A GIS based approach for predicting pavement deterioration on the UK road network

The U.K. Road network is a complex and dynamic system, managed by a federated network of organizations, often with stringent constraints on resourcing. Remediating deterioration of the network is both costly and politically sensitive; resilience, sustainability, and utilisation impacts must be constantly balanced by asset managing organizations. An approach for localized prediction of deterioration is developed in this paper, combining remote sensing and automated survey data into a GIS-based model to support decision making. In addition, predictive models are proposed in this paper, based on random forest regression and classification approaches, which use the proposed data model to create localized deterioration profiles. The machine learning model has been trained with and validated against data from a 21km length of the UK Major Roads network, using ~10 years of condition data and authoritative traffic, cartographic, and environmental data from several UK government agencies. Both the regression model and the classifier can accurately predict condition metrics, described in industry standards. This approach allows for early detection and mitigation of pavement failure and support maintenance operations on the network whilst minimizing disruption and maximizing return on investment.