Vessel Destination Prediction Using a Graph-Based Machine Learning Model

As the world's population continues to expand, maritime transport is critical to ensure economic growth. To improve security and safety of maritime transportation, the Automatic Identification System (AIS) collects real-time data about vessels and their positions. While a large portion of the AIS data is provided via an automatic tracking system, some key fields, such as destination and draught, are entered manually by the ship navigator and are thus prone to errors. To support decision making in maritime industries, in this paper we propose a data-driven vessel destination prediction algorithm based on heterogeneous graph and machine learning models. We design the task as a multi-class classification problem, where the destination port is the category to be predicted given the vessel and origin information. Then, we use a link prediction model in a weighted heterogeneous graph to predict the vessel destination. Experimental comparison against baseline methods, such as logistic regression and k-nearest neighbors, showed that our model provides a robust performance, outperforming the baseline algorithms by 9% and 33% in terms of accuracy and Fl-score, respectively. Thus, heterogeneous graph models provide a powerful alternative to predict port destination, and could support enhancing AIS data quality and better decision making in maritime transportation industries.