MSST: Multi-Scale Spatial-Temporal Representation Learning for Trajectory Similarity Computation
Li Li, Junjun Si, Jinna Lv, Junting Lu, Jianyu Zhang, Shuaifu Dai · IEEE Transactions on Big Data · 2025
Computing trajectory similarity is a fundamental task in trajectory analysis. Traditional heuristic methods suffer from quadratic computational complexity, which limits their scalability to large datasets. Recently, Trajectory Representation Learning (TRL) has been extensively studied to address this limitation. However, most existing TRL algorithms face two key challenges. First, they prioritize spatial similarity while neglecting the intricate spatio-temporal dynamics of trajectories, particularly temporal regularities. Second, these methods are often constrained by predefined single spatial or temporal scales, which can significantly impact performance, since the measurement of trajectory similarity depends on spatial and temporal resolution. To address these issues, we propose MSST, a Multi-Scale Self-supervised Trajectory Representation Learning framework. MSST simultaneously processes spatial and temporal information by generating 3D spatial-temporal tokens, thereby capturing spatio-temporal characteristics of trajectories more effectively. Further, MSST explore the multi-scale characteristics of trajectories. Finally, self-supervised contrastive learning is employed to enhance the consistency between the trajectory representations from different views. Experimental results on three real-world datasets for similarity trajectory computation provide insight into the design properties of our approach and demonstrate the superiority of our approach over existing TRL methods. MSST significantly surpasses all state-of-the-art competitors in terms of effectiveness, efficiency, and robustness. We explore the multi-scale characteristics of trajectories. To the best of our knowledge, this is the first effort in the TRL literature. Compared to previous TRL research, the proposed method can balance the noise and the details of trajectories, enabling a more comprehensive analysis by accounting for the variability inherent in trajectory data across different scales.