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Risk state changes analysis of railway dangerous goods transportation system: Based on the cusp catastrophe model

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  • Huang, Wencheng
  • Zhang, Rui
  • Xu, Minhao
  • Yu, Yaocheng
  • Xu, Yifei
  • De Dieu, Gatesi Jean

Abstract

In this paper, the cusp catastrophe model is applied to analyze the railway dangerous goods transportation system risk state changes. Firstly, a Risk-Accident Catastrophe Tree is proposed to formulate the process that how the risk factors cause the accident. Secondly, the whole risk factors are classified into five categories including failure of human behavior, machine failure, transported materials, environment problems and management problems. Next, the cusp catastrophe model of railway dangerous goods transportation system is established, the Split Coefficient is defined to evaluate bifurcation set. The results of the case study show that the risk state changes of railway dangerous goods transportation system satisfy bimodality, inaccessibility, sudden transitions and divergence, but not satisfy hysteresis. The larger the Split Coefficient is, the easier the trajectory of system control point crosses with bifurcation sets. The reason of the system state changes from a safe state to a risk state is: as long as the trajectory of system control point crosses with bifurcation sets, there must be a cusp catastrophe in the system, and the system control point will cross the fold surface, which makes the risk energy increase sharply, the structure, information and energy of the system will also be destroyed.

Suggested Citation

  • Huang, Wencheng & Zhang, Rui & Xu, Minhao & Yu, Yaocheng & Xu, Yifei & De Dieu, Gatesi Jean, 2020. "Risk state changes analysis of railway dangerous goods transportation system: Based on the cusp catastrophe model," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    • Handle: RePEc:eee:reensy:v:202:y:2020:i:c:s0951832020305603
    DOI: 10.1016/j.ress.2020.107059
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    1. Kyriakidis, Miltos & Majumdar, Arnab & Ochieng, Washington Y., 2018. "The human performance railway operational index—a novel approach to assess human performance for railway operations," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 226-243.
    2. 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).
    3. Acha-Daza, Jorge A. & Hall, Fred L., 1994. "Application of catastrophe theory to traffic flow variables," Transportation Research Part B: Methodological, Elsevier, vol. 28(3), pages 235-250, June.
    4. Dou, Wenyu & Ghose, Sanjoy, 2006. "A dynamic nonlinear model of online retail competition using Cusp Catastrophe Theory," Journal of Business Research, Elsevier, vol. 59(7), pages 838-848, July.
    5. Diks, Cees & Wang, Juanxi, 2016. "Can a stochastic cusp catastrophe model explain housing market crashes?," Journal of Economic Dynamics and Control, Elsevier, vol. 69(C), pages 68-88.
    6. 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.
    7. Jun Li & Hong Zhang & Yinshan Han & Baodong Wang, 2016. "Study on Failure of Third-Party Damage for Urban Gas Pipeline Based on Fuzzy Comprehensive Evaluation," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-15, November.
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