DURABROADS, an EU FP7 financed project launched in 2013, and led by the University of Cantabria (Spain) aims at providing a sustainable growth through the development of innovative, cost-effective and more durable pavements. The new generation of pavement is based on innovative eco-friendly nanotechnology-enhanced asphalts as well on the optimization of procedures to build and rehabilitate durable, safer and greener road infrastructure more adapted to climate change and freight corridor traffic loads. One of the objectives of this project is to identify and evaluate the existing constraints concerning currently used road materials of heavily trafficked roads (TEN-T routes) to withstand current road challenges. Due to different traffic and climate features, four European regions (Northern, Central, Western and Southern Europe) were differentiated. The climate change elements critical to various road types were identified, reviewing the pavement deterioration forms they accelerate. The traffic loads on freight corridors were evaluated considering their accelerated pavement deterioration forms. The synergistic effect of extreme climatic and mechanical loads to pavement surface was scrutinised. A comprehensive quantification methodology for extreme traffic and climatic load combinations was suggested including technical (functional), economic, environmental and social-human aspects with appropriate weighing. Then the European region-specific “optimal” asphalt wearing course types and road rehabilitation techniques for TEN-T routes were identified. The region-specific material and procedure optimization utilizes – in addition to the processing of a comprehensive literature survey – the answers coming from 81 experts of 52 European institutions to targeted questionnaire. These data were used to develop a decision support model based on AHP and TOPSIS models to facilitate the selection of asphalt pavement types. The results suggested Stone Mastic Asphalt (SMA) as the most suitable alternative in different climate change scenarios evaluated by a sensitivity analysis.