Accident causation analysis of waterway dangerous goods transport: An approach based on improved ISM and graph theory

• Published in: Ocean engineering 2024-12, Vol.313, p.119639, Article 119639
• Main Authors: Huang, Xi, Wen, Yuanqiao, Zhang, Fan, Sui, Zhongyi, Cheng, Xiaodong, Arya, Vedpal
• Format: Article
• Language: English
• Subjects: Accident causation analysis; Graph theory; Interpretive structural modeling; Waterway dangerous goods transport
• ISSN: 0029-8018
• DOI: 10.1016/j.oceaneng.2024.119639

This study presents a new method for exploring the causal mechanisms of waterway dangerous goods transport accidents. The proposed approach combines an improved Interpretive Structural Modeling (ISM) and graph theory to construct and analyze causal networks. This method has been refined to address challenges such as reliance on expert judgment in evaluating variable relationships and increased computational complexity as the number of variables grows. Firstly, an event representation model is established to discretize the accident into distinct events. Then, internal attribute influence mechanisms are defined within a network framework to identify event-attribute relationships. The reachability matrix of attribute relationships is calculated using the ISM process. Subsequently, a multi-layer causal network is constructed by integrating tiered results and graph theory, followed by topological and robustness analyses to clarify the causal mechanisms of internal attributes. Finally, the method is validated through a public accident case from the China Maritime Safety Administration, and the analysis results were corroborated with the accident investigation findings, providing comprehensive and in-depth theoretical explanation and support for the investigation outcomes. •Developed a model to identify and analyze the complex internal mechanisms that drive event occurrences.•Applied hierarchical clustering algorithms to streamline and simplify matrix calculations in traditional ISM models.•Integrated ISM and graph theory to visualize causation in waterway dangerous goods transport accidents.