REFINE: Trajectory Representation Learning via Closed-Loop Transcription

2026-08-08

Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2

AI Summary

针对轨迹表示学习中现有方法的局限性，提出REFINE框架，通过闭环转录精炼结合反馈控制理论，提升模型对局部和全局时空依赖性的捕捉能力。

Abstract

Trajectory representation learning underpins a wide range of trajectory analytics tasks; however, most existing self-supervised approaches—whether discriminative or generative—adopt an open-loop paradigm, relying on fixed data augmentations or random masking without feedback, which limits their ability to generalize and scale. We propose REFINE, a simple yet effective Representation lEarning Framework vIa closed-loop traNscription rEfinement for trajectory data. Drawing upon feedback control theory, REFINE tightly couples road-network-aware generative reconstruction with feedback-driven contrastive learning, enabling the model to capture fine-grained local movement semantics and global spatio-temporal dependencies without manually designed augmentation views. We further provide a control-theoretic analysis that establishes convergence guarantees for the proposed closed-loop optimization. Extensive experiments on four real-world datasets offer evidence that REFINE is able to consistently outperform state-of-the-art methods across multiple downstream tasks, while being strong computationally efficient and scalable.

Problem

• trajectory representation learning
• open-loop paradigm
• data augmentation
• random masking
• generalization

Innovation

• closed-loop transcription
• feedback-driven contrastive learning
• road-network-aware generative reconstruction