Reliable vessel trajectory clustering: A maritime shipping network-driven computational method

Vessel trajectory clustering has attracted growing attention in maritime applications, such as anomaly detection, route planning, behavior prediction and situational awareness, etc. However, the clustering results often suffer from low efficiency and robustness, especially for the massive vessel trajectories. To achieve satisfactory clustering results, this work proposes a maritime shipping network-driven vessel trajectory clustering method, highly dependent on the accurate and robust extraction of network nodes (i.e., waypoints). We first exploit the sub-trajectory-sensitive Douglas-Peucker (DP) and Density Peak Clustering (DPC) algorithms, respectively, to identify the turning and crossing feature points. The whole raw feature points are then obtained by combining these turning and crossing feature points with the starting and ending points of vessel trajectories. The final waypoints can be extracted accordingly by …

Recommended citation: C Xu, S Zhou, M Liang, Z Liu, RW Liu. (2025). Reliable vessel trajectory clustering: A maritime shipping network-driven computational method. Ocean Engineering, 336, 121691. https://www.sciencedirect.com/science/article/pii/S0029801825013976