ABSTRACT

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.

摘要

尽管基于 GPS 的人类轨迹数据已广泛用于旅行调查和人类流动性研究，但与研究可靠性内在相关的缺失数据或数据差距仍然是一个关键且具有挑战性的问题。本研究提出了一种基于频繁模式挖掘和时间地理的数据缺口插补框架，该框架允许通过评估缺口的时空棱柱与相应的频繁分布之间的时空拓扑关系来考虑插补过程中的时空旅行限制。活动或旅行。为了验证，建议的框架应用于从瑞士 139 名参与者收集的原始 GPS 轨迹。在案例研究中，时间和时空差距是通过从轨迹数据中随机选择活动和行程人为产生的。通过比较从原始数据、插补数据和有差距的数据计算的流动性指标（即持续时间和距离），我们从皮尔逊相关系数和偏差方面定量评估了所提出方法的性能。我们进一步将框架与基于生成的时空间隙的最短路径插值方法进行比较。比较结果证明了所提出的方法在从基于 GPS 的人体运动数据中估算间隙方面的性能和优势。我们进一步将框架与基于生成的时空间隙的最短路径插值方法进行比较。比较结果证明了所提出的方法在从基于 GPS 的人体运动数据中估算间隙方面的性能和优势。我们进一步将框架与基于生成的时空间隙的最短路径插值方法进行比较。比较结果证明了所提出的方法在从基于 GPS 的人体运动数据中估算间隙方面的性能和优势。