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Nonstandard ridehail use in Austin

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  • Edwards, Mickey

Abstract

Ridehailing quickly proliferated by geography and socioeconomic status following the 2012 introduction of UberX and Lyft, but did that proliferation extend to higher-priced, nonstandard-vehicle trips? What explains nonstandard ridehail use, who uses it, and where are they going? RideAustin trip-level data, land-use codes, ACS block groups, and parking data are analyzed using GIS and statistical software to find out. Results imply the nonstandard-vehicle, RideAustin traveler varied from the standard-vehicle traveler in several important socioeconomic ways, including access to a car, household income, race/ethnicity, age, education, and neighborhood trips per capita. About 47% of nonstandard-vehicle trips were made in neighborhoods with the highest carless household rate, and 40% of those were made in SUVs. Better tips and higher star ratings imply standard-vehicle passengers had a higher level of satisfaction compared to nonstandard-vehicle passengers. Policy recommendations are made to incentivize carpooling in these extra-capacity vehicles through subsidies.

Suggested Citation

  • Edwards, Mickey, 2020. "Nonstandard ridehail use in Austin," Journal of Transport Geography, Elsevier, vol. 86(C).
  • Handle: RePEc:eee:jotrge:v:86:y:2020:i:c:s0966692319302480
    DOI: 10.1016/j.jtrangeo.2020.102746
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    References listed on IDEAS

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    1. Deka, Devajyoti & Fei, Da, 2019. "A comparison of the personal and neighborhood characteristics associated with ridesourcing, transit use, and driving with NHTS data," Journal of Transport Geography, Elsevier, vol. 76(C), pages 24-33.
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    3. 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.
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    5. repec:ucp:bkecon:9781884829987 is not listed on IDEAS
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    Cited by:

    1. Rico Krueger & Michel Bierlaire & Prateek Bansal, 2022. "A Data Fusion Approach for Ride-sourcing Demand Estimation: A Discrete Choice Model with Sampling and Endogeneity Corrections," Papers 2212.02178, arXiv.org.
    2. Li, Shengxiao(Alex) & Zhai, Wei & Jiao, Junfeng & Wang, Chao (Kenneth), 2022. "Who loses and who wins in the ride-hailing era? A case study of Austin, Texas," Transport Policy, Elsevier, vol. 120(C), pages 130-138.

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