Highlights This is one of the earliest efforts to identify residency and detailed worker types from passively collected location data. The study features a large-scale national-level mobile device location dataset (MDLD) covering the 50 U.S. states and D.C. The paper identifies normal commuters, professional drivers, mobility-for-work workers, and nonworkers/home-based workers. The paper addresses the passive data sample biases and evaluated the travel demand patterns by worker types. The unique innovations of this study contribute to the development of MDLD-based passenger travel demand estimation.

    Abstract 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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Residency and worker status identification based on mobile device location data


    Contributors:


    Publication date :

    2022-11-13




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Surgical residency training in the mission setting: current status and future directions

    James D Smith / Dan Poenaru / David Thompson et al. | DOAJ | 2016

    Free access


    IDENTIFICATION BASED ON A LOCATION OF A MOBILE COMMUNICATIONS DEVICE

    TIMSON ANTHONY | European Patent Office | 2024

    Free access

    IDENTIFICATION BASED ON A LOCATION OF A MOBILE COMMUNICATIONS DEVICE

    TIMSON ANTHONY | European Patent Office | 2024

    Free access

    Identification based on a location of a mobile communications device

    ANTHONY TIMSON | European Patent Office | 2024

    Free access