Analysis of Navigation Characteristics of Inland Watercraft Based on DBSCAN Clustering Algorithm

Abstract

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. Using DBSCAN clustering algorithm to extract similar ship trajectories in state space to calculate the behavior patterns of different ships; Statistical methods such as kernel density estimation are further being applied to derive vessel behavior characteristics under different modes, and spatial-temporal distributions of microscopic characteristics (i.e. vessel speed, heading angle, and position). Five different kinds of behavior patterns are analyzed through a case study in bifurcation waterways of Hanjiang River in Wuhan, China. Static information (sizes of ships), spatial distribution characteristics (trajectories and speeds), and arrival patterns of vessels are successfully extracted. The model can be helpful to improve supervision efficiency of maritime traffic safety.