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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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    9. 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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