IDEAS home Printed from https://ideas.repec.org/a/eee/jotrge/v56y2016icp36-44.html
   My bibliography  Save this article

Transportation network company wait times in Greater Seattle, and relationship to socioeconomic indicators

Author

Listed:
  • Hughes, Ryan
  • MacKenzie, Don

Abstract

Transportation network companies (TNC) which use smartphone apps to connect travelers to drivers for point to point, intra-city trips have expanded rapidly in recent years, yet the impacts of their services on urban transportation systems, for good or ill, are not fully understood. These services may increase access to transportation for some individuals, but it is unclear whether these benefits are equitably distributed across different neighborhoods. In this paper, we explore the spatial variability in wait times for a TNC vehicle throughout the Seattle, WA region, testing whether areas with lower average income or a greater percentage of minorities experience different waiting times than other areas. We collected approximately 1 million observations of estimated waiting times, quasi-randomly sampled over approximately two months in 2015. We analyzed spatial and temporal patterns using local regression, which suggested lower wait times in densely populated areas of the Seattle region, and the lowest wait times during midday hours. We developed a spatial error regression model to investigate relationships between wait times and socioeconomic indicators at the census block group (CBG) level. We find that conditional on other covariates, expected waiting times are longer in CBGs with higher average income, and shorter in CBGs with greater population density and employment density. After adjusting for differences in density and income, a higher percentage of minorities in a CBG is associated with longer waiting times late at night and shorter waiting times during the day, with an average effect of close to zero. Geographically weighted regression indicates that the strength, and in some cases the sign, of these relationships varies throughout the Seattle region. Overall, the results suggests that transportation network companies offer higher performance in dense urban areas, and that adequate access to TNC services is not necessarily restricted to areas that are “white and wealthy.”

Suggested Citation

  • Hughes, Ryan & MacKenzie, Don, 2016. "Transportation network company wait times in Greater Seattle, and relationship to socioeconomic indicators," Journal of Transport Geography, Elsevier, vol. 56(C), pages 36-44.
    • Handle: RePEc:eee:jotrge:v:56:y:2016:i:c:p:36-44
    DOI: 10.1016/j.jtrangeo.2016.08.014
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0966692316301430
    Download Restriction: no
    File URL: https://libkey.io/10.1016/j.jtrangeo.2016.08.014?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
    ---><---

    References listed on IDEAS

    as
    1. Jonathan V. Hall & Alan B. Krueger, 2015. "An Analysis of the Labor Market for Uber's Driver-Partners in the United States," Working Papers 587, Princeton University, Department of Economics, Industrial Relations Section..
    2. Donald Anderson, 2014. "“Not just a taxi”? For-profit ridesharing, driver strategies, and VMT," Transportation, Springer, vol. 41(5), pages 1099-1117, September.
    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. Rayle, Lisa & Dai, Danielle & Chan, Nelson & Cervero, Robert & Shaheen, Susan, 2016. "Just a better taxi? A survey-based comparison of taxis, transit, and ridesourcing services in San Francisco," Transport Policy, Elsevier, vol. 45(C), pages 168-178.
    2. Xu, Zhengtian & Yin, Yafeng & Zha, Liteng, 2017. "Optimal parking provision for ride-sourcing services," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 559-578.
    3. Punel, Aymeric & Stathopoulos, Amanda, 2017. "Modeling the acceptability of crowdsourced goods deliveries: Role of context and experience effects," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 105(C), pages 18-38.
    4. Cristiano Codagnone & Fabienne Abadie & Federico Biagi, 2016. "The Future of Work in the ‘Sharing Economy’. Market Efficiency and Equitable Opportunities or Unfair Precarisation?," JRC Research Reports JRC101280, Joint Research Centre.
    5. Emmanuelle Reuter, 2022. "Hybrid business models in the sharing economy: The role of business model design for managing the environmental paradox," Business Strategy and the Environment, Wiley Blackwell, vol. 31(2), pages 603-618, February.
    6. Berger, Thor & Chen, Chinchih & Frey, Carl Benedikt, 2018. "Drivers of disruption? Estimating the Uber effect," European Economic Review, Elsevier, vol. 110(C), pages 197-210.
    7. Sutirtha Bagchi, 2018. "A Tale of Two Cities: An Examination of Medallion Prices in New York and Chicago," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 53(2), pages 295-319, September.
    8. Sen Li & Kameshwar Poolla & Pravin Varaiya, 2020. "Impact of Congestion Charge and Minimum Wage on TNCs: A Case Study for San Francisco," Papers 2003.02550, arXiv.org, revised Feb 2021.
    9. Alejandro Henao & Wesley E. Marshall, 2019. "The impact of ride-hailing on vehicle miles traveled," Transportation, Springer, vol. 46(6), pages 2173-2194, December.
    10. Ting Wang & Yong Zhang & Meiye Li & Lei Liu, 2019. "How Do Passengers with Different Using Frequencies Choose between Traditional Taxi Service and Online Car-Hailing Service? A Case Study of Nanjing, China," Sustainability, MDPI, vol. 11(23), pages 1-18, November.
    11. Hossain, Mokter & Mozahem, Najib Ali, 2022. "Drivers’ perceptions of the sharing economy for transport services," Technological Forecasting and Social Change, Elsevier, vol. 179(C).
    12. Xu, Zhengtian & Yin, Yafeng & Ye, Jieping, 2020. "On the supply curve of ride-hailing systems," Transportation Research Part B: Methodological, Elsevier, vol. 132(C), pages 29-43.
    13. Yang, Zhuo & Franz, Mark L. & Zhu, Shanjiang & Mahmoudi, Jina & Nasri, Arefeh & Zhang, Lei, 2018. "Analysis of Washington, DC taxi demand using GPS and land-use data," Journal of Transport Geography, Elsevier, vol. 66(C), pages 35-44.
    14. Adermon, Adrian & Hensvik, Lena, 2022. "Gig-jobs: Stepping stones or dead ends?," Labour Economics, Elsevier, vol. 76(C).
    15. Xiaowei Chen & Hongyu Zheng & Ze Wang & Xiqun Chen, 2021. "Exploring impacts of on-demand ridesplitting on mobility via real-world ridesourcing data and questionnaires," Transportation, Springer, vol. 48(4), pages 1541-1561, August.
    16. Rosie Graham, 2017. "Google and advertising: digital capitalism in the context of Post-Fordism, the reification of language, and the rise of fake news," Palgrave Communications, Palgrave Macmillan, vol. 3(1), pages 1-19, December.
    17. Brad Greenwood & Idris Adjerid & Corey M. Angst & Nathan L. Meikle, 2022. "How Unbecoming of You: Online Experiments Uncovering Gender Biases in Perceptions of Ridesharing Performance," Journal of Business Ethics, Springer, vol. 175(3), pages 499-518, January.
    18. Mujtaba Ahsan, 2020. "Entrepreneurship and Ethics in the Sharing Economy: A Critical Perspective," Journal of Business Ethics, Springer, vol. 161(1), pages 19-33, January.
    19. Annarosa Pesole & Enrique Fernandez-Macias & Cesira Urzi Brancati & Estrella Gomez Herrera, 2019. "How to quantify what is not seen? Two proposals for measuring platform work," JRC Working Papers on Labour, Education and Technology 2019-01, Joint Research Centre.
    20. 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.

    More about this item

    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:eee:jotrge:v:56:y:2016:i:c:p:36-44. 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: https://www.journals.elsevier.com/journal-of-transport-geography .

    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.