Towards safer roads: machine learning models for predicting pavement friction in CRCP

Abstract Road safety is strongly influenced by pavement friction, which governs tire traction and braking efficiency, especially in wet conditions. Conventional friction evaluation methods, while widely used, are time-consuming, costly, and often lack generalizability. This study investigates advanced machine learning (ML) techniques for predicting the friction number of continuously reinforced concrete pavement (CRCP) using data from the Long-Term Pavement Performance (LTPP) database. The dataset comprised 170 observations from 33 CRCP sections, representing different climatic and structural conditions. A 5-fold cross-validation approach was employed within MATLAB’s Regression Learner App to ensure robust and unbiased model evaluation. Six ML models were examined, including regression trees, support vector machines (SVM), ensemble methods, Gaussian Process Regression (GPR), artificial neural networks (ANN), and kernel-based approaches. Results show that the Rational Quadratic GPR model achieved the highest predictive accuracy (R² = 0.70, RMSE = 5.29, MAE = 3.90), outperforming other conventional machine learning algorithms used for comparison. Feature importance and sensitivity analyses revealed that pavement age, traffic loads, thickness, temperature, and humidity are the most influential factors affecting surface friction. The findings provide practical insights for data-driven pavement management, offering transportation agencies reliable tools to enhance safety, optimize maintenance strategies, and extend pavement service life. Although the dataset size is moderate, the consistent cross-validation results indicate strong model reliability; future studies using larger and more diverse datasets are recommended to further validate the model’s generalizability.