On the basis of a four-year record for a large sample of Ontario drivers, we have examined several tools for the identification of such drivers and investigated how they perform. Each driver is thought to have an expected number of accidents, m. In a group of drivers with common traits (such as age, gender, record of convictions and accidents) the ms have a mean E(m) and a variance VAR(m). Estimates for all combinations of traits can be obtained withinn the framework of a multivariate statistical model. In such a model it is important to use data about previous accidents or convictions. Without much loss in estimation accuracy, one may attach a weight 1 to a conviction and 2 to an accident. Model performance is described in tangible terms: how many accidents are recorded by the drivers identified by a model, what proportion of identified drivers are "false positives", how many drivers with high m remain unidentified. We conclude that by using a mutivariate statistical model one can do substantially better than by using a demerit point scheme in which points are assigned to offenses on the basis of their perceived seriousness.

  • Availability:
  • Corporate Authors:

    Pergamon Press, Incorporated

    Headington Hill Hall
    Oxford OX30BW,    
  • Authors:
    • Hauer, E
    • Persaud, B N
    • Smiley, A
    • DUNCAN, D
  • Publication Date: 1991-6

Media Info

Subject/Index Terms

Filing Info

  • Accession Number: 00608378
  • Record Type: Publication
  • Report/Paper Numbers: HS-041 114
  • Files: HSL, TRIS
  • Created Date: May 31 1991 12:00AM