Vessel movement analysis and pattern discovery using density-based clustering approach

Automatic identification system (AIS) has been widely equipped on vessels for maritime communication, positioning and traffic monitoring. The comprehensive data obtained by AIS provides spatio-temporal traces depicting the vessels' trajectories and can be used as a coherent source of information for vessels' behavior and the overall maritime traffic analysis, in supporting of the better traffic planning and service optimization. However, it is challenging to process and analysis such a large amount of AIS data that is associated with a great variety of vessels. In this paper, we propose an unsupervised data mining method using density-based strategy to analyze vessels' trajectories and extract the traffic patterns from historical AIS data. It starts with stops and moves identification from vessels' trajectories, followed by the extraction of stationary areas of interest from the stops and the detection of the main traffic routes from the moves using density-based clustering method, which takes both the speed and direction into consideration. Experiments on the real AIS data demonstrate the effectiveness of this work.