Affiliations: School of Navigation, Dalian Maritime University, Dalian, Liaoning, China
Correspondence:
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Corresponding author: Zhengjiang Liu, %****␣jcm-19-jcm190024_temp.tex␣Line␣25␣**** School of Navigation, Dalian Maritime University, Dalian 116026, Liaoning, China. E-mail: [email protected].
Abstract: The large scale, high speed and increasing number of vessels along with busy sea routes increase the complexity of marine traffic. It is important for traffic controllers or mariners to understand the traffic situation and pay more attention to high-complexity area. In previous studies, density-based clustering algorithm was often used to discover high-density vessel clusters, so as to evaluate collision risk in waters. However, it can be argued that ship’s encounter situation was ignored with those algorithms. This paper focuses on complexity modeling of the two encountering ships and clustering using data mining technology. A complexity model is proposed by employing intrinsic features to reflect pair-wise interactions between ships. A clustering method of ship to ship encountering risk is presented on the basis of complexity by proposing a new distance definition, to quickly calculate the complexity of a large number of ships in an area.
