Ship Routes Planning Based on Traffic Clustering

Abstract

This work is about navigation safety of marine traffic at sea areas. In addition to traditional approach of danger situation detection based on vessels approaching, the current paper introduces another metrics derived from kinematic parameters of the vessel to identify whether it follows patterns (rules) of the traffic at a certain sea area. Authors focused their efforts on analyzing existing traffic schemas in order to identify its danger level in general rather than scrutinizing individual cases. Along with the traditional approach of sea traffic schema identifications, we propose an original method of automated identification of sea traffic schemes based on clustering of movement parameters using historical AIS data. For the clustering decomposition subtraction clustering algorithms were considered. The historical AIS data of sea traffic at Tsugaru strait is used for identifying traffic schema and ship routes planning with the model designed under presented research.