Learning Demographic-Conditioned Mobility Trajectories with Aggregate Supervision
Jessie Z. Li, Zhiqing Hong, Toru Shirakawa, Serina Chang

TL;DR
This paper introduces ATLAS, a weakly supervised model that generates human mobility trajectories conditioned on demographic data using only regional aggregates and unlabelled individual trajectories, improving demographic realism.
Contribution
ATLAS is a novel weakly supervised approach that leverages regional aggregate data to generate demographic-conditioned mobility trajectories without requiring individual demographic labels.
Findings
ATLAS significantly reduces demographic distribution divergence (JSD down by 12-69%).
It approaches the performance of strongly supervised models.
Theoretical analysis explains when and why ATLAS is effective.
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…
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Taxonomy
TopicsHuman Mobility and Location-Based Analysis · Data-Driven Disease Surveillance · Machine Learning in Healthcare
