Ship Trajectory Clustering Method Considering Navigation Behavior Sequences

Existing ship trajectory clustering methods often overlook the impact of navigation behaviors (e.g., heading and speed variations) on clustering performance. To address this limitation, a novel ship trajectory clustering method that explicitly incorporates navigation behavior sequence is proposed. Firstly, ship trajectories are preprocessed, and key motion parameters, including the ship Rate Of Turn (ROT) and acceleration at each trajectory point, are calculated through a sliding window. Secondly, by integrating various motion parameters, the navigation behaviors corresponding to trajectory points are classified, and the classification results are taken as the core element to measure the behavior distance between different trajectories. Then, the spatial distance between trajectories is measured based on the Hausdorff distance. Finally, an adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is adopted, which fuses behavior distance and spatial distance, to realize ship trajectory clustering that takes navigation behavior into account. Experimental results on Dalian Port and Yantai Port datasets show that: (1) Compared with the classical DBSCAN and Multi-dimensional Density-Based Trajectory Clustering of Applications with Noise (MD-DBTCAN) methods, the proposed method achieves finer granularity of clustering results; (2) Compared with the classical DBSCAN method, the proposed method can effectively distinguish straight-line navigation trajectories from trajectories with frequent turning behaviors; compared with the MD-DBTCAN method, the proposed method can distinguish normal straight-line navigation trajectories from trajectories with frequent acceleration and deceleration behaviors.