IDEAS home Printed from https://ideas.repec.org/a/eee/transa/v152y2021icp1-18.html
   My bibliography  Save this article

Heterogeneity in Activity-travel Patterns of Public Transit Users: An Application of Latent Class Analysis

Author

Listed:
  • Rafiq, Rezwana
  • McNally, Michael G.

Abstract

Public transit is considered a sustainable mode of transport that can address automobile dependency and provide environmental, economic, and societal benefits. However, with typical temporal and spatial constraints such as fixed routes and schedules, transfer requirements, waiting times, and access/egress issues, public transit offers lower accessibility and mobility services than private vehicles and thus it is considered a less attractive mode to many prospective users. To improve the performance of transit and in turn to increase its usage, a broader understanding of the daily activity-travel patterns of transit users is fundamental. In this context, this study analyzed transit-based activity-travel patterns by classifying users via Latent Class Analysis (LCA). Using data from the 2017 National Household Travel Survey, the LCA model suggested that transit users could be split into five distinct classes where each class has a representative activity-travel pattern. Class 1 constituted employed white males who made transit-dominant simple work tours. Class 2 was composed of employed white females who made complex work tours. Employed white millennials comprised Class 3 and made multimodal complex tours. Transit Class 4 were non-white younger or older adult groups who made transit-dominant simple non-work tours. Last, Class 5 members made complex non-work tours with recurrent transit use and comprised single older women. This study provided insights regarding the variations of activity-travel patterns and the associated market segments of transit users in the United States. The results can assist transit agencies in identifying transit user groups with particular activity patterns and to consider market strategies that can address their travel needs.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:transa:v:152:y:2021:i:c:p:1-18
    DOI: 10.1016/j.tra.2021.07.011
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0965856421001932
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tra.2021.07.011?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. Eric I. Pas, 1983. "A Flexible and Integrated Methodology for Analytical Classification of Daily Travel-Activity Behavior," Transportation Science, INFORMS, vol. 17(4), pages 405-429, November.
    2. Shiftan, Yoram & Outwater, Maren L. & Zhou, Yushuang, 2008. "Transit market research using structural equation modeling and attitudinal market segmentation," Transport Policy, Elsevier, vol. 15(3), pages 186-195, May.
    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. Eldeeb, Gamal & Mohamed, Moataz, 2020. "Quantifying preference heterogeneity in transit service desired quality using a latent class choice model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 139(C), pages 119-133.
    5. Jarad Beckman & Konstadinos Goulias, 2008. "Immigration, residential location, car ownership, and commuting behavior: a multivariate latent class analysis from California," Transportation, Springer, vol. 35(5), pages 655-671, August.
    6. Abenoza, Roberto F. & Cats, Oded & Susilo, Yusak O., 2017. "Travel satisfaction with public transport: Determinants, user classes, regional disparities and their evolution," Transportation Research Part A: Policy and Practice, Elsevier, vol. 95(C), pages 64-84.
    7. Marco Diana & Patricia Mokhtarian, 2009. "Grouping travelers on the basis of their different car and transit levels of use," Transportation, Springer, vol. 36(4), pages 455-467, July.
    8. Arentze, Theo A. & Timmermans, Harry J.P., 2009. "A need-based model of multi-day, multi-person activity generation," Transportation Research Part B: Methodological, Elsevier, vol. 43(2), pages 251-265, February.
