Pedestrian injuries from cars and SUVs: Updated crash outcomes from the vulnerable road user injury prevention alliance (VIPA)

Traffic Inj Prev. 2020 Oct 12;21(sup1):S165-S167. doi: 10.1080/15389588.2020.1829917. Epub 2020 Nov 4.

Abstract

Objective: The current short communication was written to update research on real-world pedestrian crashes. In particular, our analysis offers a preliminary update on SUV-pedestrian crash outcomes and how they differ from car-pedestrian crash outcomes. Detailed injury data were linked to vehicle features to offer a better understanding of pedestrian injury etiology.

Methods: We analyzed 82 single-vehicle crashes from the VIPA pedestrian crash database, focusing on crashes involving an SUV or car. Each crash from this database includes an in-depth analysis of police reports, pedestrian medical records, crash reconstructions, and injury attribution by a panel of experts.

Results: SUVs remain disproportionately likely to injure and kill pedestrians compared with cars, but these differences emerged primarily at crashes of intermediate speed. Crashes at low speeds and high speeds tend to produce similar injury outcomes independent of striking vehicle type (mild and fatal, respectively). The data suggest that the elevated danger to pedestrians from SUVs in these crashes may be largely related to injuries caused by impacts with the vehicles' leading edge: the bumper, grille, and headlights.

Conclusions: Although the current analysis was based on a non-nationally representative dataset, the elevated pedestrian injury risk originating from SUVs' leading edge is consistent with past research on the subject. That is, despite the changes in vehicle design and fleet composition over the past two decades, SUVs may remain disproportionately likely to injure pedestrians compared with cars.

Keywords: Pedestrians; injury attribution; injury severity; vehicle type.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Accidents, Traffic / statistics & numerical data*
  • Automobiles / statistics & numerical data*
  • Databases, Factual
  • Humans
  • Pedestrians / statistics & numerical data*
  • Wounds and Injuries / epidemiology*