IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2102.08256.html
   My bibliography  Save this paper

On-Demand Transit User Preference Analysis using Hybrid Choice Models

Author

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

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 the 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 service and outlines directions for reducing operational costs by updating the required fleet size and assigning more vehicles for work-related trips.

Suggested Citation

  • 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.
    • Handle: RePEc:arx:papers:2102.08256
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2102.08256
    File Function: Latest version
    Download Restriction: no
    ---><---

    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. Daniel McFadden, 1986. "The Choice Theory Approach to Market Research," Marketing Science, INFORMS, vol. 5(4), pages 275-297.
    4. 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.
    5. 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).
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    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. Yue Ding & Ruimin Li & Xiaokun Wang & Joshua Schmid, 2022. "Heterogeneity of autonomous vehicle adoption behavior due to peer effects and prior-AV knowledge," Transportation, Springer, vol. 49(6), pages 1837-1860, December.

    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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Oryani, Bahareh & Koo, Yoonmo & Shafiee, Afsaneh & Rezania, Shahabaldin & Jung, Jiyeon & Choi, Hyunhong & Khan, Muhammad Kamran, 2022. "Heterogeneous preferences for EVs: Evidence from Iran," Renewable Energy, Elsevier, vol. 181(C), pages 675-691.
    9. Jørgensen, Sisse Liv & Termansen, Mette & Pascual, Unai, 2020. "Natural insurance as condition for market insurance: Climate change adaptation in agriculture," Ecological Economics, Elsevier, vol. 169(C).
    10. Francisco J. Bahamonde-Birke & Juan de Dios Ortúzar, 2015. "About the Categorization of Latent Variables in Hybrid Choice Models," Discussion Papers of DIW Berlin 1527, DIW Berlin, German Institute for Economic Research.
    11. Sarrias, Mauricio & Daziano, Ricardo, 2017. "Multinomial Logit Models with Continuous and Discrete Individual Heterogeneity in R: The gmnl Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i02).
    12. 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).
    13. David Boto-García & Petr Mariel & José Baños Pino & Antonio Alvarez, 2022. "Tourists’ willingness to pay for holiday trip characteristics: A Discrete Choice Experiment," Tourism Economics, , vol. 28(2), pages 349-370, March.
    14. Sfeir, Georges & Abou-Zeid, Maya & Rodrigues, Filipe & Pereira, Francisco Camara & Kaysi, Isam, 2021. "Latent class choice model with a flexible class membership component: A mixture model approach," Journal of choice modelling, Elsevier, vol. 41(C).
    15. 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.
    16. 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.
    17. Lorena Torres Lahoz & Francisco Camara Pereira & Georges Sfeir & Ioanna Arkoudi & Mayara Moraes Monteiro & Carlos Lima Azevedo, 2023. "Attitudes and Latent Class Choice Models using Machine learning," Papers 2302.09871, arXiv.org.
    18. Yoo, James & Ready, Richard C., 2014. "Preference heterogeneity for renewable energy technology," Energy Economics, Elsevier, vol. 42(C), pages 101-114.
    19. 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.
    20. 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.

    More about this item

    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:arx:papers:2102.08256. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.