Determination of accident-prone road sections using quantile regression

Abstract

The accurate identification of dangerous areas with high accident rates allowing governmental agencies responsible for improving road safety to properly allocate investment in critically accident prone road sections. Given this immediate need, this study aims to determine which segments are prone to accidents as well as the development of a hazard ranking of the accident prone road sections located within the city limits of Ocaña, Colombia, through the use of quantile regression. Based on the estimated model corresponding to quantile 95, it was possible to establish causal relationships between the frequency of accidents and characteristics such as length of the road section, width of the roadway, number of lanes, number of intersections, average daily traffic and average speed. The results indicate a total of seven accident prone road sections, for which a hazard ranking was established.