Spatial Clustering Method of Historical AIS Data for Maritime Traffic Routes Extraction

Abstract

The automated extraction of maritime routes that accurately resembles the real traffic of vessels is crucial for intelligent traffic management systems to understand vessel behaviour in sea areas, identify events, and support decision-making. Available solutions for maritime traffic route extraction utilize traditional clustering algorithms, which have high computational costs. Data reduction methods are proposed for use with these clustering methods to improve clustering performance, which involves a loss in movement pattern quality. Such solutions often result in low quality representations of traffic routes, which poorly estimate sailing distances for long journeys and time of arrivals and poorly identify non-conformities. In this paper, we propose a spatial clustering method (SPTCLUST-II) to extract spatial representations of sailing routes from historical Automatic Identification System (AIS) data. Our method can cluster huge volumes of trajectory data in a minimal amount of time without using any of the traditional clustering algorithms and with no reduction or modification of the spatio-temporal predicates of the original trajectories. A real-world AIS dataset captured in the area of the Gulf of Mexico is used for the evaluation of the proposed method. The results demonstrate that the proposed method extracts tankers maritime traffic routes with an accuracy of 97% and a f1-measure of 98.5% and cargoes maritime traffic routes with an accuracy of 98.1% and a f1-measure of 99%. This method can be utilized by surveillance authorities for stable and sustainable vessel traffic management.

Topics & Concepts