Identifying Optimal High-Risk Driver Segments for Safety Messaging

Given the public safety risk posed by high-risk drivers, most traffic safety agencies consider this group a key target for strategic planning purposes. The aim of this research was to develop a framework that could be used to efficiently and effectively target high-risk drivers. The specific objectives were to establish whether high-risk drivers were homogeneous and if not, to determine the optimal set of primary and secondary clusters for efficient and effective targeting with minimal resources. The study area was Saskatchewan, Canada. Multiple databases (including traffic collisions, insurance claims, and conviction data) formed the basis for the research. In this study, high-risk drivers were defined as all drivers who were enrolled both in the Driver Improvement Program of Saskatchewan Government Insurance and in the negative or penalty zone of Saskatchewan Government Insurance's safety driver rating scale as a result of accumulated demerit points. Geodemographic modeling with the neighborhood as the unit of analysis, a large number of variables, and a set of probabilistic clustering techniques were used in the analysis. The results indicated that the high-risk driver group was heterogeneous; it fell into subclusters with varying collision and traffic behavior profiles. The study found that Saskatchewan high-risk drivers were mainly in the major cities (56%) but also in rural municipalities (18%) and towns (15%). The optimal primary high-risk segments for efficient targeting were those major cities and towns where both the risk of collision involvement and the concentration of high-risk drivers were higher than the driver population. Drivers in the primary target area for messaging showed higher levels of distracted, impaired, and aggressive driving behaviors; driver inexperience; extreme fatigue; falling asleep behind the wheel; and inattention.