Vessel Destination and Arrival Time Prediction with Sequence-to-Sequence Models over Spatial Grid

We propose a sequence-to-sequence based method to predict vessels' destination port and estimated arrival time. We consider this problem as an extension of trajectory prediction problem, that takes a sequence of historical locations as input and returns a sequence of future locations, which is used to determine arrival port and estimated arrival time. Our solution first represents the trajectories on a spatial grid covering Mediterranean Sea. Then, we train a sequence-to-sequence model to predict the future movement of vessels based on movement tendency and current location. We built our solution using distributed architecture model and applied load balancing techniques to achieve both high performance and scalability.