A primary goal of all companies that have work-related driving is to prevent traffic crashes. A fundamental premise is that driver behaviors are a primary cause of such crashes.  And so, reducing risky driving behaviors can reduce crashes.  Moreover, because risky behaviors are more common than crashes, strategies to reduce these behaviors can be more efficient and proactive than those that just target crash outcomes.

In this regard, video-based data recording systems such as DrivCAM provide insights into the types of risky behaviors that are present in a driving fleet. By analyzing the location of these events, it may be possible to create maps that show where certain types of event behaviors cluster.  Moreover, by correlating these event locations with other geo-spatial databases that include weather (e.g., visibility), infrastructure (e.g., speed limits), geographic (e.g., rural roadways), and demographic (e.g., population density) information, it may be possible to predict where high-risk behaviors may occur before a fleet enters that area.  The information provided by the methodology would be useful not only for route planning and driver coaching, but could also support new technology and services that can forewarn individual drivers of high-risk conditions and give them feedback on reducing their own high-risk behaviors.