We formulate a statistical flight-pause model for human mobility, represented by a collection of random objects, called motions, appropriate for mobile phone tracking (MPT) data. We develop the statistical machinery for parameter inference and trajectory imputation under various forms of missing data. We show that common assumptions about the missing data mechanism for MPT are not valid for the mechanism governing the random motions underlying the flight-pause model, representing an understudied missing data phenomenon. We demonstrate the consequences of missing data and our proposed adjustments in both simulations and real data, outlining implications for MPT data collection and design.

翻译：我们为人类移动行为构建了一个统计飞行-停留模型，该模型由一组称为运动轨迹的随机对象集合表示，适用于手机追踪数据。我们发展了在多种缺失数据情形下进行参数推断与轨迹填补的统计方法体系。研究表明，手机追踪数据中关于缺失数据机制的常见假设并不适用于支配飞行-停留模型随机运动过程的机制，这代表了一种尚未被充分研究的缺失数据现象。通过仿真实验与真实数据分析，我们展示了缺失数据的后果及所提出的修正方法，并阐述了这对手机追踪数据采集与设计的重要启示。