A Spatio-Temporal Hybrid Multi-Head Attention Model for AIS-Based Ship Trajectory Prediction

To improve ship AIS trajectory prediction under pronounced spatiotemporal coupling and dynamic maneuvering conditions, this study proposes a Spatio-Temporal-Hybrid-Multi-head Attention model (STHA) integrating multiscale convolution, bidirectional long short-term memory, and multi-head attention. Historical AIS data from the Zhoushan waters in 2024 were preprocessed through screening, cleaning, outlier removal, resampling, and cubic spline interpolation to construct trajectory samples. Comparative experiments were conducted against BP, BiLSTM, and BiGRU using MAPE, RMSE, and R2 as evaluation metrics. The results show that STHA achieves the best overall predictive performance, more accurately follows trajectory variations across different vessel types, and exhibits better robustness in scenarios involving turning and speed changes. These findings indicate that the proposed model is effective for high-precision ship trajectory prediction and can provide useful support for subsequent collision risk assessment and navigation safety assistance.