Predicting Crashes on Expressway Ramps with Real-Time Traffic and Weather Data

Abstract

Limited research has been conducted on real-time crash analysis of expressway ramps, although there have been many studies in recent years on estimating real-time crash prediction models for main lines. This study presents Bayesian logistic regression models for single-vehicle (SV) and multivehicle (MV) crashes on expressway ramps by using real-time microwave vehicle detection system data, real-time weather data, and ramp geometric information. The results find that the logarithm of the vehicle count, average speed in a 5-min interval, and visibility are significant factors for the occurrence of SV and MV crashes. The Bayesian logistic regression models show that curved ramps and wet road surfaces would increase the possibility of an SV crash, and off-ramps would result in high risk of MV crashes. The high standard deviation of speed in a 5-min interval would significantly increase MV crash likelihood. Random Forests software was applied in variable importance analysis, and the results revealed that the most important factors influencing crashes on ramps were traffic variables, the second most important factors are weather variables, and the least important but still significant factor was the ramp geometry.