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Passive Mobile Phone Dataset to Construct Origin-destination Matrix: Potentials and Limitations

Author

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  • Patrick Bonnel

    (LET - Laboratoire d'économie des transports - UL2 - Université Lumière - Lyon 2 - ENTPE - École Nationale des Travaux Publics de l'État - CNRS - Centre National de la Recherche Scientifique)

  • Etienne Hombourger

    (Cerema Direction Est - Centre d'Etudes et d'Expertise sur les Risques, l'Environnement, la Mobilité et l'Aménagement - Direction Est - Cerema - Centre d'Etudes et d'Expertise sur les Risques, l'Environnement, la Mobilité et l'Aménagement)

  • Ana-Maria Olteanu-Raimond

    (COGIT - Cartographie et Géomatique - LaSTIG - Laboratoire des Sciences et Technologies de l'Information Géographique - ENSG - École nationale des sciences géographiques - IGN - Institut National de l'Information Géographique et Forestière [IGN])

  • Zbigniew Smoreda

    (Orange Labs [Issy les Moulineaux] - France Télécom)

Abstract

Mobile phone operators produce enormous amounts of data. In this paper we present applications performed with a dataset (communication events + handover and Location Area Up-date) collected by the operator Orange from 31 March to 11 April 2009 for the whole Paris Region. Trips are deduced from the spatio-temporal trajectory of devices through a hypothesis of stationarity within a Location Area in order to define activities. Trips are then aggregated in an origin-destination matrix which is compared with traditional data (census data and household travel survey).

Suggested Citation

  • Patrick Bonnel & Etienne Hombourger & Ana-Maria Olteanu-Raimond & Zbigniew Smoreda, 2015. "Passive Mobile Phone Dataset to Construct Origin-destination Matrix: Potentials and Limitations," Post-Print halshs-01664219, HAL.
  • Handle: RePEc:hal:journl:halshs-01664219
    DOI: 10.1016/j.trpro.2015.12.032
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-01664219
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    References listed on IDEAS

    as
    1. Morency, Catherine & Trépanier, Martin & Agard, Bruno, 2007. "Measuring transit use variability with smart-card data," Transport Policy, Elsevier, vol. 14(3), pages 193-203, May.
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    3. Santi Phithakkitnukoon & Zbigniew Smoreda & Patrick Olivier, 2012. "Socio-Geography of Human Mobility: A Study Using Longitudinal Mobile Phone Data," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-9, June.
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    5. Arana, P. & Cabezudo, S. & Peñalba, M., 2014. "Influence of weather conditions on transit ridership: A statistical study using data from Smartcards," Transportation Research Part A: Policy and Practice, Elsevier, vol. 59(C), pages 1-12.
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    Cited by:

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    2. Thomas Feilhauer & Florian Braun & Katja Faller & David Hutter & Daniel Mathis & Johannes Neubauer & Jasmin Pogatschneg & Michelle Weber, 2021. "Mobility Choices—An Instrument for Precise Automatized Travel Behavior Detection & Analysis," Sustainability, MDPI, vol. 13(4), pages 1-23, February.
    3. Mariem Fekih & Tom Bellemans & Zbigniew Smoreda & Patrick Bonnel & Angelo Furno & Stéphane Galland, 2021. "A data-driven approach for origin–destination matrix construction from cellular network signalling data: a case study of Lyon region (France)," Transportation, Springer, vol. 48(4), pages 1671-1702, August.
    4. Marko Šoštarić & Krešimir Vidović & Marijan Jakovljević & Orsat Lale, 2021. "Data-Driven Methodology for Sustainable Urban Mobility Assessment and Improvement," Sustainability, MDPI, vol. 13(13), pages 1-22, June.
    5. Ballis, Haris & Dimitriou, Loukas, 2020. "Revealing personal activities schedules from synthesizing multi-period origin-destination matrices," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 224-258.
    6. Alattar, Mohammad Anwar & Cottrill, Caitlin & Beecroft, Mark, 2021. "Public participation geographic information system (PPGIS) as a method for active travel data acquisition," Journal of Transport Geography, Elsevier, vol. 96(C).

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