A Contrastive Learning Framework for Vehicle Spatio-Temporal Trajectory Similarity in Intelligent Transportation Systems

The rapid development of vehicular networks has facilitated the extensive acquisition of vehicle trajectory data, which serve as a crucial cornerstone for a variety of intelligent transportation system (ITS) applications, such as traffic flow management and urban mobility optimization. Trajectory similarity computation has become an essential tool for analyzing and understanding vehicle movements, making it indispensable for these applications. Nonetheless, most existing methods neglect the temporal dimension in trajectory analysis, limiting their effectiveness. To address this limitation, we integrate the temporal dimension into trajectory similarity evaluations and present a novel contrastive learning framework, termed Spatio-Temporal Trajectory Similarity with Contrastive Learning, aimed at training effective representations for spatio-temporal trajectory similarity. The STT-CL framework introduces the innovative concept of spatio-temporal grids and leverages two advanced grid embedding techniques to capture the coarse-grained features of spatio-temporal trajectory points. Moreover, we design a Spatio-Temporal Trajectory Cross-Fusion Encoder (STT-CFE) that seamlessly integrates coarse-grained and fine-grained features. Experiments on two large-scale real-world datasets demonstrate that STT-CL surpasses existing methods, underscoring its potential in trajectory-driven ITS applications.