AuthorsD. Altan, M. Etemad, D. Marijan and T. Kholodna
TitleDiscovering Gateway Ports in Maritime using Temporal Graph Neural Network Port Classification
AfilliationSoftware Engineering
Project(s)TRANSACT, Department of Validation Intelligence for Autonomous Software Systems
Publication TypeProceedings, refereed
Year of Publication2022
Conference Name35th Canadian Conference on Artificial Intelligence
PublisherCanadian Artificial Intelligence Association
Keywordsactual ports, AIS data, gateway ports, maritime situational awareness, maritime traffic, port area, port classification, port congestion, spatio-temporal, temporal graph neural networks, vessel trajectories

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.


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Citation Key42454

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