MODELING THE RELATIONSHIP OF ACCIDENTS TO MILES TRAVELED

TLDR

Consideration of highway safety studies in a time-space domain is used to introduce the concept that different study designs result in different underlying probability distributions describing accident occurrence. The study proposes Poisson regression as a superior alternative to conventional linear regression for safety studies, citing smaller sample size requirements and other desirable statistical properties. The authors estimate Poisson regression models using accident, travel mileage, and environmental data from the Indiana Toll Road. A pooled model shows accident occurrence increases with automobile VMT, truck VMT, and snowfall hours, while segmentation reveals automobile accidents are more sensitive to environmental conditions than truck accidents, providing clearer insight into mileage effects.

Abstract

Consideration of highway safety studies in a time-space domain is used to introduce the concept that different study designs result in different underlying probability distributions describing accident occurrence. Poisson regression is proposed as a superior alternative to conventional linear regression for many safety studies because it requires smaller sample sizes and has other desirable statistical properties. Models are estimated using accident, travel mileage, and environmental data from the Indiana Toll Road. A pooled model including all accidents revealed that accident occurrence increases with automobile vehicle miles of travel (VMT), truck VMT, and hours of snowfall. Segmentation of the data into subsets that describe different types of collisions revealed that automobile accidents are much more sensitive to environmental conditions than are truck accidents. Use of the segmentation technique allowed a much clearer understanding of the effects of travel mileage on accident occurrence than could have been obtained from the pooled data alone.