Understanding human mobility within urban settings is fundamental for urban and transport planning. Travel demand modeling and planning typically rely on data that are collected from large-scale household travel surveys (i.e., origin–destination surveys) and compiled into single- or multiple-day travel diaries. The laborious task of collecting these data has left traditional methods with numerous limitations, resulting in significant trade-offs in regard to accuracy, sample size, and study duration, while also being vulnerable to reporting and transcription error. Rising mobile phone ownership has provided opportunities to acquire expansive cellular network data from service providers and location-based service data through smartphone applications. At the same time, the Google Maps smartphone application provides built-in infrastructure that can passively collect detailed location information from user smartphone devices. The resulting data are known as Google location history (GLH). To better understand the potential of these data offerings in transportation modeling and planning, GLH data passively collected from five different smartphones following prescribed itineraries over 12 days was evaluated. As 51% of 934 locations and 32% of 888 trips were matched to the pre-determined travel diary data, it was determined that GLH data does not currently appear to be an adequate tool for travel diary data collection. On average, locations that were missed by GLH were shorter (mean of 355 s), whereas locations that were identified were longer (mean of 762 s).

了解城市环境中的人员流动对于城市和交通规划至关重要。出行需求建模和规划通常依赖于从大规模家庭出行调查（即出发地-目的地调查）中收集的数据，并被整理成单日或多日旅行日记。收集这些数据的艰巨任务使传统方法具有许多局限性，导致在准确性，样本量和研究持续时间方面做出重大权衡，同时还容易受到报告和转录错误的影响。移动电话拥有量的上升为通过智能手机应用程序从服务提供商获取广泛的蜂窝网络数据和基于位置的服务数据提供了机会。与此同时，Google Maps智能手机应用程序提供了内置的基础架构，可以从用户智能手机设备被动收集详细的位置信息。产生的数据称为Google位置记录（GLH）。为了更好地了解这些数据产品在交通运输建模和规划中的潜力，我们评估了在12天的预定行程后从五种不同的智能手机被动收集的GLH数据。由于934个地点中的51％和888次旅行中的32％与预定的旅行日记数据相匹配，因此可以确定GLH数据当前似乎不是旅行日记数据收集的适当工具。平均而言，GLH遗漏的位置较短（平均355 s），而识别出的位置较长（平均762 s）。