UBC Theses and Dissertations

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UBC Theses and Dissertations

Ais data-driven general vessel destination prediction : a trajectory similarity-based approach Zhang, Chengkai


Shipping is one of the major transportation approaches around the world. With the growing demand for global shipping service, the vessel destination prediction has shown its significant role in improving the efficiency of decision making in industry and ensuring a safe and efficient maritime traffic environment. Currently, most vessel destination prediction methods focus on regional destination prediction, which has restrictions on destinations and regions. Thus, this thesis proposes a general AIS (Automatic Identification System) data-driven vessel destination prediction method. The proposed method first extracts the vessel's traveling trajectory and departure port from AIS records. The similarities between traveling and historical trajectories are then measured and utilized to predict the destination. The destination of the historical trajectory, which shares the highest similarity with the traveling trajectory, is predicted as the vessel's destination. Compared with related work that using maritime records as input and destination as output, the proposed method is more general, accurate, and updatable. In this thesis, a historical trajectory database was generated from more than 141 million AIS records, which covers 534,824 traveling patterns between ports and more than 5.9 million historical trajectories. Comparative studies were carried out to validate the performance of the proposed method, where eight state-of-the-art similarity measurement methods combined with two different decision strategies were implemented and compared. The experimental results demonstrate that the proposed random forest-based model combined with the port frequency-based decision strategy achieves the best prediction accuracy on 35,937 testing trajectories.

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Attribution-NonCommercial-NoDerivatives 4.0 International