Identifying the principal factors influencing traffic safety on interstate highways

Abstract

This study aims at identifying the principal factors influencing fatal, nonfatal injury and non-injury traffic crashes on urban and rural interstate highway segments using a statistical approach called principal component analysis. Initially, fourteen explanatory variables including segment length, annual average daily traffic (AADT), weekday/weekend, hour of the day, urban–rural designation of the segment, median type, pavement surface condition, roadway geometric characteristics, weather, number of lanes, and drivers’ age and gender, and the accident year were considered. Separate principal component analyses are performed for the three crash categories and the aggregate dataset. The results of the analyses show that, regardless of the crash categories used, seven principal components accounting for over 70% of the variances in the original datasets were retained. In addition to the overall dimensional reduction of the original dataset by 50%, the results suggest that the key variables contributing to the crashes across the categories of the accident types observed on the freeway segments are insignificant. The retained principal component loadings of the factors PC1, PC2, PC3, and PC5 revealed the fact that the number of lanes, the median type, and the AADT of the segments are highly correlated and represented by the first factor (PC1). Similarly, other interrelated factors such as the prevailing weather and the pavement surface condition (wet, dry, snow), the hour of the day and the lighting condition, the drivers’ age and gender are well represented by PC2, PC3, and PC5 respectively.