这篇论文给做轨迹相似性的人提供了一个现成的对比学习框架，用MoCo范式在真实AIS数据上测了多个模型，效果比基线好。
MoCo-AIS提出一种基于 Momentum Contrast 的对比学习框架，用于学习船舶轨迹的嵌入表示。该框架在大型真实AIS数据集（包含多种航行行为与工况）上评估了多种主流深度学习模型。结果表明，MoCo-AIS在轨迹相似性学习任务上显著优于现有基线方法。同时，该框架为轨迹表示模型的评估提供了统一基准平台。
MoCo-AIS: A Contrastive Learning Framework for Similarity Computation of Vessel Trajectories
Trajectory similarity is a fundamental task in analyzing mobility patterns, essential for applications such as route pattern extraction, mobility prediction, and anomaly detection. Traditional distance-based measures for computing similarity incur high computational cost, driving the adoption of lightweight learning-based approaches. Supervised methods rely on extensive labels derived from traditional distance measures and often reproduce these metrics, which limits generalization. While self-supervised learning addresses this issue through contrastive learning, it lacks a unified framework, making it difficult to compare deep learning (DL) models for consistent trajectory representation. Accordingly, this paper presents MoCo-AIS, a unified framework for learning vessel trajectory embeddings based on the Momentum Contrast (MoCo) paradigm, which formulates similarity learning through positive and negative trajectory pairs. Within this framework, we evaluate a diverse set of leading DL models on large-scale, real-world vessel-tracking AIS datasets that capture diverse navigation behaviors and operating conditions. Results demonstrate that our framework significantly improves similarity learning over existing baselines, while providing a benchmarking platform for evaluating trajectory representation models.