Random Forest–Based Pavement Surface Friction Prediction Using High-Resolution 3D Image Data

Abstract

Abstract Many studies have concluded that pavement micro- and macro- texture characteristics have significant contributions to pavement friction and thus roadway safety. This study explores novel three-dimensional (3D) areal texture parameters to represent pavement texture characteristics at both micro- and macroscales and their usage for friction prediction. Three rounds of pavement friction and texture data were collected from 2015 to 2017 at a testing site in Oklahoma with six different mixture designs. Pavement friction was acquired by a Dynamic Friction Tester, and the corresponding texture data were collected by a portable ultrahigh-resolution 3D laser scanner. Each 3D texture datum is decomposed into micro- and macroscales with predesigned Butterworth filters. Twenty-seven 3D texture parameters falling into five categories are calculated at both texture levels. Subsequently, the random forest algorithm is implemented to determine the most important texture parameters for friction predictive model development. The selected macro- and micro-texture parameters account for 48.8 % and 39.6 % contributions to high-speed friction and 50.0 % and 14.1 % contributions to low-speed friction. The temperature during testing also exhibits a significant impact, with 11.6 % and 35.9 % contributions to high- and low-speed friction, respectively.