Cause analysis of ship collision accident based on complex network theory

Jian Zhou Liu; Huaiwei Zhu; Feng Tian; Tian Chai; Han Xue

doi:10.1117/12.2673745

22 May 2023 Cause analysis of ship collision accident based on complex network theory

Jian Zhou Liu, Huaiwei Zhu, Feng Tian, Tian Chai, Han Xue

Proceedings Volume 12640, International Conference on Internet of Things and Machine Learning (IoTML 2022); 1264012 (2023) https://doi.org/10.1117/12.2673745
Event: International Conference on Internet of Things and Machine Learning (IoTML 2022), 2022, Harbin, China

Abstract

To determine the relationship between various factors leading to ship collision accidents and prevent ship collision accidents. This paper collected 100 reports of ship collision accidents, and 40 causative factors were determined from the four aspects of human, ship, environment, and management. The relationship among 136 kinds of causation factors was determined by extracting the causation chain from the accident report, and the causation network model of ship collision accidents was established. Python simulation was used to analyze the robustness of the causative network under deliberate and random attacks. By comparing the robustness of the causative network under degree value attack, betweenness centrality value attack, closeness centrality value attack, and PR value attack, the key factors in the causative network were identified, and corresponding prevention strategies were proposed. The simulation results show that the robustness of the causative network is weak under the deliberate attack, the robustness of the betweenness centrality value attack is the worst, and the node with a higher betweenness centrality value is the key node in the causal network, giving priority to the prevention of key nodes is conducive to the safe navigation of ships.

Citation Download Citation

Jian Zhou Liu, Huaiwei Zhu, Feng Tian, Tian Chai, and Han Xue "Cause analysis of ship collision accident based on complex network theory", Proc. SPIE 12640, International Conference on Internet of Things and Machine Learning (IoTML 2022), 1264012 (22 May 2023); https://doi.org/10.1117/12.2673745

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available