Comparison of Machine Learning Methods for Evaluating Pavement Roughness Based on Vehicle Response

Abstract

The roughness of a road pavement affects safety, ride comfort, and road durability. A useful indicator for evaluating roughness is the weighted longitudinal profile (wLP). In this paper, three machine learning models are compared for estimating the wLP when vehicle response information, i.e., accelerometer and wheel speed data is collected from common in-vehicle sensors. A multilayer perceptron, support vector machine (SVM) and random forest were applied for testing their effectiveness in estimating the key indices of wLP, namely, range and standard deviation. These models were trained from a set of features extracted from vehicle response simulations on accurate replications of roads with various roughness problems. In contrast to other research, the authors validated models with measurements collected with a probe vehicle. The results show that roughness phenomena can be accurately detected. The SVM produced the best results, although the models achieved rather similar performance. However, differences were found regarding the model robustness when reducing the size of the training feature set. The proposed method enables road network monitoring to be achieved by conventional passenger cars, which can be seen as a practical supplement to the prevalent road measurements with cost-intensive mobile devices.