Autoregressive STG-based Diffusion Model for Spatiotemporal Trajectory Generation

The urban foundation model is critical for trajectory-based mobile applications, which require accurate synthesis of paths that adhere to spatial constraints (road networks) and contextual constraints (e.g., weather, traffic). However, existing methods predominantly rely on task-specific models, which fail to holistically capture and integrate diverse spatial patterns (e.g., connectivity) and temporal dynamics (e.g., periodicity, trends) within a cohesive framework, limiting their generalization across diverse prediction tasks. To bridge this gap, we propose AutoDiff, a diffusion-based model generating trajectories on spatial temporal graph (STG), which establishes a new paradigm for trajectory generation as a foundation model for sequential spatiotemporal data. Specifically, we disentangle complex spatiotemporal features as generalizable segment-wise time slices on road networks through autoregressive diffusion generation, which not only enforces realistic trajectory connectivity within road networks, but also enables knowledge transfer across tasks like trajectory recovery and travel time prediction. Besides, we design a confidence-based early-exiting mechanism to eliminate redundant denoising steps without sacrificing quality, enabling scalable applications in mobility analytics. Extensive experiments on three real-world urban trajectory datasets demonstrate the superior performance of AutoDiff in path prediction, trajectory recovery and time estimation tasks, outperforming task-specific baselines while maintaining computational efficiency.