Modeling temporal continuity of spatial interactions for vessel trajectories prediction in maritime transportation systems

Learning the intricate spatio-temporal interactions among vessels poses a fundamental challenge to vessel trajectory prediction (VTP). Prevailing graph neural network-based methods typically employ a three-stage independent architecture, modeling spatial and temporal dependencies separately, which not only overlooks the temporal continuity of spatial interactions but also introduces higher computational overhead. Moreover, considering interaction semantics at distinct moments equally during the modeling process may restrict the effective learning for certain key features. To address these issues, our paper proposes an innovative Heterogeneous Spatio-temporal Graph Neural Network (HSTGN) that employs hierarchical aggregations, encompassing vessel node-level, temporal edge-level, and self-attention-level aggregations, tailored for VTP. Specifically, a heterogeneous graph is first constructed through the transformation of node and edge generators, allowing vessels to interact freely with each other across temporal dimensions. A key innovation is the vessel multi-relational graph aggregation module (VMGA), which selectively aggregates edge-specific semantics and assigns adaptive weights to enhance interaction modeling. Furthermore, we devise a gated temporal convolution module (GTCN) to compensate for potential global dependencies that VMGA might not capture. Finally, HSTGN integrates VMGA and GTCN in parallel to refine spatio-temporal correlations for more precise prediction. This study enhances VTP accuracy and efficiency, enabling safer navigation and optimized maritime route planning. Extensive experiments on three real-world Automatic Identification System (AIS) datasets demonstrate that HSTGN outperforms state-of-the-art approaches, achieving a 9.47% improvement in mean absolute error (MAE), 1.43% in root-mean-square error (RMSE), 2.72% in mean absolute percentage error (MAPE), and a 43.06% increase in inference speed, while utilizing fewer model parameters.