A NEW METHOD FOR PREDICTING CRASHWORTHINESS

Consumer information concerning the predicted 'safeness' of a new car model is based on the results of crash tests. Unfortunately, because i t allows comparisons only within size/weight groups, the information i s somewhat incompatible with the normal car-purchase decision process since consumers often consider cars within different groups. In additi on, based on past research, the association of the crash-test informat ion with real-world crash outcomes is, at best, somewhat limited. The goal of this study was to explore a methodology for improving this inf ormation, a methodology which incorporates not only the crash-test inf ormation, but also information concerning real-world occupant injury e xperience in prior crashes involving similar vehicles ('clones'). The clone information included both driver injury severity in past clone c rashes from the North Carolina accident file and various indicators of relative driver injury in clones extracted from published insurance-r elated data from the Highway Loss Data Institute (HLDI). Final models developed included both measures of the Head Index Criteria (HIC) from the crash test and some measure of clone performances as significant predictors. While the North Carolina clone data is intuitively 'cleane r' in that it describes injury level per crash rather than per insured year, the medical claims indices from the HLDI data consistently were shown to be the stronger predictors. Future research will need to loo k at ways of better combining the crash-test variables and of possible modifications to the HLDI indices. In general, the analyses generated encouraging results that appear to point to possible improvements in the crashworthiness information. Copyright (C) 1997 Elsevier Science L td.