A Method for Clustering Ship Trajectory through Extracting Multiple Feature Points

Trajectory clustering plays a significant role in the fields of ship behavior analysis and maritime regulation. Due to the inconsistent length and sampling rate of ship trajectories, as well as significant structural differences, it is difficult to achieve a high accuracy and efficient clustering for large numbers of ship trajectories in wide water areas. To address these problems, we propose to first take full advantage of the massive ship historical voyage data collected from the automatic identification system (AIS), and then extract the positional features related to the ship navigation behavior and traffic density. An efficient ship trajectory clustering method is finally presented by exploiting the multi-feature points. Moving ships, in general, have the characteristics of maintaining a similar direction and speed in most cases, the data compression method can thus be used to capture the trajectory points with significant changes in the navigation process and then extract them as the ship trajectory structure feature points. When ships come across encounters, a method for estimating the probability density is used to analyze the spatial distribution characteristics of ship traffic flow and extract their trajectory points as traffic flow feature points. To remove outliers in these two classes of feature points, a density clustering algorithm is employed to cluster the high-quality feature points, which further improves the reliability of feature points. The center of each class in the clusters is then used as the representative feature points. The distribution of ship trajectories passing through representative feature points is counted, considering ship trajectories with similar distribution as the same class. Numerous experiments have been carried out based on real-world AIS data, collected from the Chengshantou waters, the southern trough of the Yangtze River estuary, and the Zhoushan waters, to compare our proposed model with four typic clustering methods, i.e., the K-medoids clustering, the hierarchical clustering, the spectral clustering, and the density-based spatial clustering of applications with noise (DBSCAN). In the above-mentioned typical waters, the average silhouette coefficient is improved by approximately 53%, 71%, 63% and 41% and the Davies-Bouldin index is decreased by approximately 57%, 67%, 63% and 45%, respectively. At the same time, the method can reduce the clustering time by about 56% on average, which significantly improves the efficiency of ship trajectory clustering.