A Method of Constructing Maritime Route Network Based on Ship Trajectory Mining

The Maritime Route Network (MRN) is a spatiotemporal representation of maritime traffic characteristics and serves as a fundamental basis for ship route planning, behavior identification, and trajectory prediction. The vast amount of historical ship trajectory data provides foundational information for the automatic construction of the MRN. However, traditional automatic construction methods are hindered by poor accuracy in recognizing network nodes and a high error rate in connecting network edges due to trajectory data noise and uneven density distribution. To address these issues, this study proposes an automatic construction method for the MRN based on mining the spatiotemporal characteristics of ship trajectories. Three types of waypoints in the MRN are defined: stop points, entry/exit points, and route turning points. A waypoint extraction method based on trajectory spatiotemporal characteristics is designed. Additionally, a route turning point filtering strategy based on cumulative turning characteristics is proposed to effectively remove the non-route turning points caused by local activities such as ship collision avoidance and ship loitering. According to the distribution characteristics of different types of waypoints, a combination of the density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm and the convex hull algorithm is applied to extract and generate the set of MRN nodes from the waypoints set. Based on the definition of effective connection rules for the MRN nodes, the trajectory clusters between the MRN nodes are extracted from the original trajectories. The directed weighted edges between the MRN nodes are generated based on the statistical characteristics of trajectory clusters to form a directed weighted MRN. The proposed method is validated in the Pearl River Estuary area. The results indicate that the method can extract 71 MRN nodes of the three types and 200 routes. The recognition accuracy and misrecognition rate of the MRN nodes are 86.42% and 1.23%, respectively, while the accuracy rate of the MRN edge connections is nearly 95%. The proposed method effectively identifies the critical waypoints and main routes in the maritime routes and realizes the automatic construction of the MRN.