Abstract
Urban mobility data are indispensable for urban planning, transportation demand forecasting, pandemic modeling, and many other applications; however, individual mobile phone-derived Global Positioning System traces cannot generally be shared with third parties owing to severe re-identification risks. Aggregated records, such as origin-destination (OD) matrices, offer partial insights but fail to capture the key behavioral properties of daily human movement, limiting realistic city-scale analyses. This study presents a privacy-preserving synthetic mobility dataset that reconstructs daily trajectories from aggregated inputs. The proposed method integrates OD flows with two complementary behavioral constraints: (1) dwell-travel time quantiles that are available only as coarse summary statistics and (2) the universal law for the daily distribution of the number of visited locations. Embedding these elements in a multi-objective optimization framework enables the reproduction of realistic distributions of human mobility while ensuring that no personal identifiers are required. The proposed framework is validated in two contrasting regions of Japan: (1) the 23 special wards of Tokyo, representing a dense metropolitan environment; and (2) Fukuoka Prefecture, where urban and suburban mobility patterns coexist. The resulting synthetic mobility data reproduce dwell-travel time and visit frequency distributions with high fidelity, while deviations in OD consistency remain within the natural range of daily fluctuations. The results of this study establish a practical synthesis pathway under real-world constraints, providing governments, urban planners, and industries with scalable access to high-resolution mobility data for reliable analytics without the need for sensitive personal records, and supporting practical deployments in policy and commercial domains.
Abstract (translated)
城市移动数据对于城市规划、交通需求预测、疫情建模及其他多种应用来说至关重要；然而，由于严重的重新识别风险，个人手机获取的全球定位系统轨迹通常不能与第三方共享。尽管聚合记录（如出发地-目的地矩阵）能够提供部分见解，但它们无法捕捉日常人类活动的关键行为特征，这限制了对大规模城市的现实分析。这项研究提出了一种隐私保护型合成移动数据集，该数据集从聚合输入中重建每日轨迹。所提出的方法将出发地-目的地流量与两种互补的行为约束相结合：（1）仅以粗略统计数据形式存在的逗留出行时间分位数；以及（2）一天内访问地点数量的普遍规律。在多目标优化框架中嵌入这些元素能够再现真实的人类移动分布，同时确保不需要个人标识符。该框架在日本两个对比鲜明的地区得到了验证：（1）代表密集都市环境的东京特别区23区；和（2）福冈县，其中城市与郊区的出行模式共存。生成的合成移动数据在准确性上能够高度再现逗留出行时间和访问频率分布，而出发地-目的地一致性偏差仍保持在日常波动范围内。本研究的结果建立了实用综合路径的方法，在现实世界条件下为政府、城市规划者和行业提供可扩展且无需敏感个人记录就能获取高分辨率移动数据的可靠分析途径，并支持政策与商业领域的实际部署。
URL
https://arxiv.org/abs/2512.17239
