Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
