How instantaneous driving behavior contributes to crashes at intersections: Extracting useful information from connected vehicle message data

Connected and automated vehicles have enabled researchers to use big data for development of new metrics that can enhance transportation safety. Emergence of such a big data coupled with computational power of modern computers have enabled us to obtain deeper understanding of instantaneous driving behavior by applying the concept of driving volatility to quantify variations in driving behavior. This paper brings in a methodology to quantify variations in vehicular movements utilizing longitudinal and lateral volatilities and proactively studies the impact of instantaneous driving behavior on type of crashes at intersections. More than 125 million Basic Safety Message data transmitted between more than 2800 connected vehicles were analyzed and integrated with historical crash and road inventory data at 167 intersections in Ann Arbor, Michigan, USA. Given that driving volatility represents the vehicular movement and control, it is expected that erratic longitudinal/lateral movements increase the risk of crash. In order to capture variations in vehicle control and movement, we quantified and used 30 measures of driving volatility by using speed, longitudinal and lateral acceleration, and yaw-rate. Rigorous statistical models including fixed parameter, random parameter, and geographically weighted Poisson regressions were developed. The results revealed that controlling for intersection geometry and traffic exposure, and accounting unobserved factors, variations in longitudinal control of the vehicle (longitudinal volatility) are highly correlated with the frequency of rear-end crashes. Intersections with high variations in longitudinal movement are prone to have higher rear-end crash rate. Referring to sideswipe and angle crashes, along with speed and longitudinal volatility, lateral volatility is substantially correlated with the frequency of crashes. When it comes to head-on crashes, speed, longitudinal and lateral acceleration volatilities are highly associated with the frequency of crashes. Intersections with high lateral volatility have higher risk of head-on collisions due to the risk of deviation from the centerline leading to head-on crash. The developed methodology and volatility measures can be used to proactively identify hotspot intersections where the frequency of crashes is low, but the longitudinal/lateral driving volatility is high. The reason that drivers exhibit higher levels of driving volatility when passing these intersections can be analyzed to come up with potential countermeasures that could reduce volatility and, consequently, crash risk.