STATISTICAL ANALYSIS OF CRASH CONDITIONS AND THEIR RELATIONSHIP TO INJURIES

Statistical models were developed relating injury probabilities in generalized body regions to crash conditions. The data available for use was the Restraint System Evaluation Program file which contains a sample of front seat occupants of 1973 through 1975 model year automobiles in 1974-1975 towaway accidents. The file had been upgraded with the inclusion of calculated crash severity parameters such as barrier equivalent velocity and various crush measures. During the analysis a number of additional crash severity parameters were formulated. A large number of statistical techniques were utilized during the course of the work including AID, cluster analysis, discriminant analysis, categorical analysis, and logistic analysis. The analyses done addressed primarily unrestrained unejected occupants in frontal and side impacts. Additionally, restrained occupants were considered to a lesser extent. Generally, the resulting models provide an injury probability description whose most significant parameter is crash severity but which also includes occupant characteristics and other parameters.

  • Supplemental Notes:
    • Prepared in cooperation with Kinetic Research, Incorporated, Madison, Wisconsin.
  • Corporate Authors:

    University of Wisconsin, Madison

    Statistical Laboratory
    Madison, WI  United States  53718

    Kinetic Research Incorporated

    5358 Blue Bill Park Drive
    ,   United States 

    National Highway Traffic Safety Administration

    1200 New Jersey Avenue, SE
    Washington, DC  United States  20590
  • Authors:
    • Klimko, L A
    • Friedman, K
  • Publication Date: 1978-6

Media Info

  • Pagination: 172 p.

Subject/Index Terms

Filing Info

  • Accession Number: 00191731
  • Record Type: Publication
  • Source Agency: National Technical Information Service
  • Report/Paper Numbers: DOT-HS-803-818 Final Rpt.
  • Contract Numbers: DOT-HS-7-01559
  • Files: NTIS, TRIS, USDOT
  • Created Date: Jun 30 1979 12:00AM