Transportation sector undergoes a considerable transformation as it enters a new landscape where connectivity is seamless and mobility options and related business models are constantly increasing. Modern transportation systems and services have to mitigate problems emerging from complex mobility environments and intensive use of transport networks including excessive CO2 emissions, high congestion levels and reduced quality of life. Due to the saturation of most urban networks, innovative solutions to the above problems need to be underpinned by collecting, processing and broadcasting an abundance of data from various sensors, systems and service providers. Furthermore, such novel transport systems have to foresee situations in near real time and provide the means for proactive decisions, which in turn will deter problems before they even emerge. Our vision is to provide the required interoperability, adaptability and dynamicity in modern transport systems for a proactive and problem-free transportation system. OPTIMUM will establish a largely scalable, distributed architecture for the management and processing of multisource big-data, enabling continuous monitoring of transportation systems needs and proposing proactive decisions and actions in an (semi-) automatic way. OPTIMUM follows a cognitive approach based on the Observe, Orient, Decide, Act loop of the big data supply chain for continuous situational awareness. OPTIMUM's goals will be achieved by incorporating and advancing state of the art in transport and traffic modeling, travel behavior analysis, sentiment analysis, big data processing, predictive analysis and real-time event-based processing, persuasive technologies and proactive recommenders. The proposed solution will be deployed in real-life pilots in order to realise challenging use cases in the domains of proactive improvement of transport systems quality and efficiency, proactive charging for freight transport and Car2X communication integration.

INFRALERT aims to develop an expert-based information system to support and automate linear asset infrastructure management from measurement to maintenance. This enfolds the collection, storage and analysis of inspection data, the deduction of interventions to keep the performance of the network in optimal condition, and the optimal planning of maintenance interventions. It will also assess new construction strategic decisions. The condition of the land transport infrastructure has a big societal and economic relevance, since constraints result in disruptions of service. The demand for surface transport will significantly increase in the next years. Given budget restrictions, a substantial enlargement of the road/rail network in the next decades is doubtful. Besides, the aging infrastructure will require more maintenance interventions which infer normal traffic operation. Therefore, the only way to increase infrastructure capacity for the increased transportation demand is to optimise the performance of the existing infrastructure. This issue is addressed by INFRALERT. INFRALERT will develop and deploy solutions that enhance the infrastructure performance and adapt its capacity to growing needs by: (i) ensuring the operability under traffic disruptions; (ii) keeping and increasing the availability by optimising operational maintenance interventions and assessing strategic long-term decisions on new construction; and (iii) ensuring service reliability and safety by minimising incidences and failures. INFRALERT will be directly applicable by Rail and Road Infrastructure Managers in the field of Intelligent Maintenance and long term strategic planning. Two real pilots (for roads and rail transport systems) will be used to validate and demonstrate the results of the research activities. In both cases, extensive data from auscultation campaigns are available since some years ago. The empirical development of the whole project will be based on these pilot cases.