Constructing and analyzing the causation chain network for ship collision accidents

Tian Chai (), Huaiwei Zhu (), Liyang Peng (), Junya Wang (), Zhengping Fan (), Shixiao Xiao (), Jiarong Xie and Yanqing Hu ()
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Tian Chai: College of Navigation, Jimei University, Xiamen 361021, P. R. China
Huaiwei Zhu: College of Navigation, Jimei University, Xiamen 361021, P. R. China
Liyang Peng: School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou 510006, P. R. China
Junya Wang: School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou 510006, P. R. China
Zhengping Fan: School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou 510006, P. R. China
Shixiao Xiao: College of Chengyi, Jimei University, Xiamen 361021, P. R. China
Jiarong Xie: School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou 510006, P. R. China
Yanqing Hu: School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou 510006, P. R. China

International Journal of Modern Physics C (IJMPC), 2022, vol. 33, issue 09, 1-12

Abstract: Many factors can lead to ship collision accidents, and identifying the importance of these factors is crucial for marine safety management and risk assessment. Are there rooted factors that are responsible for the presence of other factors? Notably, it is hard to analyze the causal relationship between these factors, due to the diversity and the interwoven of factors, especially when the size of the accident sample is small. To address this problem, an accident-cause network and a cause-inducing chain network are constructed in this paper by extracting 98 causes from 300 ship collision accidents. Our results show that there are six hidden rooted causes, i.e. the captain did not give any orders of night, the navigation alarm on the bridge was not on, communication failure between the officer on duty and the sailor on duty, no vessel traffic service was established, the shipping company does not fully grasp the navigation management regulations which is important to ship safety, and defects in bridge resource management, responsible for the presence of others among 98 causes. Our findings may shed some new lights on how to avoid ship collision accidents.

Keywords: Complex networks; data mining; risk assessment; complex system; causal inference (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0129183122501182

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