Using traffic conviction correlates to identify high accident-risk drivers.

One of the primary missions of the California Department of Motor Vehicles is to protect the public from drivers who represent unacceptably high accident risks. Optimum fulfillment of this objective requires the development and implementation of strategies for identifying high-risk drivers. One such system in California is the department's negligent operator point system. This system assigns points to moving violations and accidents and authorizes the department to take driver control actions against drivers who meet the prima facie definition of a negligent operator. The present study explored the viability of predicting accidents from equations constructed to predict convictions for the general driving population. Equations or models that better identify drivers at increased risk of future accident involvement would increase the number of accidents prevented through post license control actions. Although the results did not support prior findings that equations keyed to citations do as well as or better than equations keyed to accidents in predicting subsequent accident involvement, a canonical correlation approach considering subsequent accident and citation rates simultaneously produced a 14.9% improvement in the classification accuracy or "hit rate" for identifying accident-involved drivers.