Can Data Generated by Connected Vehicles Enhance Safety?: Proactive Approach to Intersection Safety Management
Abstract
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.
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