IDEAS home Printed from https://ideas.repec.org/a/kap/transp/v49y2022i3d10.1007_s11116-021-10193-5.html
   My bibliography  Save this article

Exploring the correlation between ride-hailing and multimodal transit ridership in toronto

Author

Listed:
  • Wenting Li

    (University of Toronto)

  • Amer Shalaby

    (University of Toronto)

  • Khandker Nurul Habib

    (University of Toronto)

Abstract

Ride-hailing (RH) services have been growing rapidly and gaining popularity worldwide. However, many transit agencies are experiencing ridership stagnation or even decline. Understanding the correlation between RH trips and transit ridership has become an urgently important matter for transit agencies. This study aimed to explore the relationship between RH and public transit ridership and provide a starting point for future studies. This study benefitted from having access to detailed data on trip-level RH trips, transit supply and transit ridership in Toronto for three years (2016–2018). With this dataset, the study utilized random-effects panel data models and log–log regression models to estimate the correlation of RH pickup/drop-off counts with subway station and surface transit route (buses and streetcars) ridership within transit catchment areas, broken down into five different periods of a non-summer weekday. The results show that RH services generally have a positive association with subway station ridership while negatively correlating with surface transit route ridership. The positive relationship between RH and subway station ridership is the strongest during the mid-day and early evening. In contrast, the negative relationship between surface transit routes and RH ridership is the highest during peak commuting hours. Additionally, RH trip volume is more positively related to ridership at terminal/transfer subway stations in Toronto’s city centre while more negatively associated with routes with relatively poor services (e.g., low on-time performance, low vehicle running speed and low frequency) in the city centre where traffic congestion can be severe. According to the above findings, the degree of the relationship between RH and public transit demand tends to be mixed, varying by transit mode, time of day and transit level-of-service. The gained knowledge about RH and transit can provide insights for transit agencies to improve transit services, which are discussed in this paper.

