Effect of a Postviolation Driver Improvement Class on Traffic Convictions and Crashes

Abstract

Objective: To investigate the effect of a driver improvement class on postclass moving traffic violations and crashes among drivers charged with speeding.

Methods: A total of 5079 drivers who completed an 8-hour class were compared to a control group of 25,275 drivers from the same locale who had been convicted of speeding during the same time period but had not taken the class. Counts of convictions and crashes were available for all drivers for 2 years prior to the class and between 1 and 3 years after the class or key speeding conviction. Zero-inflated negative binomial models were used to measure the expected number of convictions among those who took the class compared with subjects who did not take it.

Results: Individuals with a moving violation conviction had 2.5 times the odds of having previous convictions for moving violations and almost 1.5 times the odds of having been involved in a crash. Drivers who took the class had convictions similar to the control group after the class (Incidence Rate Ratio [IRR]: 1.03, 95% confidence interval [CI]: 0.95–1.12) but were less likely to be involved in subsequent crashes (IRR: 0.83, 95% CI: 0.77–0.91).

Conclusions: The results suggest that among drivers overall, exposure to driver improvement classes as a means to change drivers’ behaviors is not significantly associated with fewer convictions for moving violations but may be effective in reducing crashes.

Keywords:

• Driver education
• Motor vehicle safety
• Motor vehicle crash

ACKNOWLEDGMENT

This study was funded in part by grant R149 CE000196 from the National Center for Injury Prevention and Control of the Centers for Disease Control and Prevention to the UNC Injury Prevention Research Center.

Notes

^a Model adjusted for age, sex, race, prior convictions due to moving violations, and prior crashes.

^b Logistic model: probability (number of moving violations >0).

*p < .001.

^a Model adjusted for age, sex, race, prior convictions due to moving violations, and prior crashes.

^b Logistic model: probability (number of moving violations >0).

*p < .001.