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An analysis of crash-safety ratings and the true assessment of injuries by vehicle

Author

Listed:
  • Cody R. Philips

    (Miami University)

  • Robert C. Garrett

    (Miami University)

  • Alan J. Tatro

    (Miami University)

  • Thomas J. Fisher

    (Miami University)

Abstract

Each year the National Highway Traffic Safety Administration and Insurance Institute for Highway Safety release safety ratings for popular makes and models of vehicles produced. We link these safety ratings with the crash data provided in the National Automotive Sampling System General Estimates System to study the efficacy of safety ratings as a predictor of the likelihood a passenger will be injured in a crash. We also consider the case of predicting a severe injury or death as a function of safety rating. A web-based dashboard was developed to graphically explore the relationship among these datasets. By considering the proportion of occupants (severely) injured per crash, we find that vehicle safety ratings generally do not have as much of an effect on likelihood of injury as one might expect, although vehicles with higher ratings can result in significant relative improvement. Our web application allows a user to explore different aspects of crashes (e.g., use of alcohol, speeding, etc.) and compare injuries and safety rating performance based on these conditions. Lastly, our dashboard allows a user to see differences between the NHTSA and IIHS ratings systems and how they correspond to the crash records.

Suggested Citation

  • Cody R. Philips & Robert C. Garrett & Alan J. Tatro & Thomas J. Fisher, 2021. "An analysis of crash-safety ratings and the true assessment of injuries by vehicle," Computational Statistics, Springer, vol. 36(3), pages 1639-1660, September.
    • Handle: RePEc:spr:compst:v:36:y:2021:i:3:d:10.1007_s00180-021-01072-9
    DOI: 10.1007/s00180-021-01072-9
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    References listed on IDEAS

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    1. C. Chatfield & G. J. Goodhardt, 1970. "The Beta‐Binomial Model for Consumer Purchasing Behaviour," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 19(3), pages 240-250, November.
    2. R. A. Rigby & D. M. Stasinopoulos, 2005. "Generalized additive models for location, scale and shape," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(3), pages 507-554, June.
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    Cited by:

    1. Roya Amjadi & Wendy Martinez, 2021. "The 2016 Data Challenge of the American Statistical Association," Computational Statistics, Springer, vol. 36(3), pages 1553-1560, September.
    2. Wen Fu & Jaeyoung Lee, 2022. "Relationship between Vehicle Safety Ratings and Drivers’ Injury Severity in the Context of Gender Disparity," IJERPH, MDPI, vol. 19(10), pages 1-14, May.

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