DWACN) for the Rail Accident Investigation Branch (RAIB) in the UK, which is based on complex network and using event chains of accidents. DWACN is composed of 109 nodes which denote causal factors and 260 directed weighted edges which represent complex interrelationships among factors. The statistical properties of directed weighted complex network are applied to reveal the critical factors, the key event chains and the important classes in DWACN. Analysis results demonstrate that DWACN has characteristics of small-world networks with short average path length and high weighted clustering coefficient, and display the properties of scale-free networks captured by that the cumulative degree distribution follows an exponential function. This modeling and analysis method can assist us to discover the latent rules of accidents and feature of faults propagation to reduce accidents. This paper is further development on the research of accident analysis methods using complex network.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:437:y:2015:i:c:p:263-277
    DOI: 10.1016/j.physa.2015.05.112
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    Cited by:

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    2. Suo Qi & Wang Liyuan & Yao Tianzi & Wang Zihao, 2021. "Promoting Metro Operation Safety by Exploring Metro Operation Accident Network," Journal of Systems Science and Information, De Gruyter, vol. 9(4), pages 455-468, August.
    3. 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).
    4. 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.
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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. Wang, Wenhao & Wang, Yanhui & Wang, Guangxing & Li, Man & Jia, Limin, 2023. "Identification of the critical accident causative factors in the urban rail transit system by complex network theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 610(C).
    8. Guo, Shengyu & Zhou, Xinyu & Tang, Bing & Gong, Peisong, 2020. "Exploring the behavioral risk chains of accidents using complex network theory in the construction industry," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
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    10. Zhou, Jin & Xu, Weixiang & Guo, Xin & Liu, Xumin, 2017. "A hierarchical network modeling method for railway tunnels safety assessment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 226-239.

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