Title: Risk factors identification and analysis for maritime accidents using complex network theory and improved Leader-Rank algorithm

Authors: Wan Su; Jing Lu; Qinran Zhang; Yijiao Liu; Yuan Ji; Hanwen Fan

Addresses: College of Transportation Engineering, Dalian Maritime University, Dalian, China ' College of Transportation Engineering, Dalian Maritime University, Dalian, China ' College of Transportation Engineering, Dalian Maritime University, Dalian, China ' College of Transportation Engineering, Dalian Maritime University, Dalian, China ' College of Transportation Engineering, Dalian Maritime University, Dalian, China ' College of Transportation Engineering, Dalian Maritime University, Dalian, China

Abstract: Risk factors identification is the first consideration that should be given to prevent maritime accidents. To assist in the effective management of maritime transportation risks, we propose a novel risk factor identification and analysis framework on the basis of complex network and Leader-Rank algorithm. By taking the maritime accidents in China as a case study, we identify 18 of 44 risk factors as critical risk factors and further reveal that risk factors network for maritime accidents is dominated by a complex interplay of human factors, environmental conditions and organisational factors. The comparison with the traditional algorithm illustrates the rationality and practicability of the presented approach. Thus, the work can offer a systematic way to interpret the risks of maritime accidents as well as provides valuable insights for developing effective preventive measures.

Keywords: risk factors identification; risk analysis; maritime accidents; complex network theory; Leader-Rank algorithm.

DOI: 10.1504/IJSTL.2025.146590

International Journal of Shipping and Transport Logistics, 2025 Vol.20 No.2, pp.245 - 270

Received: 05 Jul 2023
Accepted: 17 Dec 2023
Published online: 05 Jun 2025 *

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