IDEAS home Printed from https://ideas.repec.org/a/kap/transp/v50y2023i4d10.1007_s11116-022-10285-w.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11116-022-10285-w
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11116-022-10285-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Marta C. González & César A. Hidalgo & Albert-László Barabási, 2009. "Understanding individual human mobility patterns," Nature, Nature, vol. 458(7235), pages 238-238, March.
    2. 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.
    3. Frank Primerano & Michael Taylor & Ladda Pitaksringkarn & Peter Tisato, 2008. "Defining and understanding trip chaining behaviour," Transportation, Springer, vol. 35(1), pages 55-72, January.
    4. 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.
    5. 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.
    6. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Su, Rongxiang & Xiao, Jingyi & McBride, Elizabeth C. & Goulias, Konstadinos G., 2021. "Understanding senior's daily mobility patterns in California using human mobility motifs," Journal of Transport Geography, Elsevier, vol. 94(C).
    2. Harsh Shah & Andre L. Carrel & Huyen T. K. Le, 2024. "Impacts of teleworking and online shopping on travel: a tour-based analysis," Transportation, Springer, vol. 51(1), pages 99-127, February.
    3. Ed Manley & Chen Zhong & Michael Batty, 2018. "Spatiotemporal variation in travel regularity through transit user profiling," Transportation, Springer, vol. 45(3), pages 703-732, May.
    4. Mohiuddin, Hossain & Fitch-Polse, Dillon T. PhD, 2024. "Is Micromobility Being Used in Place of Car Trips in Daily Travel (or “Trip Chains”)?," Institute of Transportation Studies, Working Paper Series qt2r66k788, Institute of Transportation Studies, UC Davis.
    5. Timmer, Sebastian & Merfeld, Katrin & Henkel, Sven, 2023. "Exploring motivations for multimodal commuting: A hierarchical means-end chain analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 176(C).
    6. 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.
    7. Jeong-Hui Park & Eunhye Yoo & Youngdeok Kim & Jung-Min Lee, 2021. "What Happened Pre- and during COVID-19 in South Korea? Comparing Physical Activity, Sleep Time, and Body Weight Status," IJERPH, MDPI, vol. 18(11), pages 1-13, May.
    8. Matteo Böhm & Mirco Nanni & Luca Pappalardo, 2022. "Gross polluters and vehicle emissions reduction," Nature Sustainability, Nature, vol. 5(8), pages 699-707, August.
    9. Robert Stewart & Marie Urban & Samantha Duchscherer & Jason Kaufman & April Morton & Gautam Thakur & Jesse Piburn & Jessica Moehl, 2016. "A Bayesian machine learning model for estimating building occupancy from open source data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(3), pages 1929-1956, April.
    10. Arroyo Arroyo,Fatima & Fernandez Gonzalez,Marta & Matekenya,Dunstan & Espinet Alegre,Xavier, 2021. "Using Mobile Data to Understand Urban Mobility Patterns in Freetown, Sierra Leone," Policy Research Working Paper Series 9519, The World Bank.
    11. David Kofoed Wind & Piotr Sapiezynski & Magdalena Anna Furman & Sune Lehmann, 2016. "Inferring Stop-Locations from WiFi," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-15, February.
    12. Allan Pimenta & Liton (Md) Kamruzzaman, 2024. "What About Land Uses in Mobility Hub Planning for Sustainable Travel Behavior?," Sustainability, MDPI, vol. 16(20), pages 1-22, October.
    13. Subbarao, S.S.V. & Krishna Rao, K,V., 2013. "Trip Chaining Behavior in Developing Countries: A Study of Mumbai Metropolitan Region, India," European Transport \ Trasporti Europei, ISTIEE, Institute for the Study of Transport within the European Economic Integration, issue 53, pages 1-7.
    14. Zhou, Xingang & Yeh, Anthony G.O. & Yue, Yang, 2018. "Spatial variation of self-containment and jobs-housing balance in Shenzhen using cellphone big data," Journal of Transport Geography, Elsevier, vol. 68(C), pages 102-108.
    15. Maxime Lenormand & Miguel Picornell & Oliva G Cantú-Ros & Antònia Tugores & Thomas Louail & Ricardo Herranz & Marc Barthelemy & Enrique Frías-Martínez & José J Ramasco, 2014. "Cross-Checking Different Sources of Mobility Information," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-10, August.
    16. Miotti, Marco & Needell, Zachary A. & Jain, Rishee K., 2023. "The impact of urban form on daily mobility demand and energy use: Evidence from the United States," Applied Energy, Elsevier, vol. 339(C).
    17. Zheng Yan & Wenqian Robertson & Yaosheng Lou & Tom W. Robertson & Sung Yong Park, 2021. "Finding leading scholars in mobile phone behavior: a mixed-method analysis of an emerging interdisciplinary field," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(12), pages 9499-9517, December.
    18. Huang, Feihu & Qiao, Shaojie & Peng, Jian & Guo, Bing & Xiong, Xi & Han, Nan, 2019. "A movement model for air passengers based on trip purpose," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 798-808.
    19. Duan, Zhengyu & Zhao, Haoran & Li, Zhenming, 2023. "Non-linear effects of built environment and socio-demographics on activity space," Journal of Transport Geography, Elsevier, vol. 111(C).
    20. Vanky, Anthony & Courtney, Theodore & Verma, Santosh & Ratti, Carlo, 2016. "One to Many: Opportunities to Understanding Collective Behaviors in Urban Environments Through Individual's Passively-Collected Locative Data," SocArXiv f7mpd, Center for Open Science.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:transp:v:50:y:2023:i:4:d:10.1007_s11116-022-10285-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.