Passively-generated mobile data has grown increasingly popular in the travel behavior (or human mobility) literature. A relatively untapped potential for passively-generated mobile data is synthetic population generation, which is the basis for any large-scale simulations for purposes ranging from state monitoring, policy evaluation, and digital twins. And yet, this significant potential may be hindered by the growing sparsity or rate of missingness in the data, which stems from heightened privacy concerns among both data vendors and consumers (users of service platforms generating individual mobile data). To both fulfill the great potential and to address sparsity in the data, there is a need to develop a flexible and scalable model that can capture individual heterogeneity and adapt to changes in mobility patterns. We propose a conditional-generative Gaussian process framework that learns kernel structures characterizing individual mobile data and can provably replicate observed patterns. Our approach integrates physical knowledge to regularize the framework such that the generated data obeys constraints imposed by the built and natural environments (such as those on velocity and bearing). To capture travel behavior heterogeneity at the individual level, we propose a data-driven multiple kernel learning approach to determine the optimal composite kernel for every user. Our experiments demonstrate that: (1) the impact of kernel choice on mobility metrics derived from synthetic data is non-negligible; (2) physics-regularization not only reduces model bias but also improves uncertainty estimates associated with the predicted locations; and (3) the proposed method is robust and generalizes well to varying individuals and modes of travel.

中文翻译：

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学习生成合成人体移动数据：一种基于多核学习的物理正则化高斯过程方法


被动生成的移动数据在旅行行为（或人类移动性）文献中越来越受欢迎。被动生成的移动数据的一个相对未开发的潜力是合成人口生成，这是任何大规模模拟的基础，用于状态监控、政策评估和数字孪生等目的。然而，这种巨大的潜力可能会受到数据稀疏性或缺失率日益增长的阻碍，这源于数据供应商和消费者（生成个人移动数据的服务平台的用户）对隐私的高度关注。为了发挥巨大潜力并解决数据稀疏性，需要开发一个灵活且可扩展的模型，以捕获个体异质性并适应移动模式的变化。我们提出了一个条件生成高斯过程框架，该框架学习表征单个移动数据的内核结构，并可以证明地复制观察到的模式。我们的方法整合了物理知识，使框架规范化，以便生成的数据遵守建筑和自然环境施加的约束（例如速度和方位的约束）。为了在个人层面捕捉出行行为的异质性，我们提出了一种数据驱动的多核学习方法，以确定每个用户的最佳复合核。我们的实验表明：（1） 核选择对合成数据得出的迁移率指标的影响不可忽视;（2） 物理正则化不仅减少了模型偏差，还提高了与预测位置相关的不确定性估计;（3） 所提出的方法很稳健，可以很好地推广到不同的个体和出行方式。