Performance Evaluation of Sensor- and Image-Based Technologies for Automated Pavement Condition Surveys

Abstract

Even though companies that assess pavement condition compete to innovate by providing better software for automatic analysis and diagnosis, the industry as a whole remains limited, and data collection and storage methods are disparate. In fact, software and handling procedures are proprietary—each vendor has its own automated technology to detect, classify, and quantify surface distresses. In a research effort sponsored by the Ministry of Transportation of Ontario, Canada, the performance of sensor- and image-based pavement condition assessment was compared. First, a data management plan was created to allow efficient data manipulation. Second, a suitable set of similar distresses was selected as response variables of interest to design and conduct statistical experiments. Third, advanced analysis of variance was performed to allow statistical data comparisons among companies and among automated technologies. Finally, results were discussed and recommendations made. Overall, service provider measurements using sensor-based equipment showed no significant differences; however, those taken with digital image technology did. The implications of such outcomes, including implementation details to encourage practitioners to benefit from these preliminary results, are discussed. More broadly, road agencies are given an opportunity to revisit selection decisions concerning the acceptance or rejection of pavement data collected by a range of contractors.