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    Evaluation of home detection algorithms on mobile phone data using individual-level ground truth
    (2021)
    Luca Pappalardo
    ;
    FERRES, LEONARDO ADRIÁN  
    ;
    Manuel Sacasa
    ;
    Ciro Cattuto
    ;
    BRAVO CELEDÓN, MARÍA LORETO  
    Inferring mobile phone users’ home location, i.e., assigning a location in space to a user based on data generated by the mobile phone network, is a central task in leveraging mobile phone data to study social and urban phenomena. Despite its widespread use, home detection relies on assumptions that are difficult to check without ground truth, i.e., where the individual who owns the device resides. In this paper, we present a dataset that comprises the mobile phone activity of sixty-five participants for whom the geographical coordinates of their residence location are known. The mobile phone activity refers to Call Detail Records (CDRs), eXtended Detail Records (XDRs), and Control Plane Records (CPRs), which vary in their temporal granularity and differ in the data generation mechanism. We provide an unprecedented evaluation of the accuracy of home detection algorithms and quantify the amount of data needed for each stream to carry out successful home detection for each stream. Our work is useful for researchers and practitioners to minimize data requests and maximize the accuracy of the home antenna location.
    Scopus© Citations 35  3
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    Adoption-Driven Data Science for Transportation Planning: Methodology, Case Study, and Lessons Learned
    (2020)
    Eduardo Graells-Garrido
    ;
    Vanessa Peña-Araya
    ;
    BRAVO CELEDÓN, MARÍA LORETO  
    The rising availability of digital traces provides a fertile ground for data-driven solutions to problems in cities. However, even though a massive data set analyzed with data science methods may provide a powerful and cost-effective solution to a problem, its adoption by relevant stakeholders is not guaranteed due to adoption barriers such as lack of interpretability and interoperability. In this context, this paper proposes a methodology toward bridging two disciplines, data science and transportation, to identify, understand, and solve transportation planning problems with data-driven solutions that are suitable for adoption by urban planners and policy makers. The methodology is defined by four steps where people from both disciplines go from algorithm and model definition to the development of a potentially adoptable solution with evaluated outputs. We describe how this methodology was applied to define a model to infer commuting trips with mode of transportation from mobile phone data, and we report the lessons learned during the process.
    Scopus© Citations 6  3
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    Toward An Interdisciplinary Methodology to Solve New (Old) Transportation Problems
    (2020)
    Eduardo Graells-garrido
    ;
    Vanessa Peña-Araya
    Scopus© Citations 1  1