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Domino effect in marine accidents: Evidence from temporal association rules

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  • Wang, Likun
  • Huang, Ruiling
  • Shi, Wenming
  • Zhang, Caiyun

Abstract

Marine accidents cause not only significant economic losses, but also severe environmental pollution and inestimable human casualties, which have become a worldwide concern. To better cope with this concern, this paper adopts temporal association rules (TARs) to mine and discover the domino effect in marine accidents. Using the dataset of 5754 marine domino accidents (MDAs) collected from the International Maritime Organization and IHS Markit Company, the main findings of this paper are as follows. First, ‘hull damage’ was found to be the most frequent accident in MDAs, and ‘collision’ was more likely to cause the damage in the whole hull. Second, ‘oil spill’ was most often observed as a final marine accident. Meanwhile, ‘foundered’ was more likely to cause ‘oil spill’ in both oil tanker and general cargo ship MDAs. Third, it is pointed out that most probable scenarios involved ‘hull damage’ as the basic accident which ended with ‘foundered’ and ‘oil spill’ as top accidents. These findings not only advance our knowledge of marine accidents from the perspective of the domino effect, but also provide insights into improving marine safety.

Suggested Citation

  • Wang, Likun & Huang, Ruiling & Shi, Wenming & Zhang, Caiyun, 2021. "Domino effect in marine accidents: Evidence from temporal association rules," Transport Policy, Elsevier, vol. 103(C), pages 236-244.
  • Handle: RePEc:eee:trapol:v:103:y:2021:i:c:p:236-244
    DOI: 10.1016/j.tranpol.2021.02.006
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    References listed on IDEAS

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    1. Wang, Likun & Yang, Zaili, 2018. "Bayesian network modelling and analysis of accident severity in waterborne transportation: A case study in China," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 277-289.
    2. Sotiralis, P. & Ventikos, N.P. & Hamann, R. & Golyshev, P. & Teixeira, A.P., 2016. "Incorporation of human factors into ship collision risk models focusing on human centred design aspects," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 210-227.
    3. Hänninen, Maria & Kujala, Pentti, 2012. "Influences of variables on ship collision probability in a Bayesian belief network model," Reliability Engineering and System Safety, Elsevier, vol. 102(C), pages 27-40.
    4. Necci, Amos & Cozzani, Valerio & Spadoni, Gigliola & Khan, Faisal, 2015. "Assessment of domino effect: State of the art and research Needs," Reliability Engineering and System Safety, Elsevier, vol. 143(C), pages 3-18.
    5. Zhang, D. & Yan, X.P. & Yang, Z.L. & Wall, A. & Wang, J., 2013. "Incorporation of formal safety assessment and Bayesian network in navigational risk estimation of the Yangtze River," Reliability Engineering and System Safety, Elsevier, vol. 118(C), pages 93-105.
    6. Zhang, Yang & Sun, Xukai & Chen, Jihong & Cheng, Cheng, 2021. "Spatial patterns and characteristics of global maritime accidents," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    7. Marcelo Ramos Martins & Marcos Coelho Maturana, 2010. "Human Error Contribution in Collision and Grounding of Oil Tankers," Risk Analysis, John Wiley & Sons, vol. 30(4), pages 674-698, April.
    8. Khakzad, Nima & Reniers, Genserik & Abbassi, Rouzbeh & Khan, Faisal, 2016. "Vulnerability analysis of process plants subject to domino effects," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 127-136.
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