Demo: Vessel trajectory prediction using sequence-to-sequence models over spatial grid

In this paper, we propose a neural network based system to predict vessels' trajectories including the destination port and estimated arrival time. The system is designed to address DEBS Grand Challenge 2018, which provides a set of data streams containing vessel information and coordinates ordered by time. Our goal is to design a system which can accurately predict future trajectories, destination port and arrival time for a vessel. Our solution is based on the sequence-to-sequence model which uses a spatial grid for trajectory prediction. We divided sea area into a spatial grid and then used vessels' recent trajectory as a sequence of codes to extract movement tendency. The extracted movement tendency allowed us to predict future movements till the destination. We built our solution using distributed architecture model and applied load balancing techniques to achieve maximum performance and scalability. We also design an interactive user interface which showcases real-time trajectories of vessels including their predicted destination and arrival time.