Multiple Applications of Multivariate Adaptive Regression Splines Technique to Predict Rear-End Crashes at Unsignalized Intersections

Abstract

Crash prediction models are used extensively in highway safety analysis. This paper discusses a recently developed data-mining technique to predict motor vehicle crashes: the multivariate adaptive regression splines (MARS) technique. MARS shows promising predictive power and does not suffer from a black-box limitation. Negative binomial (NB) and MARS models were fitted and compared with the use of extensive data collected on unsignalized intersections in Florida. Two models were estimated for rear-end crash frequency at three-and four-legged unsignalized intersections. Treatment of crash frequency as a continuous response variable to fit a MARS model was also examined by normalizing crash frequency with the natural logarithm of the annual average daily traffic. The combination of MARS with a machine learning technique (random forest) was explored and discussed. The significant factors that affected rear-end crashes were traffic volume on the major road, upstream and downstream distances to the nearest signalized intersection, median type on the major approach, land use at the intersection's influence area, and geographic location within the state. The study showed that MARS could predict crashes almost like the traditional NB models, and its goodness-of-fit performance was encouraging. The use of MARS to predict continuous response variables yielded more favorable results than its use to predict discrete response variables. The generated MARS models showed the most promising results after the covariates were screened by using random forest.