    9. 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.
    10. Yongsung Lee & Giovanni Circella & Patricia L. Mokhtarian & Subhrajit Guhathakurta, 2020. "Are millennials more multimodal? A latent-class cluster analysis with attitudes and preferences among millennial and Generation X commuters in California," Transportation, Springer, vol. 47(5), pages 2505-2528, October.
    11. David Hensher & April Reyes, 2000. "Trip chaining as a barrier to the propensity to use public transport," Transportation, Springer, vol. 27(4), pages 341-361, December.
    12. Bhat, Chandra R. & Astroza, Sebastian & Bhat, Aarti C. & Nagel, Kai, 2016. "Incorporating a multiple discrete-continuous outcome in the generalized heterogeneous data model: Application to residential self-selection effects analysis in an activity time-use behavior model," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 52-76.
    13. Alemi, Farzad & Circella, Giovanni & Mokhtarian, Patricia & Handy, Susan, 2018. "Exploring the latent constructs behind the use of ridehailing in California," Journal of choice modelling, Elsevier, vol. 29(C), pages 47-62.
    14. Rezwana Rafiq & Michael G. McNally, 0. "A study of tour formation: pre-, during, and post-recession analysis," Transportation, Springer, vol. 0, pages 1-47.
    15. Rafiq, Rezwana & McNally, Michael G., 2020. "An empirical analysis and policy implications of work tours utilizing public transit," Transportation Research Part A: Policy and Practice, Elsevier, vol. 142(C), pages 237-259.
    16. Alonso-González, María J. & Hoogendoorn-Lanser, Sascha & van Oort, Niels & Cats, Oded & Hoogendoorn, Serge, 2020. "Drivers and barriers in adopting Mobility as a Service (MaaS) – A latent class cluster analysis of attitudes," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 378-401.
    17. Felix Haifeng Liao & Steven Farber & Reid Ewing, 2015. "Compact development and preference heterogeneity in residential location choice behaviour: A latent class analysis," Urban Studies, Urban Studies Journal Limited, vol. 52(2), pages 314-337, February.
    18. Yongsung Lee & Giovanni Circella & Patricia L. Mokhtarian & Subhrajit Guhathakurta, 0. "Are millennials more multimodal? A latent-class cluster analysis with attitudes and preferences among millennial and Generation X commuters in California," Transportation, Springer, vol. 0, pages 1-24.
    19. Recker, W. W., 1995. "The household activity pattern problem: General formulation and solution," Transportation Research Part B: Methodological, Elsevier, vol. 29(1), pages 61-77, February.
    20. Ma, Xiaolei & Liu, Congcong & Wen, Huimin & Wang, Yunpeng & Wu, Yao-Jan, 2017. "Understanding commuting patterns using transit smart card data," Journal of Transport Geography, Elsevier, vol. 58(C), pages 135-145.
    21. Linzer, Drew A. & Lewis, Jeffrey B., 2011. "poLCA: An R Package for Polytomous Variable Latent Class Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i10).
    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. Gao, Ya & Pan, Haixiao & Xie, Zhilin & Habib, Khandker Nurul, 2023. "Understanding patients heterogeneity in healthcare travel and hospital choice - A latent class analysis with covariates," Journal of Transport Geography, Elsevier, vol. 110(C).
    2. Cui, Shuang & Tian, Lijun & Xu, Yan & Wang, Yacan, 2024. "Measuring acceptance of tradable credit scheme and its effect on behavioral intention through theory of planned behavior," Transport Policy, Elsevier, vol. 150(C), pages 174-188.
    3. 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.
    4. Yuan, Yalong & Yang, Min & Feng, Tao & Ma, Yafeng & Ren, Yifeng & Ruan, Xinpei, 2022. "Heterogeneity in the transfer time of air-rail intermodal passengers based on ticket booking data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 533-552.

