IDEAS home Printed from https://ideas.repec.org/p/hal/journl/halshs-03148937.html
   My bibliography  Save this paper

Investigating day-to-day variability of transit usage on a multimonth scale with smart card data. A case study in Lyon

Author

Listed:
  • Oscar Egu

    (LAET - Laboratoire Aménagement Économie Transports - UL2 - Université Lumière - Lyon 2 - ENTPE - École Nationale des Travaux Publics de l'État - CNRS - Centre National de la Recherche Scientifique)

  • Patrick Bonnel

    (LAET - Laboratoire Aménagement Économie Transports - UL2 - Université Lumière - Lyon 2 - ENTPE - École Nationale des Travaux Publics de l'État - CNRS - Centre National de la Recherche Scientifique)

Abstract

To examine the variability of travel behaviour over time, transportation researchers need to collect longitudinal data. The first studies around day-to-day variability of travel behaviour were based on surveys. Those studies have shown that there is considerable variation in individual travel behaviour. They have also discussed the implications of this variability in terms of modelling, policy evaluation or marketing. Recently, the multiplication of big data has led to an explosion in the number of studies about travel behaviour. This is because those new data sources collect lots of data, about lots of people over long periods. In the field of public transit, smart card data is one of those big data sources. They have been used by various authors to conduct longitudinal analyses of transit usage behaviour. However, researchers working with smart card data mostly rely on clustering techniques to measure variability, and they often use conceptual framework different from those of transportation researchers familiar with traditional data sources. In particular, there is no study based on smart card data that explicitly measure day-to-day intrapersonal variability of transit usage. Therefore, the purpose of this investigation is to address this gap. To do this, a clustering method and a similarity metric are combined to explore simultaneously interpersonal and intrapersonal variability of transit usage. The application is done with a rich dataset covering a 6 months period (181 days) and it contributes to the growing literature on smart card data. Results of this research confirm previous works based on survey data and show that there is no one size fits all approach to the problem of day-to-day variability of transit usage. They also prove that combining clustering algorithm with day-to-day intrapersonal similarity metric is a valuable tool to mine smart card data. The findings of this study can help in identifying new passenger segmentation and in tailoring information and services.

Suggested Citation

  • Oscar Egu & Patrick Bonnel, 2020. "Investigating day-to-day variability of transit usage on a multimonth scale with smart card data. A case study in Lyon," Post-Print halshs-03148937, HAL.
    • Handle: RePEc:hal:journl:halshs-03148937
    DOI: 10.1016/j.tbs.2019.12.003
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-03148937
    as

    Download full text from publisher

    File URL: https://shs.hal.science/halshs-03148937/document
    Download Restriction: no
    File URL: https://libkey.io/10.1016/j.tbs.2019.12.003?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
    ---><---

    References listed on IDEAS

    as
    1. Tommy Gärling & Kay Axhausen, 2003. "Introduction: Habitual travel choice," Transportation, Springer, vol. 30(1), pages 1-11, February.
    2. Bagchi, M. & White, P.R., 2005. "The potential of public transport smart card data," Transport Policy, Elsevier, vol. 12(5), pages 464-474, September.
    3. Charles Raux & Tai-Yu Ma & Eric Cornelis, 2016. "Variability in daily activity-travel patterns: the case of a one-week travel diary," Post-Print halshs-01389479, HAL.
    4. Yusak Susilo & Kay Axhausen, 2014. "Repetitions in individual daily activity–travel–location patterns: a study using the Herfindahl–Hirschman Index," Transportation, Springer, vol. 41(5), pages 995-1011, September.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Uğur Baç, 2020. "An Integrated SWARA-WASPAS Group Decision Making Framework to Evaluate Smart Card Systems for Public Transportation," Mathematics, MDPI, vol. 8(10), pages 1-24, October.
    2. Liu, Shasha & Yamamoto, Toshiyuki & Yao, Enjian & Nakamura, Toshiyuki, 2021. "Examining public transport usage by older adults with smart card data: A longitudinal study in Japan," Journal of Transport Geography, Elsevier, vol. 93(C).
    3. Cong Liao & Teqi Dai, 2022. "Is “Attending Nearby School” Near? An Analysis of Travel-to-School Distances of Primary Students in Beijing Using Smart Card Data," Sustainability, MDPI, vol. 14(7), pages 1-12, April.
    4. Zahnow, Renee & Abewickrema, Wanuji, 2023. "Examining regularity in vehicular traffic through Bluetooth scanner data: Is the daily commuter the regular road user?," Journal of Transport Geography, Elsevier, vol. 109(C).
    5. Gao, Jie & He, Sylvia Y. & Ettema, Dick & Helbich, Marco, 2023. "Travel behavior changes due to life events: Longitudinal evidence from Dutch couple households," Transportation Research Part A: Policy and Practice, Elsevier, vol. 175(C).

