The goal of this project was to develop a strategy for maximizing the number of traffic crashes prevented by tailoring educational, rehabilitative, and license control interventions to identifiable high-risk problem driver groups. Regression models were applied to a random sample of licensed California drivers with the objective of identifying groups of drivers with elevated risks of being involved in future traffic crashes. The driving records of the risk groups identified from the models were examined to identify drivers not receiving any form of driver improvement or license control actions. The risk levels of these identified "untreated" drivers were compared with negligent operators who have received licensing actions to determine how existing discretionary and mandatory actions correlate with traffic safety risk. The defining characteristics of high-risk drivers escaping driver improvement or license control actions were examined in an attempt to construct a recommended set of countermeasures. The potential utility of these countermeasures in terms of crash reduction and benefit-cost ratios was estimated based on prior research evidence and mathematical simulation. In examining the defining characteristics of high-risk groups that currently escape driver improvement interventions, the majority was characterized either by TVS dismissals, citations, or crashes. These elements often combine with each other and with other risk factors to increase crash risk beyond that of drivers who meet the state’s prima facie definition of a "negligent operator." It is noted that there are two fundamental considerations for constructing a countermeasure system: (1) the countermeasures must be economically and operationally feasible, and (2) they must be legally permissible. Therefore, this study recommends interventions involving minimal expense, no in-person contact with DMV personnel, and no license-control actions. (Author/publisher)
Abstract