AIS Data-Based Efficient Ship Trajectory Clustering

High-quality ship trajectory clustering is an important fundament for learning meaningful knowledge from massive historical trajectories, which are commonly extracted from the shipborne Automatic Identification System (AIS) data. However, clustering of massive trajectory data easily suffers from two limitations, i.e., low accuracy and high computational cost. In this work, we propose to develop an efficient ship trajectory clustering method based on the virtual maritime traffic network, which is constructed using the massive AIS data. In particular, we first adopt the popular Douglas-Peucker (DP) algorithm to simplify the ship trajectories to obtain the first type of feature points, where the moving ships change the course significantly. The second type of features points, where exist the large number of ship encounters, is also obtained through estimating the ship density map. The density-based clustering method is employed to remove the unwanted outliers in these feature points, contributing to the extraction of important waypoints. The virtual maritime traffic network is then constructed using the extracted waypoints. To obviously shorten the computational cost, the virtual network is finally used to cluster the massive ship trajectories by introducing the concepts of base cluster and stream cluster. Two famous metrics, i.e., Silhouette Coefficient (SC) and Davies-Bouldin Index (DBI), will be exploited to evaluate the trajectory clustering results for our method and several competing methods. Experimental results on realistic AIS data have demonstrated the superior clustering performance of our method in terms of computational efficiency, SC and DBI metrics.