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.

翻译：船舶轨迹聚类旨在发现相似的轨迹模式，已在水上应用中广泛采用。传统方法通常依赖预定义规则和阈值来识别离散的船舶行为，追求高聚类质量并对完整序列（无论是原始轨迹还是其子轨迹）进行聚类，无法表征行为的演化过程。为解决这一问题，我们提出了层次船舶行为预测聚类方法（PC-HiV）。PC-HiV首先利用层次表示将每条轨迹转化为行为序列，然后基于这些表示预测序列中每个时间戳的演化状态。通过结合预测聚类与潜在编码，PC-HiV同时提升了聚类与预测性能。基于真实AIS数据集的实验表明，PC-HiV优于现有方法，在捕获船舶类型（不定期船与班轮）以及排放控制区内行为演化差异方面展现了有效性。结果显示，本方法在纯度评分上分别比NN-Kmeans和Robust DAA高出3.9%和6.4%。