Machine Learning for Pavement Friction Prediction Using Scikit-Learn

摘要

During the last decades, the advent of Artificial Intelligence (AI) has been taking place in several technical and scientific areas. Despite its success, AI applications to solve real-life problems in pavement engineering are far from reaching its potential. In this paper, a Python machine learning library, scikit-learn, is used to predict asphalt pavement friction. Using data from the Long-Term Pavement Performance (LTPP) database, 113 different sections of asphalt concrete pavement, spread all over the United States, were selected. Two machine learning models were built from these data to predict friction, one based on linear regression and the other on regularized regression with lasso. Both models showed to be feasible and perform similarly. According to the results, initial friction plays an essential role in the way friction evolves over time. The results of this study also showed that scikit-learn can be a versatile tool to solve pavement engineering problems. By applying machine learning methods to predict asphalt pavements friction, this paper emphasizes how theory and practice can be effectively coupled to solve real-life problems in contemporary transportation.