Suggested Citation

  • Wenting Li & Amer Shalaby & Khandker Nurul Habib, 2022. "Exploring the correlation between ride-hailing and multimodal transit ridership in toronto," Transportation, Springer, vol. 49(3), pages 765-789, June.
  • Handle: RePEc:kap:transp:v:49:y:2022:i:3:d:10.1007_s11116-021-10193-5
    DOI: 10.1007/s11116-021-10193-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11116-021-10193-5
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s11116-021-10193-5?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. Boisjoly, Geneviève & Grisé, Emily & Maguire, Meadhbh & Veillette, Marie-Pier & Deboosere, Robbin & Berrebi, Emma & El-Geneidy, Ahmed, 2018. "Invest in the ride: A 14 year longitudinal analysis of the determinants of public transport ridership in 25 North American cities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 434-445.
    2. Kuhnimhof, Tobias & Buehler, Ralph & Wirtz, Matthias & Kalinowska, Dominika, 2012. "Travel trends among young adults in Germany: increasing multimodality and declining car use for men," Journal of Transport Geography, Elsevier, vol. 24(C), pages 443-450.
    3. Litman, Todd, 2007. "Evaluating rail transit benefits: A comment," Transport Policy, Elsevier, vol. 14(1), pages 94-97, January.
    4. Clewlow, Regina R. & Mishra, Gouri S., 2017. "Disruptive Transportation: The Adoption, Utilization, and Impacts of Ride-Hailing in the United States," Institute of Transportation Studies, Working Paper Series qt82w2z91j, Institute of Transportation Studies, UC Davis.
    5. Chiou, Yu-Chiun & Jou, Rong-Chang & Yang, Cheng-Han, 2015. "Factors affecting public transportation usage rate: Geographically weighted regression," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 161-177.
    6. 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.
    7. Taylor, Brian D. & Fink, Camille N.Y., 2003. "The Factors Influencing Transit Ridership: A Review and Analysis of the Ridership Literature," University of California Transportation Center, Working Papers qt3xk9j8m2, University of California Transportation Center.
    8. Michael L. Anderson, 2014. "Subways, Strikes, and Slowdowns: The Impacts of Public Transit on Traffic Congestion," American Economic Review, American Economic Association, vol. 104(9), pages 2763-2796, September.
    9. Young, Mischa & Allen, Jeff & Farber, Steven, 2020. "Measuring when Uber behaves as a substitute or supplement to transit: An examination of travel-time differences in Toronto," Journal of Transport Geography, Elsevier, vol. 82(C).
    10. Alejandro Henao & Wesley E. Marshall, 2019. "The impact of ride-hailing on vehicle miles traveled," Transportation, Springer, vol. 46(6), pages 2173-2194, 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. Tian, Guang & Ewing, Reid & Li, Han, 2023. "Exploring the influences of ride-hailing services on VMT and transit usage – Evidence from California," Journal of Transport Geography, Elsevier, vol. 110(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. Zhang, Zhaolin & Zhai, Guocong & Xie, Kun & Xiao, Feng, 2022. "Exploring the nonlinear effects of ridesharing on public transit usage: A case study of San Diego," Journal of Transport Geography, Elsevier, vol. 104(C).
    2. Lee, Yongsung & Lee, Bumsoo, 2022. "What’s eating public transit in the United States? Reasons for declining transit ridership in the 2010s," Transportation Research Part A: Policy and Practice, Elsevier, vol. 157(C), pages 126-143.
    3. Soria, Jason & Stathopoulos, Amanda, 2021. "Investigating socio-spatial differences between solo ridehailing and pooled rides in diverse communities," Journal of Transport Geography, Elsevier, vol. 95(C).
    4. Zou, Zhenpeng & Cirillo, Cinzia, 2021. "Does ridesourcing impact driving decisions: A survey weighted regression analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 146(C), pages 1-12.
    5. Brown, Anne, 2022. "Not all fees are created equal: Equity implications of ride-hail fee structures and revenues," Transport Policy, Elsevier, vol. 125(C), pages 1-10.
    6. Xiaoxia Dong & Erick Guerra & Ricardo A. Daziano, 2022. "Impact of TNC on travel behavior and mode choice: a comparative analysis of Boston and Philadelphia," Transportation, Springer, vol. 49(6), pages 1577-1597, December.
    7. Yao, Di & Xu, Liqun & Li, Jinpei, 2020. "Does technical efficiency play a mediating role between bus facility scale and ridership attraction? Evidence from bus practices in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 77-96.
    8. Dean, Matthew D. & Kockelman, Kara M., 2021. "Spatial variation in shared ride-hail trip demand and factors contributing to sharing: Lessons from Chicago," Journal of Transport Geography, Elsevier, vol. 91(C).
    9. Simon J. Berrebi & Kari E. Watkins, 2020. "Whos Ditching the Bus?," Papers 2001.02200, arXiv.org, revised Mar 2020.
    10. Barajas, Jesus M. & Brown, Anne, 2021. "Not minding the gap: Does ride-hailing serve transit deserts?," Journal of Transport Geography, Elsevier, vol. 90(C).
    11. Oded Cats & Rafal Kucharski & Santosh Rao Danda & Menno Yap, 2022. "Beyond the dichotomy: How ride-hailing competes with and complements public transport," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-17, January.
    12. Brown, Anne, 2021. "Not All Fees are Created Equal: Equity Implications of Ride-hail Fee Structures," OSF Preprints cpsqu, Center for Open Science.
    13. Adam Millard-Ball & Liwei Liu & Whitney Hansen & Drew Cooper & Joe Castiglione, 2023. "Where ridehail drivers go between trips," Transportation, Springer, vol. 50(5), pages 1959-1981, October.
    14. Hossain Mohiuddin, 2021. "Planning for the First and Last Mile: A Review of Practices at Selected Transit Agencies in the United States," Sustainability, MDPI, vol. 13(4), pages 1-20, February.
    15. Berrebi, Simon J. & Watkins, Kari E., 2020. "Who’s ditching the bus?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 136(C), pages 21-34.
    16. Wenyuan Zhou & Xuanrong Li & Zhenguo Shi & Bingjie Yang & Dongxu Chen, 2023. "Impact of Carpooling under Mobile Internet on Travel Mode Choices and Urban Traffic Volume: The Case of China," Sustainability, MDPI, vol. 15(8), pages 1-15, April.
    17. Michael Manville & Brian D. Taylor & Evelyn Blumenberg & Andrew Schouten, 2023. "Vehicle access and falling transit ridership: evidence from Southern California," Transportation, Springer, vol. 50(1), pages 303-329, February.
    18. Yash Babar & Gordon Burtch, 2020. "Examining the Heterogeneous Impact of Ride-Hailing Services on Public Transit Use," Information Systems Research, INFORMS, vol. 31(3), pages 820-834, September.
    19. Nair, Gopindra S. & Bhat, Chandra R. & Batur, Irfan & Pendyala, Ram M. & Lam, William H.K., 2020. "A model of deadheading trips and pick-up locations for ride-hailing service vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 135(C), pages 289-308.
    20. Yan, Xiang & Liu, Xinyu & Zhao, Xilei, 2020. "Using machine learning for direct demand modeling of ridesourcing services in Chicago," Journal of Transport Geography, Elsevier, vol. 83(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:kap:transp:v:49:y:2022:i:3:d:10.1007_s11116-021-10193-5. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.