Evaluation of Pavement Roughness Using an Android-Based Smartphone

Abstract

Modern smartphones are equipped with many useful sensors, such as gyroscopes, magnetometers, global positioning system (GPS) receivers, and three-dimensional (3D) accelerometers. In this study, smartphones’ 3D accelerometers were used for collecting a vehicle’s vertical acceleration data. Through the use of various signal processing and pattern recognition techniques, such as cross correlations, Welch periodograms, and variance analyses, the measured signals (time series acceleration data) were identified and correlated with the actual international roughness index (IRI) values. It was found that the variance among the vertical acceleration measurements was the key feature for classifying the measured signals. A validation analysis was also conducted to measure the reliability of this methodology. The initial validation results suggested that, using this methodology, the smartphone used could predict with reasonable certainty the actual IRI values. The study was performed on 35 roadway segments extracted from the Wyoming local roads pavement management system (PMS). Also, the selected segments have various lengths and geometric features reflecting the actual roadway segments under any PMS. The major advantage of this technique includes the low-cost solution of measuring local roadway roughness.