MUSE: Multi-Granular Urban Semantic Environment Modeling for Road-Network-Constrained Trajectory Generation

Human mobility trajectory generation synthesizes spatiotemporal traces that reflect real travel patterns, supporting urban planning, transportation analysis, and mobility research. Existing road-network-constrained generators primarily learn transitions between road segments from historical trajectories, leaving the urban environmental knowledge associated with these roads insufficiently explored. Points of interest (POIs) provide rich descriptions of urban functions, but turning heterogeneous, multi-scale POI information into road-aligned representations for generation remains challenging. To this end, we introduce **MUSE**, a Multi-granular Urban Semantic Environment modeling framework for integrating road-network topology with road-aligned environmental knowledge. It organizes open POI data into three complementary granularities: aggregate functional distributions (Macro), nearby POI instances (Local), and representative functional anchors (Landmark). A Road Query selectively aggregates instances and fuses these sources while preserving POI categories and source identities. Road and Environment autoregressive branches then learn from the same next-road targets, with gated environment-to-road interaction incorporating environmental history into road generation. Experiments on Beijing, Chengdu, and Xi'an show that **MUSE** improves the spatial and temporal distributional fidelity of generated trajectories, demonstrating the value of extracting road-aligned, multi-granular environmental knowledge and learning it jointly with road-network structure.