    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. Sanjay Gupta & Kushagra Sinha, 2022. "Assessing the Factors Impacting Transport Usage of Mobility App Users in the National Capital Territory of Delhi, India," Sustainability, MDPI, vol. 14(21), pages 1-20, October.
    2. Luo, Shuli & He, Sylvia Y. & Grant-Muller, Susan & Song, Linqi, 2023. "Influential factors in customer satisfaction of transit services: Using crowdsourced data to capture the heterogeneity across individuals, space and time," Transport Policy, Elsevier, vol. 131(C), pages 173-183.
    3. Rezwana Rafiq & Michael G. McNally, 2023. "An exploratory analysis of alternative travel behaviors of ride-hailing users," Transportation, Springer, vol. 50(2), pages 571-605, April.
    4. Diana, Marco & Pronello, Cristina, 2010. "Traveler segmentation strategy with nominal variables through correspondence analysis," Transport Policy, Elsevier, vol. 17(3), pages 183-190, May.
    5. Miriam Pirra & Ruggero G. Pensa, 2019. "Comparing Transport Quality Perception among Different Travellers in European Cities through Co-Cluster Analysis," Sustainability, MDPI, vol. 11(24), pages 1-17, December.
    6. Shah, Harsh & Carrel, Andre L. & Le, Huyen T.K., 2021. "What is your shopping travel style? Heterogeneity in US households’ online shopping and travel," Transportation Research Part A: Policy and Practice, Elsevier, vol. 153(C), pages 83-98.
    7. Singh, Abhilash C. & Faghih Imani, Ahmadreza & Sivakumar, Aruna & Luna Xi, Yang & Miller, Eric J., 2024. "A joint analysis of accessibility and household trip frequencies by travel mode," Transportation Research Part A: Policy and Practice, Elsevier, vol. 181(C).
    8. Zhi Dong & Jiaqi Zhang & Xiaoqi Gong & Laijun Wang, 2024. "The Role of Smart Travel Service in Intercity Travel Satisfaction: Does Traveler Heterogeneity Matter?," Sustainability, MDPI, vol. 16(17), pages 1-16, August.
    9. Allahviranloo, Mahdieh & Recker, Will, 2013. "Daily activity pattern recognition by using support vector machines with multiple classes," Transportation Research Part B: Methodological, Elsevier, vol. 58(C), pages 16-43.
    10. Deka, Devajyoti & Carnegie, Jon, 2021. "Predicting transit mode choice of New Jersey workers commuting to New York City from a stated preference survey," Journal of Transport Geography, Elsevier, vol. 91(C).
    11. 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).
    12. Wang, Yihong & Correia, Gonçalo Homem de Almeida & de Romph, Erik & Timmermans, H.J.P., 2017. "Using metro smart card data to model location choice of after-work activities: An application to Shanghai," Journal of Transport Geography, Elsevier, vol. 63(C), pages 40-47.
    13. Zidan Mao & Fangyu Liu & Ying Zhao, 2023. "Happy city for everyone: Generational differences in rural migrant workers’ leisure in urban China," Urban Studies, Urban Studies Journal Limited, vol. 60(16), pages 3252-3271, December.
    14. Hainan Huang & Yi Lin & Jiancheng Weng & Jian Rong & Xiaoming Liu, 2018. "Identification of Inelastic Subway Trips Based on Weekly Station Sequence Data: An Example from the Beijing Subway," Sustainability, MDPI, vol. 10(12), pages 1-15, December.
    15. Alonso-González, María J. & Hoogendoorn-Lanser, Sascha & van Oort, Niels & Cats, Oded & Hoogendoorn, Serge, 2020. "Drivers and barriers in adopting Mobility as a Service (MaaS) – A latent class cluster analysis of attitudes," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 378-401.
    16. Abenoza, Roberto F. & Cats, Oded & Susilo, Yusak O., 2017. "Travel satisfaction with public transport: Determinants, user classes, regional disparities and their evolution," Transportation Research Part A: Policy and Practice, Elsevier, vol. 95(C), pages 64-84.
    17. Jie Huang & David Levinson & Jiaoe Wang & Haitao Jin, 2019. "Job-worker spatial dynamics in Beijing: Insights from Smart Card Data," Working Papers 2019-01, University of Minnesota: Nexus Research Group.
    18. Kaveh Jahanshahi & Ying Jin, 2021. "Identification and mapping of spatial variations in travel choices through combining structural equation modelling and latent class analysis: findings for Great Britain," Transportation, Springer, vol. 48(3), pages 1329-1359, June.
    19. Thibaut Dubernet & Kay Axhausen, 2015. "Implementing a household joint activity-travel multi- agent simulation tool: first results," Transportation, Springer, vol. 42(5), pages 753-769, September.
    20. Shelat, Sanmay & Huisman, Raymond & van Oort, Niels, 2018. "Analysing the trip and user characteristics of the combined bicycle and transit mode," Research in Transportation Economics, Elsevier, vol. 69(C), pages 68-76.

    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:152:y:2021:i:c:p:1-18. 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.