Vessel trajectory prediction in harbors: A deep learning approach with maritime-based data preprocessing and Berthing Side Integration

This study proposes a novel deep learning approach for vessel trajectory prediction in harbor environments, emphasizing the integration of berthing side information. Using Automatic Identification System (AIS) data from Busan Port, we compare Bidirectional Long Short-Term Memory (Bi-LSTM), Bidirectional Gated Recurrent Unit (Bi-GRU), and Transformer models. We identify the berthing side (port or starboard) as a crucial factor that influences vessel trajectories during berth approach, revealing significant differences in final approach paths. Port-side berthing vessels typically take the shortest course from the fairway endpoint, while starboard-side vessels approach parallel to the berth before final berthing. We employed advanced data preprocessing techniques, including DBSCAN clustering, cubic spline interpolation, and Gaussian noise-based data augmentation. Models were trained and evaluated on datasets with and without berthing side information. The Bi-LSTM model outperformed others in most scenarios, improving prediction accuracy by up to 23% when using berthing sidespecific datasets. Paired t-tests confirmed statistically significant performance improvements for the Bi-LSTM model. This research enhances maritime safety and efficiency by providing more accurate trajectory predictions, enabling Vessel Traffic Services Operators (VTSOs) to respond to potential hazards swiftly and assisting pilots in developing safer berthing plans.