IDEAS home Printed from https://ideas.repec.org/a/eee/eejocm/v49y2023ics1755534523000520.html
   My bibliography  Save this article

On-demand transit user preference analysis using hybrid choice models

Author

Listed:
  • Alsaleh, Nael
  • Farooq, Bilal
  • Zhang, Yixue
  • Farber, Steven

Abstract

In light of the increasing interest to transform the fixed-route public transit (FRT) services into on-demand transit (ODT) services, there exists a strong need for a comprehensive evaluation of the effects of this shift on the users. Such an analysis can help municipalities and service providers to design and operate more convenient, attractive, and sustainable transit solutions. To understand the user preferences, we developed three hybrid choice models: integrated choice and latent variable (ICLV), latent class (LC), and latent class integrated choice and latent variable (LC-ICLV) models. We used these models to analyze the public transit user's preferences in Belleville, Ontario, Canada. Hybrid choice models were estimated using a rich dataset that combined the actual level of service attributes obtained from Belleville's ODT service and self-reported usage behaviour obtained from a revealed preference survey of the ODT users. The latent class models divided the users into two groups with different travel behaviour and preferences. The results showed that the captive user's preference for ODT service was significantly affected by the number of unassigned trips, in-vehicle time, and main travel mode before the ODT service started. On the other hand, the non-captive user's service preference was significantly affected by the Time Sensitivity and the Online Service Satisfaction latent variables, as well as the performance of the ODT service and trip purpose. This study attaches importance to improving the reliability and performance of the ODT services and outlines directions for reducing operational costs by updating the required fleet size and assigning more vehicles for work-related trips.

