PathTraj: Prompt-Aware Two-Stage Hierarchical Trajectory Generation from Language

Synthetic trajectory generation offers a practical means of obtaining human mobility data when access to real records is limited by privacy concerns, collection costs, and proprietary restrictions. However, existing language-conditioned methods typically map a global condition directly to trajectories. As a result, they often overlook intermediate travel requirements, degrade on long journeys and under ambiguous instructions, and fail to preserve fine-grained motion dynamics. To address these limitations, we propose PathTraj, a prompt-aware two-stage hierarchical framework that decomposes generation into route planning and trajectory synthesis. In the planning stage, PathTraj grounds the instruction in the road network to infer feasible origin and destination segments, then decodes a connected sequence of segments under graph constraints and converts it into continuous geometric guidance. In the synthesis stage, a diffusion model generates coordinate-level trajectories conditioned on the planned route and the instruction, while a Motion Representation Extractor derives local motion features from intermediate denoising states to guide realistic speed and acceleration dynamics. Experiments on two real-world taxi trajectory datasets show that PathTraj outperforms existing baselines in instruction faithfulness and motion realism, and maintains this advantage across travel distances and levels of instruction detail. The code is available at https://anonymous.4open.science/r/PathTraj-EC63 .