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

TL;DR

This study analyzes high-frequency connected vehicle data to identify driving volatility measures that correlate with crash frequency at intersections, providing a new approach for proactive safety management using empirical data.

Contribution

It introduces and validates multiple measures of driving volatility from connected vehicle data as predictors of intersection crash risk, enhancing safety analysis methods.

Findings

Higher driving volatility measures are linked to increased crash frequency.

Vehicle data points beyond threshold-bands correlate with more crashes.

Time-varying speed volatility is a significant crash predictor.

Abstract

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.