Key Risk Factor Identification for Deep-Sea Transportation Safety Based on Complex Network Theory

Deep-sea transportation is faced with complex navigation environments, long voyages, limited emergency response resources, and interacting safety risks. Existing studies have mainly focused on individual risk factors, while the correlations and coupling effects among different factors have received insufficient attention. To identify the key risk factors affecting deep-sea transportation safety, this paper proposes a novel key factor identification model based on complex network theory. Firstly, 34 risk factors affecting deep-sea transportation safety are selected from five aspects using a literature analysis method. Secondly, a weighted directed network of risk factors is constructed based on complex network theory. Then, to evaluate the node importance, six node importance evaluation indicators are established, and a node importance evaluation method is proposed by integrating the analytic hierarchy process (AHP), technique for order preference by similarity to an ideal solution (TOPSIS), and gray relational analysis (GRA). Key risk factors are then determined according to the node importance evaluation results. Finally, the effectiveness of the proposed model is verified through a case study. The results show that the top five most critical risk factors are risk of leakage, emergency speed, physical and chemical properties of the cargoes, sense of personnel safety duty, and seasonal route. The findings can provide practical support for maritime authorities, shipping companies, and safety managers in formulating targeted prevention, control, and emergency response measures for deep-sea transportation safety.