Applying frequent-pattern mining and time geography to impute gaps in smartphone-based human-movement data

Though GPS-based human trajectory data have been commonly used in travel surveys and human mobility studies, missing data or data gaps that are intrinsically relevant to research reliability remain a critical and challenging issue. This study proposes a novel framework for imputing data gaps based on frequent-pattern mining and time geography, which allows for considering spatio-temporal travel restrictions during imputation by evaluating the spatio-temporal topology relations between the space-time prisms of gaps and corresponding frequent activities or trips. For the validation, the proposed framework is applied to raw GPS trajectories that were collected from 139 participants in Switzerland. In the case study, the temporal and spatio-temporal gaps are artificially generated by randomly choosing activities and trips from the trajectory data. Through comparing the mobility indicators (i.e. duration and distance) calculated from raw data, imputed data, and data with gaps, we quantitatively evaluate the performance of the proposed method in terms of Pearson correlation coefficients and deviation. We further compare the framework with the shortest path interpolation method based on the generated spatio-temporal gaps. The comparison results demonstrate the performance and advantage of the proposed method in imputing gaps from GPS-based human movement data.<br>

尽管基于全球定位系统（Global Positioning System）的人类轨迹数据已广泛应用于出行调查与人类移动性研究，但与研究可靠性内在相关的数据缺失或数据间隙问题，仍是一项关键且极具挑战性的难题。本研究提出一种基于频繁模式挖掘与时间地理学的数据间隙填补新框架，该框架可通过评估间隙时空棱柱与对应高频出行活动或行程之间的时空拓扑关系，在填补过程中考量时空出行约束。为验证该框架的有效性，本研究将其应用于瑞士139名受访者采集的原始GPS轨迹数据。在案例研究中，研究人员通过从轨迹数据中随机选取出行活动与行程，人工生成时间间隙与时空间隙。通过对比基于原始数据、填补后数据与存在间隙数据计算得到的移动性指标（即出行时长与出行距离），本研究以皮尔逊相关系数与偏差为指标，定量评估所提方法的性能表现。本研究还将所提框架与基于人工生成时空间隙的最短路径插值法进行对比。对比结果证明了所提方法在填补GPS人类移动数据间隙方面的性能与优势。