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What are the multimodal patterns of individual mobility at the day level in the Paris region? A two-stage data-driven approach based on the 2018 Household Travel Survey

Author

Listed:
  • Biao Yin

    (Ecole des Ponts ParisTech-Univ Gustave Eiffel)

  • Fabien Leurent

    (Centre International de Recherche sur l’Environnement et le Développement)

Abstract

Human mobility patterns and their socio-demographic association have been widely studied on travel behavior analysis. To better suit the services of multimodal transport systems, people’s travel behavior needs to be examined at different levels, concerning the complexity of their multimodal trips or trip-chains. This article aims to reveal multimodal patterns of individual mobility and their relationships with socio-demographic characteristics and with travel complexities based on the 2018 Household Travel Survey in the Paris region. To identify the multimodal patterns, a two-stage statistical analysis is conducted. At the first stage (at the trip level), fifteen trip types are identified depending on the categories of travel modes and the degrees of modal trip lengths, duration, and departure time. At the second stage (at the day level), the individuals are characterized by their mobility profiles that are interpreted with the respective frequencies of the fifteen trip types on the day. Based on the profiles, six clusters, i.e., six daily mobility patterns, are obtained. Among the patterns, the daily travel distances vary widely (from 1 to 7 times), as do the daily travel time budgets (from 1 to 3 times). From the relationship analysis, we find that the obtained mobility patterns come along with specific features of car ownership and transit subscription. Socio-demographic associations to the clusters are also distinct. The daily mobility patterns demonstrate an adverse correlation between the trip complexity and the trip chain complexity. The findings in this study could help policy makers to implement concrete strategies for targeted people at multimodality circumstances.

Suggested Citation

  • Biao Yin & Fabien Leurent, 2023. "What are the multimodal patterns of individual mobility at the day level in the Paris region? A two-stage data-driven approach based on the 2018 Household Travel Survey," Transportation, Springer, vol. 50(4), pages 1497-1526, August.
    • Handle: RePEc:kap:transp:v:50:y:2023:i:4:d:10.1007_s11116-022-10285-w
    DOI: 10.1007/s11116-022-10285-w
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    References listed on IDEAS

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    1. Frank Primerano & Michael Taylor & Ladda Pitaksringkarn & Peter Tisato, 2008. "Defining and understanding trip chaining behaviour," Transportation, Springer, vol. 35(1), pages 55-72, January.
    2. Montserrat Miramontes & Maximilian Pfertner & Hema Sharanya Rayaprolu & Martin Schreiner & Gebhard Wulfhorst, 2017. "Impacts of a multimodal mobility service on travel behavior and preferences: user insights from Munich’s first Mobility Station," Transportation, Springer, vol. 44(6), pages 1325-1342, November.
    3. Marion Leroutier & Philippe Quirion, 2021. "Tackling Transport-Induced Pollution in Cities: A case Study in Paris," Working Papers 2021.07, FAERE - French Association of Environmental and Resource Economists.
    4. Marta C. González & César A. Hidalgo & Albert-László Barabási, 2009. "Understanding individual human mobility patterns," Nature, Nature, vol. 458(7235), pages 238-238, March.
    5. Florian Schneider & Danique Ton & Lara-Britt Zomer & Winnie Daamen & Dorine Duives & Sascha Hoogendoorn-Lanser & Serge Hoogendoorn, 2021. "Trip chain complexity: a comparison among latent classes of daily mobility patterns," Transportation, Springer, vol. 48(2), pages 953-975, April.
    6. Vrtic, M. & Fröhlich, P. & Schüssler, N. & Axhausen, K.W. & Lohse, D. & Schiller, C. & Teichert, H., 2007. "Two-dimensionally constrained disaggregate trip generation, distribution and mode choice model: Theory and application for a Swiss national model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(9), pages 857-873, November.
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