Maritime Traffic Route Detection Framework Based on Statistical Density Analysis From AIS Data Using a Clustering Algorithm

Abstract

Maritime traffic routes by ships navigation vary according to country and geographic characteristics, and they differ according to the characteristics of the ships. In ocean areas adjacent to coasts, regulated routes are present, e.g., traffic separation scheme for ships entering and leaving; however, most ocean areas do not have such routes. Maritime traffic route research has been conducted based on computer engineering to create routes; however, ship characteristics were not considered. Thus, this article proposes a framework to generate maritime traffic routes using statistical density analysis. Here, automatic identification system (AIS) data are used to derive quantitative traffic routes. Preprocessing is applied to the AIS data, and a similar ship trajectory pattern is decomposed into a matrix based on the Hausdorff–distance algorithm and then stored in a database. A similar pattern makes the AIS trajectory simple using the Douglas–Peucker algorithm. In addition, density-based spatial clustering of applications with noise (DBSCAN) is performed to identify the waypoints of vessels then create routes by connecting waypoints. The width of maritime routes created based on a similar ship trajectory is subjected to kernel density estimation analysis (KDE). Then, waypoints evaluation of the main route is performed from the results of KDE 75% and 90% considering the statistical in the total maritime traffic, and the results applied to the targeted ocean area are compared. Finally, the result of KDE 90% of maritime traffic with framework analyzed the safety route, which can be a basis for developing routes for maritime autonomous surface ships.

Keywords

• AIS data
• clustering algorithm
• DBSCAN
• kernel density estimation
• framework
• maritime traffic route