[2603.03275] Learning Demographic-Conditioned Mobility Trajectories with Aggregate Supervision

Computer Science > Machine Learning arXiv:2603.03275 (cs) [Submitted on 3 Mar 2026] Title:Learning Demographic-Conditioned Mobility Trajectories with Aggregate Supervision Authors:Jessie Z. Li, Zhiqing Hong, Toru Shirakawa, Serina Chang View a PDF of the paper titled Learning Demographic-Conditioned Mobility Trajectories with Aggregate Supervision, by Jessie Z. Li and 3 other authors View PDF HTML (experimental) Abstract:Human mobility trajectories are widely studied in public health and social science, where different demographic groups exhibit significantly different mobility patterns. However, existing trajectory generation models rarely capture this heterogeneity because most trajectory datasets lack demographic labels. To address this gap in data, we propose ATLAS, a weakly supervised approach for demographic-conditioned trajectory generation using only (i) individual trajectories without demographic labels, (ii) region-level aggregated mobility features, and (iii) region-level demographic compositions from census data. ATLAS trains a trajectory generator and fine-tunes it so that simulated mobility matches observed regional aggregates while conditioning on demographics. Experiments on real trajectory data with demographic labels show that ATLAS substantially improves demographic realism over baselines (JSD $\downarrow$ 12%--69%) and closes much of the gap to strongly supervised training. We further develop theoretical analyses for when and why ATLAS works, identifyin...