Reconstruction of maritime route network in nearshore waters with noise removal using step-by-step density-based clustering

The increasing volume of marine traffic has significantly heightened the risk of accidents in nearshore and confined waters. While previous studies have primarily focused on large-scale maritime route networks, they often overlook the detailed delineation of ship channel profiles and centerlines. In high-risk areas, accurately mapping ship channel outlines and primary routes is crucial for traffic safety and route selection. Thus, this paper proposes a method for modeling maritime route networks in high-risk areas using Automatic Identification System data and Step-by-step Density-Based Spatial Clustering of Applications with Noise (S2-DBSCAN). First, stay trajectories are cleaned, and anomalies are identified. Then, three trajectory similarity metrics Hausdorff distance, Course Over Ground (COG) similarity of key turning points, and average COG similarity are used to design the S2-DBSCAN algorithm for trajectory clustering. Finally, the centerline of clusters is extracted using constrained Delaunay triangulation to form the route network. Experimental results on real data demonstrate that our method effectively removes noise and extracts the route network from ship trajectory clusters. In particular, the identified directional routes accurately represent ships’ true positions and movement patterns in geospatial space. This enhances route selection in busy waters, supports risk avoidance strategies, and aids maritime regulation.