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Volatile driving, characterized by fluctuations in speed and accelerations and aggressive lane changing/merging, is known to contribute to transportation crashes. To fully understand driving volatility with the intention of reducing it, the objective of this study is to identify its key correlates, while focusing on highly volatile trips. First, a measure of driving volatility based on vehicle speed is applied to trip data collected in the California Household Travel Survey during 2012-2013. Specifically, the trips containing driving cycles (N=62839 trips) were analyzed to obtain driving volatility. Second, correlations of volatility with the trip, vehicle, and person level variables were quantified using Ordinary Least Squares and quantile regression models. The results of the 90th percentile regression (which distinguishes the 10% highly volatile trips from the rest) show that trips taken by pickup trucks, hatchbacks, convertibles, and minivans are less volatile when compared to the trips taken by sedans. Moreover, longer trips have less driving volatility. In addition, younger drivers are more volatile drivers than old ones. Overall, the results of this study are reasonable and beneficial in identifying correlates of driving volatility, especially in terms of understanding factors that differentiate highly volatile trips from other trips. Reductions in driving volatility have positive implications for transportation safety. From a methodological standpoint, this study is an example of how to extract useful (volatility) information from raw vehicle speed data and use it to calm down drivers and ultimately improve transportation safety., Comment: Presented at the 97th Annual Meeting Transportation Research Board, January 2018. Washington D.C

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 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. The intersection data was error-checked and verified for accuracy. Then, 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. 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, i.e., hot-spots where crashes are waiting to happen. Therefore, a deeper analysis of these intersections can be undertaken and proactive safety countermeasures considered at high volatility locations to enhance safety., Comment: Forthcoming, submitted for publication in Transportation Research Record: Journal of the Transportation Research Board

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.