  Vessel trajectory clustering, which aims to find similar trajectory patterns,
has been widely leveraged in overwater 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, failing to represent
their evolution. To resolve this problem, we propose a Predictive Clustering of
Hierarchical Vessel Behavior (PC-HiV). PC-HiV first uses hierarchical
representations to transform every trajectory into a behavioral sequence. Then,
it 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 on real AIS
datasets demonstrate PC-HiV's superiority over existing methods, showcasing its
effectiveness in capturing behavioral evolution discrepancies between vessel
types (tramp vs. liner) and within emission control areas. Results show that
our method outperforms NN-Kmeans and Robust DAA by 3.9% and 6.4% of the purity
score.
