MSRA-Net: A Novel Multi-Ship Risk Attribution Network for Collision-Risk Prediction

As vessel traffic becomes increasingly dense in inland waterways, accurate prediction and interpretation of multi-ship collision risk are important for navigational safety and intelligent maritime supervision. However, existing methods mainly focus on pairwise risk assessment or scenario-level prediction, while providing limited insight into which vessels and interactions contribute to predicted future risk. To address these limitations, this study proposes a Multi-Ship Risk Attribution Network (MSRA-Net) for interpretable short-term collision-risk prediction from historical Automatic Identification System (AIS) data. The proposed framework integrates three key components: a Dynamic Multi-Ship Interaction Graph (DMIG) to represent time-varying vessel states and encounter relationships, a Spatio-Temporal Risk Prediction Network (STRP-Net) to capture temporal vessel dynamics and inter-vessel interactions, and a Multi-Ship Risk Attribution (MSRA) module to quantify vessel-level and interaction-level contributions to the predicted risk. The performance of MSRA-Net is evaluated using 6,433,925 AIS observations collected from a representative section of the Yangtze River in Wuhan, China. Experimental results show that MSRA-Net achieves the lowest average MAE of 0.0493 across 1, 3, and 5 min prediction horizons and obtains an MAE of 0.0632, RMSE of 0.0740, and R2 of 0.862 at the 5 min horizon. Progressive vessel-deletion experiments further demonstrate attribution fidelity, with high-to-low deletion achieving a DelAUC of 0.1730, compared with 0.3016 for random deletion and 0.4384 for low-to-high deletion. Controlled AIS perturbation experiments also show that the learned attribution patterns remain relatively stable under small variations in vessel states. Overall, MSRA-Net provides a unified framework for dynamic multi-ship interaction modeling, future collision-risk prediction, and model-level risk attribution, offering an interpretable approach for maritime traffic safety analysis.