Suggested Citation

  • Alsaleh, Nael & Farooq, Bilal & Zhang, Yixue & Farber, Steven, 2023. "On-demand transit user preference analysis using hybrid choice models," Journal of choice modelling, Elsevier, vol. 49(C).
    • Handle: RePEc:eee:eejocm:v:49:y:2023:i:c:s1755534523000520
    DOI: 10.1016/j.jocm.2023.100451
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1755534523000520
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jocm.2023.100451?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. Ababio-Donkor, Augustus & Saleh, Wafaa & Fonzone, Achille, 2020. "The role of personal norms in the choice of mode for commuting," Research in Transportation Economics, Elsevier, vol. 83(C).
    2. Walker, Joan & Ben-Akiva, Moshe, 2002. "Generalized random utility model," Mathematical Social Sciences, Elsevier, vol. 43(3), pages 303-343, July.
    3. Sanaullah, Irum & Alsaleh, Nael & Djavadian, Shadi & Farooq, Bilal, 2021. "Spatio-temporal analysis of on-demand transit: A case study of Belleville, Canada," Transportation Research Part A: Policy and Practice, Elsevier, vol. 145(C), pages 284-301.
    4. Mohammad Torkjazi & Nathan Huynh, 2019. "Effectiveness of Dynamic Insertion Scheduling Strategy for Demand-Responsive Paratransit Vehicles Using Agent-Based Simulation," Sustainability, MDPI, vol. 11(19), pages 1-12, September.
    5. Anspacher, David & Khattak, Asad J. & Yim, Youngbin, 2004. "Demand-Responsive Transit Shuttles: Who Will Use Them?," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt2th2n99r, Institute of Transportation Studies, UC Berkeley.
    6. William H. Greene & David A. Hensher, 2013. "Revealing additional dimensions of preference heterogeneity in a latent class mixed multinomial logit model," Applied Economics, Taylor & Francis Journals, vol. 45(14), pages 1897-1902, May.
    7. Junyi Shen, 2009. "Latent class model or mixed logit model? A comparison by transport mode choice data," Applied Economics, Taylor & Francis Journals, vol. 41(22), pages 2915-2924.
    8. Neeraj Saxena & Taha Rashidi & David Rey, 2020. "Determining the Market Uptake of Demand Responsive Transport Enabled Public Transport Service," Sustainability, MDPI, vol. 12(12), pages 1-18, June.
    9. Vij, Akshay & Walker, Joan L., 2016. "How, when and why integrated choice and latent variable models are latently useful," Transportation Research Part B: Methodological, Elsevier, vol. 90(C), pages 192-217.
    10. Peter Boxall & Wiktor Adamowicz, 2002. "Understanding Heterogeneous Preferences in Random Utility Models: A Latent Class Approach," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 23(4), pages 421-446, December.
    11. Alsaleh, Nael & Farooq, Bilal, 2021. "Interpretable data-driven demand modelling for on-demand transit services," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 1-22.
    12. Daniel McFadden, 1986. "The Choice Theory Approach to Market Research," Marketing Science, INFORMS, vol. 5(4), pages 275-297.
    13. Hurtubia, Ricardo & Nguyen, My Hang & Glerum, Aurélie & Bierlaire, Michel, 2014. "Integrating psychometric indicators in latent class choice models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 64(C), pages 135-146.
    14. Miah, Md Mintu & Naz, Farah & Hyun, Kate (Kyung) & Mattingly, Stephen P. & Cronley, Courtney & Fields, Noelle, 2020. "Barriers and opportunities for paratransit users to adopt on-demand micro transit," Research in Transportation Economics, Elsevier, vol. 84(C).
    15. Zhou, Heng & Norman, Richard & Xia, Jianhong(Cecilia) & Hughes, Brett & Kelobonye, Keone & Nikolova, Gabi & Falkmer, Torbjorn, 2020. "Analysing travel mode and airline choice using latent class modelling: A case study in Western Australia," Transportation Research Part A: Policy and Practice, Elsevier, vol. 137(C), pages 187-205.
    16. Hall, Jonathan D. & Palsson, Craig & Price, Joseph, 2018. "Is Uber a substitute or complement for public transit?," Journal of Urban Economics, Elsevier, vol. 108(C), pages 36-50.
    17. Kamargianni, Maria & Dubey, Subodh & Polydoropoulou, Amalia & Bhat, Chandra, 2015. "Investigating the subjective and objective factors influencing teenagers’ school travel mode choice – An integrated choice and latent variable model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 473-488.
    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. Nael Alsaleh & Bilal Farooq & Yixue Zhang & Steven Farber, 2021. "On-Demand Transit User Preference Analysis using Hybrid Choice Models," Papers 2102.08256, arXiv.org, revised Aug 2023.
    2. Siliang Luan & Qingfang Yang & Zhongtai Jiang & Huxing Zhou & Fanyun Meng, 2022. "Analyzing Commute Mode Choice Using the LCNL Model in the Post-COVID-19 Era: Evidence from China," IJERPH, MDPI, vol. 19(9), pages 1-26, April.
    3. Aaditya, Bh. & Rahul, T.M., 2021. "Psychological impacts of COVID-19 pandemic on the mode choice behaviour: A hybrid choice modelling approach," Transport Policy, Elsevier, vol. 108(C), pages 47-58.
    4. Rossetti, Tomás & Guevara, C. Angelo & Galilea, Patricia & Hurtubia, Ricardo, 2018. "Modeling safety as a perceptual latent variable to assess cycling infrastructure," Transportation Research Part A: Policy and Practice, Elsevier, vol. 111(C), pages 252-265.
    5. Hurtubia, Ricardo & Nguyen, My Hang & Glerum, Aurélie & Bierlaire, Michel, 2014. "Integrating psychometric indicators in latent class choice models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 64(C), pages 135-146.
    6. Xuemei Fu, 2021. "How habit moderates the commute mode decision process: integration of the theory of planned behavior and latent class choice model," Transportation, Springer, vol. 48(5), pages 2681-2707, October.
    7. Tran, Yen & Yamamoto, Toshiyuki & Sato, Hitomi, 2020. "The influences of environmentalism and attitude towards physical activity on mode choice: The new evidences," Transportation Research Part A: Policy and Practice, Elsevier, vol. 134(C), pages 211-226.
    8. Kim, Seheon & Rasouli, Soora, 2022. "The influence of latent lifestyle on acceptance of Mobility-as-a-Service (MaaS): A hierarchical latent variable and latent class approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 304-319.
    9. Haghani, Milad & Bliemer, Michiel C.J. & Hensher, David A., 2021. "The landscape of econometric discrete choice modelling research," Journal of choice modelling, Elsevier, vol. 40(C).
    10. Liu, Yicong & Loa, Patrick & Wang, Kaili & Habib, Khandker Nurul, 2023. "Theory-driven or data-driven? Modelling ride-sourcing mode choices using integrated choice and latent variable model and multi-task learning deep neural networks," Journal of choice modelling, Elsevier, vol. 48(C).
    11. Jin, Yana & Andersson, Henrik & Zhang, Shiqiu, 2020. "Do preferences to reduce health risks related to air pollution depend on illness type? Evidence from a choice experiment in Beijing, China," Journal of Environmental Economics and Management, Elsevier, vol. 103(C).
    12. Lahoz, Lorena Torres & Pereira, Francisco Camara & Sfeir, Georges & Arkoudi, Ioanna & Monteiro, Mayara Moraes & Azevedo, Carlos Lima, 2023. "Attitudes and Latent Class Choice Models using Machine Learning," Journal of choice modelling, Elsevier, vol. 49(C).
    13. Stephane Hess, 2014. "Latent class structures: taste heterogeneity and beyond," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 14, pages 311-330, Edward Elgar Publishing.
    14. Marley, A.A.J. & Swait, J., 2017. "Goal-based models for discrete choice analysis," Transportation Research Part B: Methodological, Elsevier, vol. 101(C), pages 72-88.
    15. Tomás Rossetti & Ricardo Daziano, 2023. "How does self-assessed health status relate to preferences for cycling infrastructure? A latent class and latent variable approach," Transportation, Springer, vol. 50(3), pages 913-928, June.
    16. Mikkel Thorhauge & Akshay Vij & Elisabetta Cherchi, 2021. "Heterogeneity in departure time preferences, flexibility and schedule constraints," Transportation, Springer, vol. 48(4), pages 1865-1893, August.
    17. Zhou, Heng & Norman, Richard & Xia, Jianhong(Cecilia) & Hughes, Brett & Kelobonye, Keone & Nikolova, Gabi & Falkmer, Torbjorn, 2020. "Analysing travel mode and airline choice using latent class modelling: A case study in Western Australia," Transportation Research Part A: Policy and Practice, Elsevier, vol. 137(C), pages 187-205.
    18. Janez Dolšak & Nevenka Hrovatin & Jelena Zorić, 2020. "Analysing Consumer Preferences, Characteristics, and Behaviour to Identify Energy-Efficient Consumers," Sustainability, MDPI, vol. 12(23), pages 1-19, November.
    19. Varela, Elsa & Jacobsen, Jette Bredahl & Soliño, Mario, 2014. "Understanding the heterogeneity of social preferences for fire prevention management," Ecological Economics, Elsevier, vol. 106(C), pages 91-104.
    20. Román, Concepción & Arencibia, Ana Isabel & Feo-Valero, María, 2017. "A latent class model with attribute cut-offs to analyze modal choice for freight transport," Transportation Research Part A: Policy and Practice, Elsevier, vol. 102(C), pages 212-227.

    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:eejocm:v:49:y:2023:i:c:s1755534523000520. 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.journals.elsevier.com/journal-of-choice-modelling .

    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.