Mixed-Effects Zero-Inflated Negative Binomial Crash Predictive Models for Unsignalized Intersections along Two-Lane Highways with Minor Roads Operating with Very Low Traffic Volumes

Abstract

Current and past investigations into road accidents have mainly focused on high-volume roads which have experienced a significant number of collisions. However, low-volume roads also record crash data which merit analysis to understand the causes of accidents and find potential countermeasures. This study focuses on unsignalized intersections linking major highways and minor stop-controlled local roads that operate with very low traffic volumes. In this scenario, an over-abundance of zero observations has to be accounted for. To address this issue, zero-inflated Poisson and negative binomial regression models have been calibrated and analyzed to find the geometric and operational factors which contribute most to the crash rate. Random effects have also been considered to account for unobserved heterogeneity inherent in the grouping of intersections within the database. The results indicate that the mixed-effect negative binomial model had the best prediction performance. Developing models for subgroups of crashes, for example, failure-to-yield and other types of crash, resulted in better prediction performance. The major road’s average annual daily traffic and the surface type (i.e., paved or unpaved) of minor road approaches had a significant effect on the frequency of all crash types. The type of intersection (three- or four-legs), the skew angle of intersection, deviation angle of near curves, and operating speed differences also had statistically significant effects on the frequency of crashes that do not include the failure-to-yield.