发表机构
Fondazione Bruno Kessler (FBK); George Mason University（布鲁诺·凯斯勒基金会; 乔治梅森大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出BePop框架，结合人口普查与时间使用调查校准手机GPS移动数据，改善其行为代表性，校正偏差对移动性测度的影响，为人口代表性移动性推断提供通用方案。
AI 中文摘要
手机移动数据已改变人类行为研究，但人口统计与行为偏差会损害其代表性，扭曲人口层面的推断。现有校准方法主要解决人口统计与地理代表性，基本未校正行为差异。本文提出行为人口（BePop）框架，利用人口普查数据和时间使用调查，联合校准移动数据以匹配代表性人口统计与行为分布。BePop将移动序列嵌入行为画像，估计个体层面权重，使人口构成与日常活动模式均对齐。在美国三个大都市区，该框架持续提升GPS衍生移动性与代表性行为分布的一致性，包括时间分配、活动转换和移动 motif（模式）。校准还显著改变下游移动性指标，表明行为偏差可传播至常用移动性测度。本研究确立行为代表性是人口统计校准的关键补充，为人口代表性移动性推断提供通用框架。
英文摘要
Mobile phone mobility data have transformed the study of human behavior, but demographic and behavioral biases can compromise their representativeness and distort population-level inference. Existing calibration approaches primarily address demographic and geographic representativeness, leaving behavioral discrepancies largely uncorrected. Here we introduce the Behavioral Population (BePop) framework, which jointly calibrates mobility data to representative demographic and behavioral distributions using census data and time-use surveys. BePop embeds mobility sequences into behavioral profiles and estimates person-level weights that align both population composition and daily activity patterns. Across three U.S. metropolitan areas, the framework consistently improves agreement between GPS-derived mobility and representative behavioral distributions, including time allocation, activity transitions, and mobility motifs. Calibration also substantially alters downstream mobility indicators, demonstrating that behavioral biases can propagate into commonly used mobility measures. Our results establish behavioral representativeness as a critical complement to demographic calibration and provide a general framework for population-representative mobility inference.
