Resumen
Recognizing vessel navigation patterns plays a vital role in understanding maritime traffic behaviors, managing and planning vessel activities, spotting outliers, and predicting traffic. However, the growth in trajectory data and the complexity of maritime traffic in recent years presents formidable challenges to this endeavor. Existing approaches predominantly adopt a ?trajectory perspective?, where the instantaneous behaviors of vessel groups (e.g., the homing of fishing vessels) that occurred at certain times are concealed in the massive trajectories. To bridge this gap and to reveal collective patterns and behaviors, we look at vessel patterns and their dynamics at only individual points in time (snapshots). In particular, we propose a recognition framework from the snapshot perspective, mixing ingredients from group dynamics, computational geometry, graph theory, and visual perception theory. This framework encompasses algorithms for detecting basic types of patterns (e.g., collinear, curvilinear, and flow) and strategies to combine the results. Case studies were carried out using vessel trajectory (AIS) data around the Suez Canal and other areas. We show that the proposed methodology outperformed DBSCAN and clustering by measuring local direction centrality (CDC) in recognizing fine-grained vessel groups that exhibit more cohesive behaviors. Our results find interesting collective behaviors such as convoy, turning, avoidance, mooring (in open water), and berthing (in the dock), and also reveal abnormal behaviors. Such results can be used to better monitor, manage, understand, and predict maritime traffic and/or conditions.