MoCo-AIS: A Contrastive Learning Framework for Similarity Computation of Vessel Trajectories

• URL: http://arxiv.org/abs/2606.17978v1
• Date: Tue, 16 Jun 2026 14:30:09 GMT
• Title: MoCo-AIS: A Contrastive Learning Framework for Similarity Computation of Vessel Trajectories
• Abstract summary: Trajectory similarity is a fundamental task in analyzing mobility patterns.<n>Traditional distance-based measures for computing similarity incur high computational cost.<n>This paper presents MoCo-AIS, a unified framework for learning vessel trajectory embeddings.
• Score: 0.6150052602894222
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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