Predictive Clustering of Vessel Behavior Based on Hierarchical Trajectory Representation

Vessel trajectory clustering, which aims to find similar trajectory patterns, has been widely leveraged in maritime applications. Most traditional methods use predefined rules and thresholds to identify discrete vessel behaviors. They aim for high-quality clustering and conduct clustering on entire sequences, whether the original trajectory or its sub-trajectories, overlooking the behavioral significance and evolution characteristics. To resolve this problem, we propose a Predictive Clustering of Hierarchical Vessel Behavior (PC-HiV). PC-HiV first utilizes hierarchical representations to transform every trajectory into a behavioral sequence. It then predicts evolution at each timestamp of the sequence based on the representations. By applying predictive clustering and latent encoding, PC-HiV improves clustering and predictions simultaneously. Experiments conducted on real AIS datasets demonstrate that PC-HiV effectively captures behavioral evolution discrepancies between different vessel types (tramp vs. liner) and near emission control area boundaries. Additionally, the results show that PC-HiV outperforms NN-Kmeans and Robust DAA by 3.9% and 6.4% in terms of purity scores, thereby proving the superiority of the proposed PC-HiV over existing models.