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Mobile phone data in transportation research: methods for benchmarking against other data sources

Author

Listed:
  • Andreas Dypvik Landmark

    (SINTEF)

  • Petter Arnesen

    (SINTEF)

  • Carl-Johan Södersten

    (SINTEF)

  • Odd André Hjelkrem

    (SINTEF)

Abstract

The ubiquity of personal cellular phones in society has led to a surging interest in using Big Data generated by mobile phones in transport research. Studies have suggested that the vast amount of data could be used to estimate origin–destination (OD) matrices, thereby potentially replacing traditional data sources such as travel surveys. However, constructing OD matrices from mobile phone data (MPD) entails multiple challenges, and the lack of ground truth hampers the evaluation and validation of the estimated matrices. Furthermore, national laws may prohibit the distribution of MPD for research purposes, compelling researchers to work with pre-compiled OD matrices with no insight into the methods used. In this paper, we analyse a set of such pre-compiled OD matrices from the greater Oslo area and perform validation procedures against several sources to assess the quality and robustness of the OD matrices as well as their usefulness in transportation planning applications. We find that while the OD matrices correlate well with other sources at a low resolution, the reliability decreases when a finer level of detail is chosen, particularly when comparing shorter trips between neighbouring areas. Our results suggest that coarseness of data and privacy concerns restrict the usefulness of MPD in transport research in the case where OD matrices are pre-compiled by the operator.

Suggested Citation

  • Andreas Dypvik Landmark & Petter Arnesen & Carl-Johan Södersten & Odd André Hjelkrem, 2021. "Mobile phone data in transportation research: methods for benchmarking against other data sources," Transportation, Springer, vol. 48(5), pages 2883-2905, October.
    • Handle: RePEc:kap:transp:v:48:y:2021:i:5:d:10.1007_s11116-020-10151-7
    DOI: 10.1007/s11116-020-10151-7
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    References listed on IDEAS

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    2. Mofeng Yang & Yixuan Pan & Aref Darzi & Sepehr Ghader & Chenfeng Xiong & Lei Zhang, 2022. "A data-driven travel mode share estimation framework based on mobile device location data," Transportation, Springer, vol. 49(5), pages 1339-1383, October.

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