Multi-path long-term vessel trajectories forecasting with probabilistic feature fusion for problem shifting

This paper presents a deep auto-encoder model and a phased framework approach to predict the next 12 h of vessel trajectories using 1 to 3 h of Automatic Identification System data as input. The strategy involves fusing spatiotemporal features from AIS messages with probabilistic features engineered from historical AIS data to reduce forecasting uncertainty. The probabilistic features have an F1-Score of approximately 85% and 75% for the vessel route and destination prediction, respectively. Under such circumstances, we achieved an R2 Score of over 98% with different layer structures and varying feature combinations; the high R2 Score is a natural outcome of the well-defined shipping lanes in the study region. However, our proposal stands out among competing approaches as it demonstrates the capability of complex decision-making during turnings and route selection. Furthermore, we have shown that our model achieves more accurate forecasting with average and median errors of 11km and 6km, respectively, a 25% improvement from the current state-of-the-art approaches. The resulting model from this proposal is deployed as part of a broader Decision Support System to safeguard whales by preventing the risk of vessel-whale collisions under the smartWhales initiative and acting on the Gulf of St. Lawrence in Atlantic Canada. • Probabilistic feature augmentation for deriving trajectory route and destination. • Conditional probability model for spatial feature distillation from AIS data streams. • Feature fusion and augmentation for problem shifting into trajectory reconstruction. • AutoEncoder designed for faster trajectory reconstruction with fewer parameters. • Module of a Decision Support System that avoids vessel-whale collisions in Canada.