  Traditionally, evaluation of intersection safety has been largely reactive,
based on historical crash frequency data. However, the emerging data from
Connected and Automated Vehicles (CAVs) can complement historical data and help
in proactively identify intersections which have high levels of variability in
instantaneous driving behaviors prior to the occurrence of crashes. Based on
data from Safety Pilot Model Deployment in Ann Arbor, Michigan, this study
developed a unique database that integrates intersection crash and inventory
data with more than 65 million real-world Basic Safety Messages logged by 3,000
connected vehicles, providing a more complete picture of operations and safety
performance of intersections. As a proactive safety measure and a leading
indicator of safety, this study introduces location-based volatility (LBV),
which quantifies variability in instantaneous driving decisions at
intersections. LBV represents the driving performance of connected vehicle
drivers traveling through a specific intersection. As such, by using
coefficient of variation as a standardized measure of relative dispersion, LBVs
are calculated for 116 intersections in Ann Arbor. To quantify relationships
between intersection-specific volatilities and crash frequencies, rigorous
fixed- and random-parameter Poisson regression models are estimated. While
controlling for exposure related factors, the results provide evidence of
statistically significant (5% level) positive association between
intersection-specific volatility and crash frequencies for signalized
intersections. The implications of the findings for proactive intersection
safety management are discussed in the paper.
