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, identifying key factors including demographic diversity across regions and the informativeness of the aggregate feature, paired with experiments demonstrating the practical implications of our theory. We release our code at https://github.com/schang-lab/ATLAS.

翻译：人类移动轨迹在公共卫生与社会科学领域被广泛研究，不同人口统计群体展现出显著差异的移动模式。然而，现有的轨迹生成模型很少能捕捉这种异质性，因为大多数轨迹数据集缺乏人口统计标签。为填补这一数据缺口，我们提出了ATLAS，一种仅使用以下数据的弱监督人口统计条件化轨迹生成方法：(i) 无人口统计标签的个体轨迹，(ii) 区域级聚合移动特征，以及 (iii) 来自人口普查数据的区域级人口统计构成。ATLAS训练一个轨迹生成器并对其进行微调，使得模拟的移动与观测到的区域聚合数据相匹配，同时以人口统计信息为条件。在带有人口统计标签的真实轨迹数据上的实验表明，ATLAS在人口统计真实性方面较基线模型有显著提升（JSD $\downarrow$ 12%--69%），并大幅缩小了与强监督训练之间的差距。我们进一步对ATLAS何时及为何有效进行了理论分析，识别出关键因素，包括跨区域的人口统计多样性和聚合特征的信息量，并辅以实验证明了我们理论的实际意义。我们在 https://github.com/schang-lab/ATLAS 发布了代码。