IDEAS home Printed from https://ideas.repec.org/a/eee/transa/v165y2022icp321-336.html
   My bibliography  Save this article

CIAM: A data-driven approach for classifying long-term engagement of public transport riders at multiple temporal scales

Author

Listed:
  • Cardell-Oliver, Rachel
  • Olaru, Doina

Abstract

Many human activities, including daily travel, show a mix of stable, intermittent and changing patterns in demand by individuals over time. However, the lack of continuous, long-term, passenger-linked data for public transport (PT) journeys means that we do not know how passenger ridership evolves in real-world networks. This paper proposes the CIAM model for classifying long-term passenger engagement with PT. CIAM is a data-driven model combining year-on-year churn (C), monthly intensity (I), annual (A) and multi-year (M) engagement. Parameter search algorithms are used to ensure that the learned features are distinctive and robust. We evaluated CIAM using a 5-year dataset from a PT network with over 300 million journeys. CIAM identified distinct patterns of long-term ridership at multiple time scales. Although the total number of annual journeys was relatively stable over the five years, we found long-term differences between passenger subgroups. Churn of passengers was a major factor in ridership with only 55% of passengers retained from year to year. Patterns of annual engagement are often intermittent, so short-term snapshots of a few weeks are typically not good indicators for longer term engagement. Only 27% of high-frequency, full-fare riders still have the same level of engagement four years later, compared with 55% who continue high-frequency engagement after only one year.

Suggested Citation

  • Cardell-Oliver, Rachel & Olaru, Doina, 2022. "CIAM: A data-driven approach for classifying long-term engagement of public transport riders at multiple temporal scales," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 321-336.
    • Handle: RePEc:eee:transa:v:165:y:2022:i:c:p:321-336
    DOI: 10.1016/j.tra.2022.09.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0965856422002300
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tra.2022.09.002?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. Müggenburg, Hannah & Busch-Geertsema, Annika & Lanzendorf, Martin, 2015. "Mobility biographies: A review of achievements and challenges of the mobility biographies approach and a framework for further research," Journal of Transport Geography, Elsevier, vol. 46(C), pages 151-163.
    2. Rafiq, Rezwana & McNally, Michael G., 2021. "Heterogeneity in Activity-travel Patterns of Public Transit Users: An Application of Latent Class Analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 152(C), pages 1-18.
    3. 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.
    4. Pengfei Lin & Jiancheng Weng & Dimitrios Alivanistos & Siyong Ma & Baocai Yin, 2020. "Identifying and Segmenting Commuting Behavior Patterns Based on Smart Card Data and Travel Survey Data," Sustainability, MDPI, vol. 12(12), pages 1-18, June.
    5. Collins, Patricia A. & MacFarlane, Robert, 2018. "Evaluating the determinants of switching to public transit in an automobile-oriented mid-sized Canadian city: A longitudinal analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 118(C), pages 682-695.
    6. Ayako Taniguchi & Satoshi Fujii, 2007. "Promoting Public Transport Using Marketing Techniques in Mobility Management and Verifying their Quantitative Effects," Transportation, Springer, vol. 34(1), pages 37-49, January.
    7. Deng, Yue & Wang, Jiaxin & Gao, Chao & Li, Xianghua & Wang, Zhen & Li, Xuelong, 2021. "Assessing temporal–spatial characteristics of urban travel behaviors from multiday smart-card data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 576(C).
    8. Tommy Gärling & Kay Axhausen, 2003. "Introduction: Habitual travel choice," Transportation, Springer, vol. 30(1), pages 1-11, February.
    9. Ma, Zhenliang & Koutsopoulos, Haris N. & Liu, Tianyou & Basu, Abhishek Arunasis, 2020. "Behavioral response to promotion-based public transport demand management: Longitudinal analysis and implications for optimal promotion design," Transportation Research Part A: Policy and Practice, Elsevier, vol. 141(C), pages 356-372.
    10. Zhanhong Cheng & Martin Trépanier & Lijun Sun, 2021. "Probabilistic model for destination inference and travel pattern mining from smart card data," Transportation, Springer, vol. 48(4), pages 2035-2053, August.
    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. 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.
    2. Busch-Geertsema, Annika & Lanzendorf, Martin, 2017. "From university to work life – Jumping behind the wheel? Explaining mode change of students making the transition to professional life," Transportation Research Part A: Policy and Practice, Elsevier, vol. 106(C), pages 181-196.
    3. Büchel, Beda & Marra, Alessio Daniele & Corman, Francesco, 2022. "COVID-19 as a window of opportunity for cycling: Evidence from the first wave," Transport Policy, Elsevier, vol. 116(C), pages 144-156.
    4. de Haas, M.C. & Scheepers, C.E. & Harms, L.W.J. & Kroesen, M., 2018. "Travel pattern transitions: Applying latent transition analysis within the mobility biographies framework," Transportation Research Part A: Policy and Practice, Elsevier, vol. 107(C), pages 140-151.
    5. Doody, Brendan J., 2020. "Becoming ‘a Londoner’: Migrants’ experiences and habits of everyday (im)mobilities over the life course," Journal of Transport Geography, Elsevier, vol. 82(C).
    6. Lee, Yongsung & Guhathakurta, Subhrajit, 2018. "An analysis of the effects of suburban densification on vehicle use for shopping: Do existing residents respond to land-use changes in the same way as recent movers?," Journal of Transport Geography, Elsevier, vol. 68(C), pages 193-204.
    7. Jessica Berg & Malin Henriksson & Jonas Ihlström, 2019. "Comfort First! Vehicle-Sharing Systems in Urban Residential Areas: The Importance for Everyday Mobility and Reduction of Car Use among Pilot Users," Sustainability, MDPI, vol. 11(9), pages 1-16, April.
    8. Chen, Ruoyu & Zhou, Jiangping, 2022. "Fare adjustment’s impacts on travel patterns and farebox revenue: An empirical study based on longitudinal smartcard data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 164(C), pages 111-133.
    9. Morgan, Njogu, 2020. "The stickiness of cycling: Residential relocation and changes in utility cycling in Johannesburg," Journal of Transport Geography, Elsevier, vol. 85(C).
    10. 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.
    11. Van Acker, Veronique & Ho, Loan & Stevens, Larissa & Mulley, Corinne, 2020. "Quantifying the effects of childhood and previous residential experiences on the use of public transport," Journal of Transport Geography, Elsevier, vol. 86(C).
    12. 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.
    13. Liu, Yan & Wang, Siqin & Xie, Bin, 2019. "Evaluating the effects of public transport fare policy change together with built and non-built environment features on ridership: The case in South East Queensland, Australia," Transport Policy, Elsevier, vol. 76(C), pages 78-89.
    14. 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).
    15. Jurgena Myftiu, 2022. "Promoting commute active modes during the Covid-19 pandemic. What is the role of multimodal travel mode choices?," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 76(4), pages 4-12, October-D.
    16. He, Yifan & Zhao, Chen & Zeng, An, 2022. "Ranking locations in a city via the collective home-work relations in human mobility data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    17. 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.
    18. 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.
    19. 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.
    20. Pieroni, Caio & Giannotti, Mariana & Alves, Bianca B. & Arbex, Renato, 2021. "Big data for big issues: Revealing travel patterns of low-income population based on smart card data mining in a global south unequal city," Journal of Transport Geography, Elsevier, vol. 96(C).

    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:eee:transa:v:165:y:2022:i:c:p:321-336. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/547/description#description .

    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.