Trade-Offs Between Safety and Volatility in Driving Interactions: Evidence from A Connected Vehicle Pilot Study

Forward collision warning (FCW) systems are available in many new vehicles and are becoming increasingly popular. It is important to clearly understand their effectiveness in reducing collision risks, as well as their potential negative impacts on driving safety and volatility. This research aims to answer this question using real-world connected vehicles (CV) data collected under naturalistic settings. We extract 2,332 FCW events from the New York City CV Pilot Deployment dataset, including 1,326 events in the treatment group (warning issued) and 1,006 in the control group (no warning issued). From these FCW events, eleven volatility and nine safety variables are proposed and calculated, considering the interactions between the following and leading vehicles. These variables are further filtered using an ensemble variable elimination and selection method based on Variance Inflation Factors, Person correlation, and Akaike Information Criterion. Three binary logit models are developed for modeling volatility, safety, and both volatility and safety, respectively. These models are compared based on their log-likelihood values and the margin effects (MEs) of variables. The results show that FCW systems significantly enhance driving safety by reducing the time vehicles spend in high-risk conditions (ME: −4.59%) and the period of harsh braking to avoid collisions (ME: −5.27%) through issuing audible warnings. Despite increases in driving volatility, the overall benefits of FCW on safety outweigh the risks, with a benefit-to-risk ratio of 48.72%, resulting in a favorable net effect on driving dynamics from a statistical perspective.