Real-time large-scale map matching using mobile phone data

Essam Algizawy, Tetsuji Ogawa, Ahmed El-Mahdy

    研究成果: Article

    12 引用 (Scopus)

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    With the wide spread use of mobile phones, cellular mobile big data is becoming an important resource that provides a wealth of information with almost no cost. However, the data generally suffers from relatively high spatial granularity, limiting the scope of its application. In this article, we consider, for the first time, the utility of actual mobile big data for map matching allowing for “microscopic” level traffic analysis. The state-of-the-art in map matching generally targets GPS data, which provides far denser sampling and higher location resolution than the mobile data. Our approach extends the typical Hidden-Markov model used in map matching to accommodate for highly sparse location trajectories, exploit the large mobile data volume to learn the model parameters, and exploit the sparsity of the data to provide for real-time Viterbi processing. We study an actual, anonymised mobile trajectories data set of the city of Dakar, Senegal, spanning a year, and generate a corresponding road-level traffic density, at an hourly granularity, for each mobile trajectory. We observed a relatively high correlation between the generated traffic intensities and corresponding values obtained by the gravity and equilibrium models typically used in mobility analysis, indicating the utility of the approach as an alternative means for traffic analysis.

    元の言語English
    記事番号52
    ジャーナルACM Transactions on Knowledge Discovery from Data
    11
    発行部数4
    DOI
    出版物ステータスPublished - 2017 7 1

    ASJC Scopus subject areas

    • Computer Science(all)

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