Joint activities, despite their importance, remain poorly explained in travel behavior analysis due to lack of empirical data. This study, as an alternative to traditional travel behavior surveys (i) estimates joint activity detection rates using Google Maps Location History data under experimental conditions, (ii) quantifies the effect magnitude of factors affecting detection probability, and (iii) discusses its potential application to detect joint activities in real social networks. To do so, an experiment was conducted where participants were asked to execute daily schedules designed to simulate daily travel incorporating joint activities. For Android devices, detection rates for 4-person group activities ranged from 22% under the strictest spatiotemporal accuracy criteria to 60% under less strict yet still operational criteria. The performance of iPhones was markedly worse than Android devices, irrespective of accuracy criteria. In addition, logit models were estimated to evaluate factors affecting activity detection given different spatiotemporal accuracy thresholds. In terms of effect magnitudes, non-trivial effects on activity detection probability were found for floor area ratio (FAR) at location, activity duration, Android device ratio, device model ratio, whether the destination was an open space or not, and group size.

Although current activity detection rates are not ideal, these levels must be weighed against the potential of observing travel behavior over long periods of time, and using Google Maps Location History data in conjunction with other data-gathering methodologies to compensate for some of its limitations.

尽管联合活动很重要，但由于缺乏经验数据，在旅行行为分析中仍然很难解释。作为传统旅行行为调查的替代方案，本研究 (i) 在实验条件下使用谷歌地图位置历史数据估计联合活动检测率，(ii) 量化影响检测概率的因素的影响幅度，以及 (iii) 讨论其潜在应用检测真实社交网络中的联合活动。为此，进行了一项实验，要求参与者执行旨在模拟日常旅​​行并结合联合活动的每日时间表。对于 Android 设备，4 人团体活动的检测率从最严格的时空准确度标准下的 22% 到不太严格但仍可操作的标准下的 60% 不等。无论准确性标准如何，iPhone 的性能都明显低于 Android 设备。此外，在给定不同的时空精度阈值的情况下，估计 logit 模型以评估影响活动检测的因素。在影响幅度方面，发现位置的容积率 (FAR)、活动持续时间、Android 设备比率、设备型号比率、目的地是否为开放空间以及群体规模对活动检测概率的非平凡影响.

尽管当前的活动检测率并不理想，但必须权衡这些水平与长期观察旅行行为的潜力，并结合使用谷歌地图位置历史数据和其他数据收集方法来弥补其一些局限性。