  This paper addresses the challenge of boosting the precision of multi-path
long-term vessel trajectory forecasting on engineered sequences of Automatic
Identification System (AIS) data using feature fusion for problem shifting. We
have developed a deep auto-encoder model and a phased framework approach to
predict the next 12 hours of vessel trajectories using 1 to 3 hours of AIS data
as input. To this end, we fuse the spatiotemporal features from the AIS
messages with probabilistic features engineered from historical AIS data
referring to potential routes and destinations. As a result, we reduce the
forecasting uncertainty by shifting the problem into a trajectory
reconstruction problem. 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.
