The relationship between demerit points accrual and crash involvement

Abstract

The objective of this study was to consider whether a driver's prior demerit points data (or their offence data used in other ways) could be used (in combination with other variables) to improve the prediction of the driver's subsequent crashes. To achieve this aim, multivariate statistical models were developed to represent drivers' crash involvements during 1993-1994 as a function of potential predictor variables measured during 1991-1992. The initial multivariate model fitted included all available driver and licence variables (that is, driver age, driver sex, driver location and endorsement of licence) as well as the driver's reported casualty crash and serious injury involvements, and total traffic convictions during 1991-1992. Adding a driver's prior offences (whether as demerit point levels or categories of offence) into this model produced the two models with the best predictive ability in identifying drivers with future crash-involvements. For both these models, the proportion of drivers amongst the 500 highest scoring drivers who were subsequently involved in 1993-1994 crashes was 12.4 percent, considerably greater than the 7.3 percent crash-involvement rate for the top 500 drivers identified by the initial model which did not use prior offence data. The corresponding crash-involvement rate for all drivers in the database was 0.76 percent. The research on the relationship between demerit points accrual and subsequent crash involvement has shown that inclusion of a driver's prior offences (whether as demerit point levels or categories of offence) in a multivariate model adds to the predictive ability of that model in identifying drivers with subsequent crash-involvements. The more efficient model uses a driver's prior offences classified into demerit point levels. Demerit points alone can be used to predict a driver's subsequent crash involvement, but an even better model can be produced by including prior casualty crash involvements as well.