Abstract Accurate trajectory forecasting plays a pivotal role in various maritime applications, including route optimization, collision prevention, and intelligent traffic management. Traditional approaches, including statistical models and conventional machine learning methods, have demonstrated constrained capabilities in modeling the complex spatiotemporal characteristics of maritime trajectories. Deep learning architectures have shown remarkable potential in processing voluminous navigation data and learning sophisticated movement patterns through their hierarchical feature extraction mechanisms. This study presents an innovative deep learning framework for vessel trajectory prediction (Trans-Graph), which effectively integrates diverse features extracted from Automatic Identification System (AIS) data. The trajectories of vessels are expressed by the graphs. An architecture combing the graph neural networks (GNN) with Transformer is fabricated to process and analyze maritime data. To enhance the model’s capability in learning temporal ship position features, an auxiliary training task is implemented by using the randomly masked contextual information passing through a bi-directional LSTM network. The framework’s performance was rigorously evaluated using the AIS dataset from Copenhagen Port, Denmark, demonstrating significant advantages over existing baseline models in terms of prediction accuracy and computational efficiency.
Deng et al. (Thu,) studied this question.