    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. Benito Zaragozí & Sergio Trilles & Aaron Gutiérrez & Daniel Miravet, 2021. "Development of a Common Framework for Analysing Public Transport Smart Card Data," Energies, MDPI, vol. 14(19), pages 1-22, September.
    2. Xia Zhao & Mengying Cui & David Levinson, 2023. "Exploring temporal variability in travel patterns on public transit using big smart card data," Environment and Planning B, , vol. 50(1), pages 198-217, January.
    3. Thomas, Tom & La Paix Puello, Lissy & Geurs, Karst, 2019. "Intrapersonal mode choice variation: Evidence from a four-week smartphone-based travel survey in the Netherlands," Journal of Transport Geography, Elsevier, vol. 76(C), pages 287-300.
    4. Gutiérrez, Aaron & Domènech, Antoni & Zaragozí, Benito & Miravet, Daniel, 2020. "Profiling tourists' use of public transport through smart travel card data," Journal of Transport Geography, Elsevier, vol. 88(C).
    5. 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.
    6. Liao, Cong & Scheuer, Bronte, 2022. "Evaluating the performance of transit-oriented development in Beijing metro station areas: Integrating morphology and demand into the node-place model," Journal of Transport Geography, Elsevier, vol. 100(C).
    7. Lovejoy, Kristin, 2012. "Mobility Fulfillment Among Low-car Households: Implications for Reducing Auto Dependence in the United States," Institute of Transportation Studies, Working Paper Series qt4v44b5qn, Institute of Transportation Studies, UC Davis.
    8. Edmond Daramy-Williams & Jillian Anable & Susan Grant-Muller, 2019. "Car Use: Intentional, Habitual, or Both? Insights from Anscombe and the Mobility Biography Literature," Sustainability, MDPI, vol. 11(24), pages 1-17, December.
    9. Heinen, Eva & Chatterjee, Kiron, 2015. "The same mode again? An exploration of mode choice variability in Great Britain using the National Travel Survey," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 266-282.
    10. Toşa, Cristian & Sato, Hitomi & Morikawa, Takayuki & Miwa, Tomio, 2018. "Commuting behavior in emerging urban areas: Findings of a revealed-preferences and stated-intentions survey in Cluj-Napoca, Romania," Journal of Transport Geography, Elsevier, vol. 68(C), pages 78-93.
    11. Kazagli, Evanthia & de Lapparent, Matthieu, 2023. "A discrete choice modeling framework of heterogenous decision rules accounting for non-trading behavior," Journal of choice modelling, Elsevier, vol. 48(C).
    12. Chen, Ching-Fu & Lai, Wen-Tai, 2011. "The effects of rational and habitual factors on mode choice behaviors in a motorcycle-dependent region: Evidence from Taiwan," Transport Policy, Elsevier, vol. 18(5), pages 711-718, September.
    13. Els Hannes & Diana Kusumastuti & Maikel Espinosa & Davy Janssens & Koen Vanhoof & Geert Wets, 2012. "Mental maps and travel behaviour: meanings and models," Journal of Geographical Systems, Springer, vol. 14(2), pages 143-165, April.
    14. Kevin Credit & Zander Arnao, 2023. "A method to derive small area estimates of linked commuting trips by mode from open source LODES and ACS data," Environment and Planning B, , vol. 50(3), pages 709-722, March.
    15. Benjamin Motte-Baumvol & Julie Fen-Chong & Olivier Bonin, 2023. "Immobility in a weekly mobility routine: studying the links between mobile and immobile days for employees and retirees," Transportation, Springer, vol. 50(5), pages 1723-1742, October.
    16. Delbosc, Alexa & Nakanishi, Hitomi, 2017. "A life course perspective on the travel of Australian millennials," Transportation Research Part A: Policy and Practice, Elsevier, vol. 104(C), pages 319-336.
    17. Christian Martin Mützel & Joachim Scheiner, 2022. "Investigating spatio-temporal mobility patterns and changes in metro usage under the impact of COVID-19 using Taipei Metro smart card data," Public Transport, Springer, vol. 14(2), pages 343-366, June.
    18. Chunguang Liu & Xinyu Zuo & Xiaoning Gu & Mengru Shao & Chao Chen, 2023. "Activity Duration under the COVID-19 Pandemic: A Comparative Analysis among Different Urbanized Areas Using a Hazard-Based Duration Model," Sustainability, MDPI, vol. 15(12), pages 1-28, June.
    19. Kevin Maréchal, 2018. "Recasting the understanding of habits for behaviour-oriented policies in transportation," ULB Institutional Repository 2013/270475, ULB -- Universite Libre de Bruxelles.
    20. Caspar G. Chorus & Benedict G. C. Dellaert, 2012. "Travel Choice Inertia: The Joint Role of Risk Aversion and Learning," Journal of Transport Economics and Policy, University of Bath, vol. 46(1), pages 139-155, January.

    More about this item

    Keywords

    Public transit; Travel behavior; Smart card data; Passenger clustering; Day-to-day variability; User segmentation;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:hal:journl:halshs-03148937. 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: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    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.