Traditionally, evaluation of intersection safety has been largely reactive and based on historical crash frequency data. However, the emerging data from connected and autonomous vehicles can complement historical data and help in proactively identifying intersections with high levels of variability in instantaneous driving behaviors before the occurrence of crashes. On the basis of data from the Safety Pilot Model Deployment in Ann Arbor, Michigan, this study developed a unique database that integrated intersection crash and inventory data with more than 65 million real-world basic safety messages logged by 3,000 connected vehicles; this database provided a more complete picture of operations and safety performance at intersections. As a proactive safety measure and a leading indicator of safety, location-based volatility was introduced; this quantified variability in instantaneous driving decisions at intersections. Location-based volatility represented the driving performance of connected-vehicle drivers traveling through a specific intersection. As such, with the use of the coefficient of variation as a standardized measure of relative dispersion, location-based volatility was calculated for 116 intersections in Ann Arbor. Rigorous fixed- and random-parameter Poisson regression models were estimated to quantify relationships between intersection-specific volatilities and crash frequencies. Although exposure-related factors were controlled for, the results provided evidence of a statistically significant (at the 5% level) positive association between intersection-specific volatility and crash frequencies for signalized intersections. The implications of these findings for proactive intersection safety management are discussed.

With the emergence of high-frequency connected and automated vehicle data, analysts can extract useful information from them. To this end, the concept of “driving volatility” is defined and explored as deviation from the norm. Several measures of dispersion and variation can be computed in different ways using vehicles’ instantaneous speed, acceleration, and jerk observed at intersections. This study explores different measures of volatility, representing newly available surrogate measures of safety, by combining data from the Michigan Safety Pilot Deployment of connected vehicles with crash and inventory data at several intersections. For each intersection, 37 different measures of volatility were calculated. These volatilities were then used to explain crash frequencies at intersection by estimating fixed and random parameter Poisson regression models. Given that volatility reflects the degree to which vehicles move, erratic movements are expected to increase crash risk. Results show that an increase in three measures of driving volatility are positively associated with higher intersection crash frequency, controlling for exposure variables and geometric features. More intersection crashes were associated with higher percentages of vehicle data points (speed & acceleration) lying beyond threshold-bands. These bands were created using mean plus two standard deviations. Furthermore, a higher magnitude of time-varying stochastic volatility of vehicle speeds when they pass through the intersection is associated with higher crash frequencies. These measures can be used to locate intersections with high driving volatilities. A deeper analysis of these intersections can be undertaken, and proactive safety countermeasures considered to enhance safety.

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