Individual mobility trajectories are difficult to measure and often incur long periods of missingness. Aggregation of this mobility data without accounting for the missingness leads to erroneous results, underestimating travel behavior. This paper proposes Dynamic Time Warping-Based Multiple Imputation (DTWBMI) as a method of filling long gaps in human mobility trajectories in order to use the available data to the fullest extent. This method reduces spatiotemporal trajectories to time series of particular travel behavior, then selects candidates for multiple imputation on the basis of the dynamic time warping distance between the potential donor series and the series preceding and following the gap in the recipient series and finally imputes values multiple times. A simulation study designed to establish optimal parameters for DTWBMI provides two versions of the method. These two methods are applied to a real-world dataset of individual mobility trajectories with simulated missingness and compared against other methods of handling missingness. Linear interpolation outperforms DTWBMI and other methods when gaps are short and data are limited. DTWBMI outperforms other methods when gaps become longer and when more data are available.

翻译：个体移动轨迹难以精确测量，且常存在长时间段的数据缺失。若忽略缺失问题直接对移动数据进行聚合分析，将导致结果失真，低估实际出行行为。本文提出基于动态时间规整的多重插补方法，旨在填补人类移动轨迹中的长间隙数据，以最大限度地利用现有数据。该方法将时空轨迹简化为特定出行行为的时间序列，根据潜在供体序列与受体序列间隙前后片段之间的动态时间规整距离筛选多重插补候选序列，最终进行多次数值插补。通过模拟研究确定DTWBMI的最优参数，形成该方法的两个版本。将这两种方法应用于包含模拟缺失的真实个体移动轨迹数据集，并与其它缺失数据处理方法进行比较。当数据量有限且间隙较短时，线性插值法优于DTWBMI及其它方法；当间隙较长且数据更充足时，DTWBMI则表现出更优性能。