A low-cost pavement-rating system, based on machine learning, utilising smartphone sensors

Abstract

One of the key innovations of this study is the development of a cost-efficient approach for pavement monitoring. This work aims to develop a system that evaluates roadway pavement surface conditions with improved frequency by utilising unsupervised machine learning algorithms and smartphone sensors. The evaluation of roadways utilising complex contemporary data sets is currently conducted periodically because of the high cost of collection methods. For this purpose, the study presents a data-driven framework on the use of a vehicle, a smartphone, an on-board diagnostic device and machine learning for the rating of pavement surfaces, while statistical features are considered in both time- and frequency-domain forms. The selection of features is performed utilising unsupervised classification algorithms. Moreover, the proposed system architecture has been field-tested for the detection of pavement anomalies and the classification of five rating categories. Furthermore, the proposed system may provide daily information on roadway pavement surface conditions, which can be used by agencies for automating the planning of pavement maintenance operations and for improving driving safety.