提出统一框架，用对比学习提升船舶轨迹相似性计算效率与泛化能力。
MoCo-AIS: A Contrastive Learning Framework for Similarity Computation of Vessel Trajectories
- 基于动量对比机制构建统一轨迹嵌入框架，通过正负样本对学习相似性。
- 在真实大规模AIS数据上验证，性能显著优于现有基线方法。
- 适合研究轨迹分析、船舶行为建模或对比学习的科研人员使用。
轨迹相似性是分析移动模式的基础任务，对路径模式提取、移动预测和异常检测等应用至关重要。传统基于距离的相似性度量计算成本高，促使轻量级学习方法的采用。监督方法依赖大量由传统度量生成的标签，常复现原有指标，限制泛化能力。尽管自监督学习通过对比学习缓解此问题，但缺乏统一框架，难以对深度学习模型进行一致的轨迹表征比较。为此，本文提出MoCo-AIS，一种基于动量对比（MoCo）范式的统一框架，通过正负轨迹对实现相似性学习。在包含多样航行行为与运营条件的真实世界船舶追踪AIS数据集上，评估了多种先进深度学习模型。结果表明，该框架在相似性学习上显著优于现有基线，同时为轨迹表征模型提供基准评测平台。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。