Behavior Pattern Mining of Inland Vessels Based on Trajectories

In view of complex situations of marine traffic safety, it is of great significance to investigate AIS data mining methods for useful traffic information.On basis of behavior patterns of inland vessels, a four-dimensional state-space model including temporal and spatial locations, speed, and course is proposed to describe behavior patterns of vessels.Considering high time complexity of extracting similar ship trajectories in the state space model, an incremental DBSCAN algorithm is thus introduced for effective calculations of different behavior patterns of vessels.Statistical methods such as kernel density estimation are further applied to derive vessel behavior characteristics under different modes, and spatial-temporal distributions of microscopic characteristics (i.e.vessel speed, heading angle, and position).Six different kinds of behavior patterns are analyzed through a case study in bifurcation waterways of Hanjiang River in Wuhan, China.Static information (types and sizes of ships), spatial distribution characteristics (trajectories, speeds, and heading angles), and arrival patterns of vessels are successfully extracted.The model can be helpful to improve supervision efficiency of maritime traffic safety.