Approximation of Pavement Key Performance Indicators from Crowdsourced Data

The feasibility of Pavement KPI approximation from crowdsourced data is examined. A dedicated smartphone application for the collection of positioning and accelerometer data as well as regular manual driver inputs is used. Smartphone application data is correlated with KPIs determined using pavement defect and International Roughness Index data collected with a Laser Crack Measuring System. Statistical and machine learning models are developed for the approximation of KPIs from smartphone data, and then the resulting models are used to predict pavement KPIs beyond the training dataset. The performance of alternative models is examined and the cost savings of such an approach in the deployment and running of a Pavement Asset Management System.