IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0248311.html
   My bibliography  Save this article

Associating ridesourcing with road safety outcomes: Insights from Austin, Texas

Author

Listed:
  • Eleftheria Kontou
  • Noreen McDonald

Abstract

Improving road safety and setting targets for reducing traffic-related crashes and deaths are highlighted as part of the United Nations sustainable development goals and worldwide vision zero efforts. The advent of transportation network companies and ridesourcing expands mobility options in cities and may impact road safety outcomes. 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, a 0.25% decrease in road injuries, and a 0.36% decrease in DWI offenses in Travis County. On the other hand, ridesourcing use is not significantly associated with road fatalities. This study augments existing work because it moves beyond binary indicators of ridesourcing availability and analyzes crash and ridesourcing trips patterns within an urbanized area rather than their metropolitan-level variation. Contributions include developing a data-rich approach for assessing the impacts of ridesourcing use on the transportation system’s safety, which may serve as a template for future analyses for other cities. Our findings provide feedback to policymakers by clarifying associations between ridesourcing use and traffic safety and uncover the potential to achieve safer mobility systems with transportation network companies.

Suggested Citation

  • Eleftheria Kontou & Noreen McDonald, 2021. "Associating ridesourcing with road safety outcomes: Insights from Austin, Texas," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-18, March.
  • Handle: RePEc:plo:pone00:0248311
    DOI: 10.1371/journal.pone.0248311
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0248311
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0248311&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0248311?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. 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.
    2. 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.
    3. 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.
    4. Millo, Giovanni & Piras, Gianfranco, 2012. "splm: Spatial Panel Data Models in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 47(i01).
    5. 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.
    6. 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.
    7. 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.
    8. 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).
    9. 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).
    10. Leon Moskatel & David Slusky, 2019. "Did UberX reduce ambulance volume?," Health Economics, John Wiley & Sons, Ltd., vol. 28(7), pages 817-829, July.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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).
    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. Eleftheria Kontou & Noreen C. McDonald, 2020. "Associating Ridesourcing with Road Safety Outcomes: Insights from Austin Texas," Papers 2001.03461, arXiv.org, revised Feb 2021.
    2. Roger Bivand & Giovanni Millo & Gianfranco Piras, 2021. "A Review of Software for Spatial Econometrics in R," Mathematics, MDPI, vol. 9(11), pages 1-40, June.
    3. Millo, Giovanni, 2014. "Maximum likelihood estimation of spatially and serially correlated panels with random effects," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 914-933.
    4. Matt Ruther, 2014. "The effect of growth in foreign born population share on county homicide rates: A spatial panel approach," Papers in Regional Science, Wiley Blackwell, vol. 93, pages 1-23, November.
    5. repec:rri:wpaper:201303 is not listed on IDEAS
    6. Zheng, Xinye & Li, Fanghua & Song, Shunfeng & Yu, Yihua, 2013. "Central government's infrastructure investment across Chinese regions: A dynamic spatial panel data approach," China Economic Review, Elsevier, vol. 27(C), pages 264-276.
    7. Frank Davenport, 2017. "Estimating standard errors in spatial panel models with time varying spatial correlation," Papers in Regional Science, Wiley Blackwell, vol. 96, pages 155-177, March.
    8. Badi H. Baltagi & Zhenlin Yang, 2013. "Standardized LM tests for spatial error dependence in linear or panel regressions," Econometrics Journal, Royal Economic Society, vol. 16(1), pages 103-134, February.
    9. Badi H. Baltagi & Long Liu, 2015. "Testing for Spacial Lag and Spatial Error Dependence in a Fixed Effects Panel Data Model Using Double Length Artificial Regressions," Center for Policy Research Working Papers 183, Center for Policy Research, Maxwell School, Syracuse University.
    10. Millo, Giovanni & Piras, Gianfranco, 2012. "splm: Spatial Panel Data Models in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 47(i01).
    11. Suarez, Federico & Fulginiti, Lilyan & Perrin, Richard, 2015. "The Value of Water in Agriculture: The U.S. High Plains Aquifer," 2015 Conference, August 9-14, 2015, Milan, Italy 211644, International Association of Agricultural Economists.
    12. Gianfranco Piras, 2013. "Efficient GMM Estimation of a Cliff and Ord Panel Data Model with Random Effects," Spatial Economic Analysis, Taylor & Francis Journals, vol. 8(3), pages 370-388, September.
    13. Baltagi, Badi H. & Yang, Zhenlin, 2013. "Heteroskedasticity and non-normality robust LM tests for spatial dependence," Regional Science and Urban Economics, Elsevier, vol. 43(5), pages 725-739.
    14. 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).
    15. Nicholas Campisi & Hill Kulu & Júlia Mikolai & Sebastian Klüsener & Mikko Myrskylä, 2020. "A spatial perspective on the Nordic fertility decline: the role of economic and social uncertainty in fertility trends," MPIDR Working Papers WP-2020-036, Max Planck Institute for Demographic Research, Rostock, Germany.
    16. Kouassi, Eugene & Mougoué, Mbodja & Sango, Joel & Bosson Brou, J.M. & Amba, Claude M.O. & Salisu, Afeez Adebare, 2014. "Testing for heteroskedasticity and spatial correlation in a two way random effects model," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 153-171.
    17. Nikolay Kaledin, 2013. "Societal geography and regionalization of society," ERSA conference papers ersa13p1334, European Regional Science Association.
    18. Piras, Gianfranco & Prucha, Ingmar R., 2014. "On the finite sample properties of pre-test estimators of spatial models," Regional Science and Urban Economics, Elsevier, vol. 46(C), pages 103-115.
    19. Guilherme Resende & Alexandre Carvalho & Patrícia Sakowski & Túlio Cravo, 2016. "Evaluating multiple spatial dimensions of economic growth in Brazil using spatial panel data models," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 56(1), pages 1-31, January.
    20. Elzbieta Szulc & Dagna Wleklinska, 2015. "Spatio-temporal Analysis of Convergence of Development Level of Selected Stock Exchanges in the Period of 2004–2012," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 15, pages 5-26.
    21. Hui Zhang & Haiqian Ke, 2022. "Spatial Spillover Effects of Directed Technical Change on Urban Carbon Intensity, Based on 283 Cities in China from 2008 to 2019," IJERPH, MDPI, vol. 19(3), pages 1-19, February.

    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:plo:pone00:0248311. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.