Causation Analysis of Maritime Accidents Based on Coupled AcciMap and GCN Model

With the development of sustainable inland waterway transportation, maritime safety has become a matter of wide concern. Causation analysis of ship accidents is known as an important prerequisite for achieving waterborne transportation safety. A novel AcciMap–graph convolutional network (GCN)-based causation analysis model has been proposed for maritime traffic accidents in the main Yangtze River waterway. Mutual information, a grey wolf optimizer (GWO), a dropout layer and weakly supervised learning are introduced to obtain causal chains and the causal importance scores of accident causes. The results indicate that, compared with baseline models (e.g., GIN, GAT, APPNP, and GraphSAGE), the AcciMap–GCN model achieved the lowest values for all performance metrics, with an MAE of 0.0820, an RMSE of 0.1694, and a training convergence epoch of 74. The model robustness was comprehensively investigated through ablation experiments. Based on the causal importance scores of accident causes, maritime safety administration strategies are systematically proposed. The present study provides a novel perspective for analyzing the causal relationships of maritime accidents and shares useful insights into sustainable waterway transportation.