An Improved DBSCAN Clustering Method for AIS Trajectories Incorporating DP Compression and Discrete Fréchet Distance
摘要
AIS provides a huge amount of maritime traffic data containing spatial and temporal information in a limited area. Trajectory clustering based on AIS data is a pre-task in intelligent maritime domain, providing typical movement patterns of vessels for follow-up studies in navigation safety and maritime supervision. This paper presents an AIS trajectory clustering method incorporating discrete Fréchet distance and Douglas-Peucker (DP) algorithm, based on improved density-based spatial clustering of applications with noise (DBSCAN). Experimental results on the dataset of vessels entering and leaving the Taiwan Strait in November 2017 demonstrate the effectiveness of our method.