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With the emergence of high-frequency connected and automated vehicle data,analysts have become able to extract useful information from them. To this end,the concept of "driving volatility" is defined and explored as deviation fromthe norm. Several measures of dispersion and variation can be computed indifferent ways using vehicles' instantaneous speed, acceleration, and jerkobserved at intersections. This study explores different measures ofvolatility, representing newly available surrogate measures of safety, bycombining data from the Michigan Safety Pilot Deployment of connected vehicleswith crash and inventory data at several intersections. The intersection datawas error-checked and verified for accuracy. Then, for each intersection, 37different measures of volatility were calculated. These volatilities were thenused to explain crash frequencies at intersection by estimating fixed andrandom parameter Poisson regression models. Results show that an increase inthree measures of driving volatility are positively associated with higherintersection crash frequency, controlling for exposure variables and geometricfeatures. More intersection crashes were associated with higher percentages ofvehicle data points (speed & acceleration) lying beyond threshold-bands. Thesebands were created using mean plus two standard deviations. Furthermore, ahigher magnitude of time-varying stochastic volatility of vehicle speeds whenthey pass through the intersection is associated with higher crash frequencies.These measures can be used to locate intersections with high drivingvolatilities, i.e., hot-spots where crashes are waiting to happen. Therefore, adeeper analysis of these intersections can be undertaken and proactive safetycountermeasures considered at high volatility locations to enhance safety.

Extracting useful information from connected vehicle data: An empirical study of driving volatility measures and crash frequency at intersections