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.

翻译：轨迹相似度是分析移动模式的基础任务，对于路径模式提取、移动预测和异常检测等应用至关重要。传统的基于距离的相似度计算方法计算成本高昂，因此推动了轻量级基于学习方法的应用。监督方法依赖于从传统距离度量中提取的大量标签，并且通常再现这些指标，这限制了泛化能力。虽然自监督学习通过对比学习解决了这一问题，但缺乏统一的框架，使得难以比较深度学习模型以获取一致的轨迹表示。因此，本文提出了MoCo-AIS，这是一个基于动量对比（MoCo）范式的统一框架，用于学习船舶轨迹嵌入，通过正负轨迹对来表述相似度学习。在该框架内，我们在大规模真实世界船舶跟踪AIS数据集上评估了多种主流深度学习模型，这些数据集捕获了多样化的航行行为与运行条件。结果表明，我们的框架显著提升了现有基准模型上的相似度学习性能，同时为评估轨迹表示模型提供了一个基准平台。