Trajectory Representation Learning on Grids and Road Networks with Spatio-Temporal Dynamics

Urban Foundation Models (UFMs) are emerging as a transformative paradigm for understanding and managing complex urban systems, offering new capabilities to improve and transform urban environments. Trajectory data (e.g., vehicle movements) contains rich spatio-temporal patterns of human mobility and urban dynamics that are crucial for understanding city-wide movement behaviors, traffic patterns, and transportation system operations. Effectively modeling such trajectory data is crucial for UFMs, yet this poses significant challenges due to the high-dimensional nature of raw trajectories and their complex spatio-temporal dynamics. Trajectory representation learning addresses these challenges by transforming raw trajectory data into compact, meaningful representations that capture essential mobility patterns and facilitates the application to various urban tasks, such as trajectory similarity computation, travel time estimation, or destination prediction. This is achieved by learning low-dimensional representations from high-dimensional and raw trajectory data. However, existing methods for trajectory representation learning either rely on grid-based or road-based representations, which are inherently different and thus, could lose information contained in the other modality. Moreover, these methods overlook the dynamic nature of urban traffic, relying on static road network features rather than time-varying traffic patterns. In this paper, we propose TIGR , a novel model designed to integrate grid and road network modalities while incorporating spatio-temporal dynamics to learn rich, general-purpose representations of trajectories. We evaluate TIGR on three real-world datasets and demonstrate the effectiveness of combining both modalities by substantially outperforming state-of-the-art methods, i.e., up to 59.34% for trajectory similarity, up to 16.65% for travel time estimation, and up to 14.81% for destination prediction.