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Associating Ridesourcing with Road Safety Outcomes: Insights from Austin Texas

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  • Eleftheria Kontou
  • Noreen C. McDonald

Abstract

Improving road safety and setting targets for reducing traffic-related crashes and deaths are highlighted as part of the United Nation's sustainable development goals and vision zero efforts around the globe. The advent of transportation network companies, such as ridesourcing, expands mobility options in cities and may impact road safety outcomes. In this study, we analyze the effects of ridesourcing use on road crashes, injuries, fatalities, and driving while intoxicated (DWI) offenses in Travis County Texas. Our approach leverages real-time ridesourcing volume to explain variation in road safety outcomes. Spatial panel data models with fixed effects are deployed to examine whether the use of ridesourcing is significantly associated with road crashes and other safety metrics. Our results suggest that for a 10% increase in ridesourcing trips, we expect a 0.12% decrease in road crashes (p

Suggested Citation

  • Eleftheria Kontou & Noreen C. McDonald, 2020. "Associating Ridesourcing with Road Safety Outcomes: Insights from Austin Texas," Papers 2001.03461, arXiv.org, revised Feb 2021.
  • Handle: RePEc:arx:papers:2001.03461
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    1. Debarsy, Nicolas & Ertur, Cem, 2010. "Testing for spatial autocorrelation in a fixed effects panel data model," Regional Science and Urban Economics, Elsevier, vol. 40(6), pages 453-470, November.
    2. Millo, Giovanni & Piras, Gianfranco, 2012. "splm: Spatial Panel Data Models in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 47(i01).
    3. Carpenter, Christopher S. & Stehr, Mark, 2008. "The effects of mandatory seatbelt laws on seatbelt use, motor vehicle fatalities, and crash-related injuries among youths," Journal of Health Economics, Elsevier, vol. 27(3), pages 642-662, May.
    4. Kong, Hui & Zhang, Xiaohu & Zhao, Jinhua, 2020. "How does ridesourcing substitute for public transit? A geospatial perspective in Chengdu, China," Journal of Transport Geography, Elsevier, vol. 86(C).
    5. William A. V. Clark & William Lisowski, 2017. "Decisions to move and decisions to stay: life course events and mobility outcomes," Housing Studies, Taylor & Francis Journals, vol. 32(5), pages 547-565, July.
    6. Leon Moskatel & David Slusky, 2019. "Did UberX reduce ambulance volume?," Health Economics, John Wiley & Sons, Ltd., vol. 28(7), pages 817-829, July.
    7. González, Silvia R. & Loukaitou-Sideris, Anastasia & Chapple, Karen, 2019. "Transit neighborhoods, commercial gentrification, and traffic crashes: Exploring the linkages in Los Angeles and the Bay Area," Journal of Transport Geography, Elsevier, vol. 77(C), pages 79-89.
    8. Jerry Hausman, 2015. "Specification tests in econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 112-134.
    9. Baltagi, Badi H. & Song, Seuck Heun & Koh, Won, 2003. "Testing panel data regression models with spatial error correlation," Journal of Econometrics, Elsevier, vol. 117(1), pages 123-150, November.
    10. Luc Anselin & Raymond J. G. M. Florax, 1995. "Small Sample Properties of Tests for Spatial Dependence in Regression Models: Some Further Results," Advances in Spatial Science, in: Luc Anselin & Raymond J. G. M. Florax (ed.), New Directions in Spatial Econometrics, chapter 2, pages 21-74, Springer.
    11. Goodspeed, Robert & Xie, Tian & Dillahunt, Tawanna R. & Lustig, Josh, 2019. "An alternative to slow transit, drunk driving, and walking in bad weather: An exploratory study of ridesourcing mode choice and demand," Journal of Transport Geography, Elsevier, vol. 79(C), pages 1-1.
    12. 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.
    13. Huang, Yuan & Wang, Xiaoguang & Patton, David, 2018. "Examining spatial relationships between crashes and the built environment: A geographically weighted regression approach," Journal of Transport Geography, Elsevier, vol. 69(C), pages 221-233.
    14. Anselin, Luc & Hudak, Sheri, 1992. "Spatial econometrics in practice : A review of software options," Regional Science and Urban Economics, Elsevier, vol. 22(3), pages 509-536, September.
    15. Xiaokun Wang & Kara Kockelman, 2007. "Specification and estimation of a spatially and temporally autocorrelated seemingly unrelated regression model: application to crash rates in China," Transportation, Springer, vol. 34(3), pages 281-300, May.
    16. Kirk, David S. & Cavalli, Nicolo & Brazil, Noli, 2020. "The implications of ridehailing for risky driving and road accident injuries and fatalities," Social Science & Medicine, Elsevier, vol. 250(C).
    17. Jones, Steven & Lidbe, Abhay & Hainen, Alex, 2019. "What can open access data from India tell us about road safety and sustainable development?," Journal of Transport Geography, Elsevier, vol. 80(C).
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