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Publications
Rong, H., Teixeira, A.P. and Guedes Soares, C. (2020), “Data mining approach to shipping route characterization and anomaly detection based on AIS data”, Ocean Engineering, Vol. 198, pp. 106936-1 - 106936-12
A data mining approach is presented for probabilistic characterization of the maritime traffic off the continental coast of Portugal based on Automatic Identification System data. The approach automatically groups historical traffic data in terms of ship types, sizes, final destinations and other characteristics that influence the maritime traffic patterns in a given area. The approach consists of identifying relevant waypoints along a route where significant changes on the ships’ dynamic behaviour are observed, such as changes in heading, using a clustering method. Then, the maritime traffic is characterized probabilistically at the identified waypoints in terms of lateral distribution of the trajectories and speed profile, which allows the characterization of the typical behaviour of a group of similar ships along a particular route. In the proposed approach heading changes are automatically detected using the Douglas and Peucker algorithm and clustered by the density-based spatial clustering of applications with noise algorithm. The proposed method is applied to the characterization of southbound maritime traffic from the traffic separation scheme off Cape Roca to the ports of Lisbon, Setubal and Sines. Finally, an example of ship trajectory anomaly detection based on the developed maritime traffic probabilistic models is provided.
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