A sample of 827,955 records of Ontario drivers containing information about age, gender, convictions, accidents, demerit points, and suspensions for 1981-1984 has been examined. On this basis 16 alternate models to estimate a driver's accident potential have been formulated. It appears that the currently used demerit point system, wherein the number of points associated with an offense reflects the perceived seriousness of the offense, is not a good predictor of accident potential. One can predict better by relying on the driver's record of accidents and convictions and still better by making use of a model for which the "regression weights" have been rigorously estimated. The performance of alternative models for the estimation of drivers' accident potential is described in terms of "hits" and "false alarms." It is shown, for example, that if the top 10,000 drivers are selected by the best model, 3,757 of these are expected to have an accident potential in excess of four times the population average; these are the "hits." Of the same 10,000, one should expect 792 to have an accident potential that is below the population average. These are the "false alarms." The best model uses age, gender, total accidents, and 14 conviction categories. This model identifies approximately twice as many high accident potential drivers as the current demerit point system. Even the simplest model, which uses total convictions as the only variable, predicts 50% more high accident potential drivers than the current system.

Media Info

  • Features: Figures; References; Tables;
  • Pagination: p. 53-64
  • Monograph Title: Application and management of accident data
  • Serial:

Subject/Index Terms

Filing Info

  • Accession Number: 00495033
  • Record Type: Publication
  • ISBN: 0309049741
  • Files: TRIS, TRB, ATRI
  • Created Date: Jun 30 1990 12:00AM