XTraj: A Coarse-to-Fine Autoregressive Framework for Transferable Trajectory Generation

Generating realistic human trajectories is essential for mobility simulation but remains challenging when they must generalize across cities and respect road network constraints. In practice, existing methods often suffer from three key limitations: geographic overfitting, length-rigid generation, and off-road drift when generating GPS coordinates directly. To address these issues, we propose \textbf{XTraj}, a transferable coarse-to-fine autoregressive framework for road-consistent trajectory generation. Specifically, XTraj first learns transferable road-segment representations by integrating road geometry, POI context, historical traffic intensity, and graph topology. Based on these representations, it then autoregressively generates variable-length road-segment routes under road-connectivity constraints. Finally, instead of directly regressing latitude-longitude coordinates, XTraj further refines each generated route in a route-progress space by predicting monotonic progress increments along valid road geometry, from which GPS points are recovered by interpolation. This route-aligned design naturally supports variable-length GPS generation and guarantees road-geometry consistency by construction. Experiments on two real-world vehicle trajectory datasets show that XTraj improves spatial fidelity over competitive baselines, transfers effectively to unseen cities in a zero-shot setting, and ensures road-geometry consistency by construction without any post-hoc map matching. The implementation is provided in \textbf{https://anonymous.4open.science/r/XTraj-EBE7/}.