  Vessel navigation is influenced by various factors, such as dynamic
environmental factors that change over time or static features such as vessel
type or depth of the ocean. These dynamic and static navigational factors
impose limitations on vessels, such as long waiting times in regions outside
the actual ports, and we call these waiting regions gateway ports. Identifying
gateway ports and their associated features such as congestion and available
utilities can enhance vessel navigation by planning on fuel optimization or
saving time in cargo operation. In this paper, we propose a novel temporal
graph neural network (TGNN) based port classification method to enable vessels
to discover gateway ports efficiently, thus optimizing their operations. The
proposed method processes vessel trajectory data to build dynamic graphs
capturing spatio-temporal dependencies between a set of static and dynamic
navigational features in the data, and it is evaluated in terms of port
classification accuracy on a real-world data set collected from ten vessels
operating in Halifax, NS, Canada. The experimental results indicate that our
TGNN-based port classification method provides an f-score of 95% in classifying
ports.
