Incorporating Driving Behavior Metrics Derived from Naturalistic Driving Data into Macroscopic Safety Modeling

Abstract

This research leveraged datasets from the Strategic Highway Research Program 2 (SHRP2) Naturalistic Driving Study (NDS) to explore the potential benefits of incorporating macroscopic measures derived from NDS into traditional safety modeling. Large datasets with traversals from more than 1,700 unique drivers were used to extract driving behavior on freeway segments. New sets of time series totaling over 1,600 h of driving were developed, including vehicle dynamics exclusively during car-following, while also tracking the spacing between the instrumented vehicle and the vehicle being followed. This paper focuses on the statistical modeling of crash frequency incorporating macroscopic metrics derived from the new time-series datasets as regards the mean, median, variance, and 85th percentile of vehicle spacing, vehicle speed, and traffic density. Results of this exploration indicate that an increase in the traffic density variance, an increase in the speed variance, and a decrease in the mean vehicle spacing had significant effects associated with increases in multi-vehicle crash frequencies. These results can be used to estimate the safety effect of countermeasures that may change speed, density, and or spacing, along with changes in annual average daily traffic (AADT).