Automated Road Transport (ART) is seen as one of the key technologies and major technological advancements influencing and shaping our future mobility and quality of life. The ART technology encompasses passenger cars, public transport vehicles, and urban and interurban freight transport and also extends to the road, IT and telecommunication infrastructure needed to guarantee safe and efficient operations of the vehicles. In this framework, CARTRE is accelerating development and deployment of automated road transport by increasing market and policy certainties. CARTRE supports the development of clearer and more consistent policies of EU Member States in collaboration with industry players ensuring that ART systems and services are compatible on a EU level and are deployed in a coherent way across Europe. CARTRE includes a joint stakeholder’s forum in order to coordinate and harmonise ART approaches at European and international level. CARTRE creates a solid knowledge base of all European activities, supports current activities and structures research outcomes by enablers and thematic areas. CARTRE involves more than 60 organisations to consolidate the current industry and policy fragmentation surrounding the development of ART.

Highly automated vehicles and cooperative ITS technology will get more and more present in the near future. By combining both, guidance of (groups of) such vehicles can considerably improve especially in urban areas. For management of automated vehicles at signalized intersection and corridors, the MAVEN (Managing Automated Vehicles Enhances Network) project will develop infrastructure-assisted platoon organization and negotiation algorithms. These extend and connect vehicle systems for trajectory and maneuver planning and infrastructure systems for adaptive traffic light optimization. Traffic lights adapting their signal timing to facilitate the movement of organized platoons and reversely will yield substantial better utilization of infrastructure capacity, reduction of vehicle delay and reduction of emission. The MAVEN project will build a system prototype for both field tests and extensive modeling for impact assessment, contribute to the development of enabling technologies such as communication standards and high-precision maps, and develop ADAS techniques for inclusion of vulnerable road users. Additionally, MAVEN will include a user assessment and the development of a roadmap for the introduction of vehicle-road automation to support road authorities in understanding changes in their role and the tasks of traffic management systems. Finally, MAVEN will white paper on ‘management of automated vehicles in a smart city environment’ will be written to position the MAVEN results in the broader perspective of passenger transport in smart / future cities and to embed them with smart city principles and technologies as well as service delivery.

The assurance of security, privacy, reliability and safety features is key-point to unlock the enormous potential that the connected vehicles systems paradigm i.e., the dynamic Cyberphysical system of highly-equipped infrastructure-connected vehicles with numerous third-party components, can offer towards safer transportation. The emerging systems expose a variety of wireless-communication and hardware interfaces which result in a large attack surface; thus, attempts to assess the degree of confidence that security needs are satisfied come with prohibited cost for automotive stakeholders and OEMs. SAFERtec project will leverage a highly-skilled consortium to first model the varying exposure of a prototype connected vehicle system to numerous threats appearing under two generic instances of the increasingly pervasive V2I setting. One relates to road-side unit communication while the other involves the interaction with cloud application and passengers' smart devices. Then, adopting a systematic vertical approach SAFERtec will obtain an in-depth look of the possible vulnerabilities performing penetration-testing on individual hardware components and upper-layer V2I applications. Considering the available security mechanisms a third party provider already applies to each module, SAFERtec will determine a corresponding protection profile as a summary of the identified risks. An innovative framework appropriately designed for unified and thus, cost-effective use across all modules will employ statistical tools and security metrics to quantify the involved security assurance levels and also feed the incomplete automotive standards. Research on dependability methods will then allow the framework's transition from individual modules to the connected vehicle system. All above results will be incorporated and made available through an open-access toolkit that will pave the way towards the cost-effective identification of security assurance levels for connected vehicle systems.

Big Data will have a profound economic and societal impact in the mobility and logistics sector, which is one of the most-used industries in the world contributing to approximately 15% of GDP. Big Data is expected to lead to 500 billion USD in value worldwide in the form of time and fuel savings, and savings of 380 megatons CO2 in mobility and logistics. With freight transport activities projected to increase by 40% in 2030, transforming the current mobility and logistics processes to become significantly more efficient, will have a profound impact. A 10% efficiency improvement may lead to EU cost savings of 100 BEUR. Despite these promises, interestingly only 19 % of EU mobility and logistics companies employ Big Data solutions as part of value creation and business processes. The TransformingTransport project will demonstrate, in a realistic, measurable, and replicable way the transformations that Big Data will bring to the mobility and logistics market. To this end, TransformingTransport, validates the technical and economic viability of Big Data to reshape transport processes and services to significantly increase operational efficiency, deliver improved customer experience, and foster new business models. TransformingTransport will address seven pilot domains of major importance for the mobility and logistics sector in Europe: (1) Smart High-ways, (2) Sustainable Vehicle Fleets, (3) Proactive Rail Infrastructures, (4) Ports as Intelligent Logistics Hubs, (5) Efficient Air Transport, (6) Multi-modal Urban Mobility, (7) Dynamic Supply Chains. The TransformingTransport consortium combines knowledge and solutions of major European ICT and Big Data technology providers together with the competence and experience of key European industry players in the mobility and logistics domain.

Big Data integration in European cities is of utmost importance for municipalities and companies to offer effective information services, enable efficient data-driven transportation and mobility, reduce CO2 emissions, assess the efficiency of infrastructure, as well as enhance the quality of life of citizens. At present this integration is substantially limited due to the following factors: 1) Urban Big Data is locked in isolated industrial and public sectors, and 2) The actual Big Data integration is an extremely hard technical problem due to the heterogeneity of data sources, variety of formats, sizes, quality as well as update rates, such that the integration requires significant human intervention. QROWD addresses these challenges by offering methods to perform cross-sectoral streaming Big Data integration including geographic, transport, meteorological, cross domain and news data, while capitalizing on human feedback channels. The main objectives of QROWD are: (1) Facilitating cross-sectoral Big Data stream integration for urban mobility including real-time data on individual and public transportation combined with further available sources, such as weather conditions and infrastructure information to create a comprehensive overview of the city traffic; (2) Supporting participation and feedback of various stakeholder groups to foster data-driven innovation in cities; and (3) Building a platform providing hybrid computational methods relying on efficient algorithms complemented with human computation and feedback. The main outcomes of QROWD are: (1) Two data value chains in the sectors of urban mobility and public transportation using a mix of large scale heterogeneous multilingual datasets; and (2) Cross-sectoral and cross-lingual technology, including algorithms and tools covering all phases of the cross-sectoral Big Data Value Chain building on W3C standards and capitalizing on a flexible and efficient combination of human and machine-based computation.