Dimitrios Kaklis, Ioannis Kontopoulos, Iraklis Varlamis, Ioannis Z. Emiris, Takis Varelas.
Abstract: Trajectory data holds pivotal importance in the shipping industry and transcend their
significance in various domains, including transportation, health care, tourism, surveillance, and
security. In the maritime domain, improved predictions for estimated time of arrival (ETA) and
optimal recommendations for alternate routes when the weather conditions deem it necessary can
lead to lower costs, reduced emissions, and an increase in the overall efficiency of the industry. To this
end, a methodology that yields optimal route recommendations for vessels is presented and evaluated
in comparison with real-world vessel trajectories. The proposed approach utilizes historical vessel
tracking data to extract maritime traffic patterns and implements an A* search algorithm on top of
these patterns. The experimental results demonstrate that the proposed approach can lead to shorter
vessel routes compared to another state-of-the-art routing methodology, resulting in cost savings
for the maritime industry. This research not only enhances maritime routing but also demonstrates
the broader applicability of trajectory mining, offering insights and solutions for diverse industries
reliant on trajectory data.
Journal of Marine Science and Engineering as part of the special issue for Machine Learning and Modeling for Ship Design 2024, 12(1), 157; https://doi.org/10.3390/jmse12010157
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