Data mining approach to shipping route characterization and anomaly detection based on MS data

A data mining approach is presented for probabilistic characterization of maritime traffic and anomaly detection. The approach automatically groups historical traffic data provided by the Automatic Identification System in terms of ship types, sizes, final destinations and other characteristics that influence the maritime traffic patterns off the continental coast of Portugal. The approach consists of identifying relevant waypoints along a route where significant changes in the ships' navigational behaviour are observed, such as changes in heading, using trajectory compression and clustering algorithms. This provides a vector-based representation of the ship routes consisting of straight legs and connecting turning sections that facilitates route probabilistic characterization and anomaly detection. The maritime traffic is characterized probabilistically at the identified route legs and 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, Settbal and Sines. Finally, an example of ship trajectory anomaly detection based on the developed maritime traffic probabilistic models is provided.