Mobile device location data (MDLD) have been widely recognized for their rich human mobility information and thus considered as a supplementary data source for the current travel data bank consisting of travel survey data and traffic monitoring data. However, the lack of ground truth information about the device owners raises concern about the biases and representativeness of the nonprobability MDLD sample and significantly limits the applications of MDLD. This paper focuses on identifying two important socio-demographic characteristics for the MDLD sample devices: residency and worker status, including four worker types (normal commuters, professional drivers, mobility-for-work workers, and nonworkers/home-based workers). Based on the spatial–temporal patterns of location sightings and derived trips from MDLD, a comprehensive imputation framework is proposed with parameters calibrated against public domain ground truth data. A national-level case study in the U.S. based on a commercial MDLD dataset further evaluates the performances of the proposed heuristic rules. The multi-level validation results indicate a satisfying match against the ground truth data and prove the effectiveness of the proposed methods. As one of the earliest efforts to identify the residency and worker status information for a large-scale national-level MDLD dataset, mobile workers—including professional drivers and mobility-for-work workers—are also identified from MDLD for the first time.

移动设备位置数据 (MDLD) 因其丰富的人类移动信息而得到广泛认可，因此被视为当前由旅行调查数据和交通监控数据组成的旅行数据库的补充数据源。然而，缺乏关于设备所有者的真实信息引起了人们对非概率 MDLD 样本的偏差和代表性的担忧，并显着限制了 MDLD 的应用。本文侧重于确定 MDLD 样本设备的两个重要社会人口特征：居住地和工人身份，包括四种工人类型（正常通勤者、专业司机、流动工作工人和非工人/家庭工人）。基于位置目击的时空模式和来自 MDLD 的衍生旅行，提出了一个全面的插补框架，其中的参数根据公共领域的地面实况数据进行了校准。美国基于商业 MDLD 数据集的国家级案例研究进一步评估了所提出的启发式规则的性能。多级验证结果表明与地面实况数据的匹配令人满意，证明了所提出方法的有效性。作为为大型国家级 MDLD 数据集识别居住和工人身份信息的最早努力之一，流动工人——包括专业司机和流动工作工人——也首次从 MDLD 中识别出来。基于商业 MDLD 数据集进一步评估所提出的启发式规则的性能。多级验证结果表明与地面实况数据的匹配令人满意，证明了所提出方法的有效性。作为为大型国家级 MDLD 数据集识别居住和工人身份信息的最早努力之一，流动工人——包括专业司机和流动工作工人——也首次从 MDLD 中识别出来。基于商业 MDLD 数据集进一步评估所提出的启发式规则的性能。多级验证结果表明与地面实况数据的匹配令人满意，证明了所提出方法的有效性。作为为大型国家级 MDLD 数据集识别居住和工人身份信息的最早努力之一，流动工人——包括专业司机和流动工作工人——也首次从 MDLD 中识别出来。