Résumé: Assessing the quality of road surfaces for maintenance has typically been an expensive exercise for local government agencies. This paper evaluates the feasibility of using accelerometer and speed data collected from on-board diagnostic (OBD-II) devices and labelled using the PASER system of road classification to classify road quality and identify poor stretches. An ordinal logistic model and a support vector machine (SVM) classifier were first trained on features created from raw data. The SVM and an artificial neural network (ANN) were then trained separately on the raw data parameters followed by a convolutional neural network (CNN) model. All models have comparable results in terms of overall classification accuracy on validation datasets (around 67%) but the ANN and CNN models were superior in their ability to correctly identify ‘Poor’ and ‘Good’ stretches. The CNN model had the best performance among all models evaluated. The approach presented offers a low-cost method to map poor roads and identify stretches that require more attention as an alternative to more expensive traditional methods.

Langue: Anglais