Course Variance Clustering for Traffic Route Waypoint Extraction

Rapid Development and adoption of AIS as a survailance tool has resulted in widespread application of data analysis technology, in addition to AIS ship trajectory clustering. AIS data-based clustering has become an increasingly popular method for marine traffic pattern recognition, ship route prediction and anomaly detection in recent year. In this paper we propose a route waypoint extraction by clustering ships CoG variance trajectory using Density-Based Spatial Clustering of Application with Noise (DBSCAN) algorithm in both port approach channel and coastal waters. The algorithm discovers route waypoint effectively. The result of the study could be used in traffic route extraction, and more-so develop a maritime anomaly detection tool.