Navigation Decision Support: Discover of Vessel Traffic Anomaly According to the Historic Marine Data


  • Andrius Daranda Vilnius University
  • Gintautas Dzemyda VU Institute of Data Science and Digital Technologies Vilnius University, Lithuania


marine anomaly detection, marine traffic, spatial data, DBSCAN, clustering, k-nearest neighbors, regression


During the last years, marine traffic dramatically increases. Marine traffic safety highly depends on the mariner’s decisions and particular situations. The watch officer must continuously observe the marine traffic for anomalies because the anomaly detection is crucial to predict dangerous situations and to make a decision in time for safe marine navigation. In this paper, we present marine traffic anomaly detection by the combination of the DBSCAN clustering algorithm (Density- Based Spatial Clustering of Applications with Noise) with k-nearest neighbors analysis among the clusters and particular vessels. The clustering algorithm is applied to the historic marine traffic data — a set of vessel turn points. In our experiments, the total number of turn points was about 3 million, and about 160 megabytes of computer store was used. A formal numerical criterion to com-pare anomaly with normal traffic flow case has been proposed. It gives us a possibility to detect the vessels outside the typical traffic pattern. The proposed meth-od ensures the right decisions in different oceanic scale or hydro meteorology conditions in the detection of anomaly situation of the vessel.


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