As the introduction of automated vehicles becomes feasible, even in urban areas, it will be necessary to investigate their impacts on traffic safety and efficiency. This is particularly true during the early stages of market introduction, where automated vehicles of all SAE levels, connected vehicles (able to communicate via V2X) and conventional vehicles will share the same roads with varying penetration rates. There will be zones and situations on the roads where high automation can be granted, and others where it is not allowed or not possible due to missing sensor inputs, high complexity situations, etc. In the areas where those zones merge many automated vehicles will change their activated level of automation. Therefore, we refer to these areas as “Transition Areas”. TransAID will develop and demonstrate traffic management procedures and protocols to enable smooth coexistence of automated, connected and conventional vehicles especially at Transition Areas. A hierarchical approach will be followed where control actions will be implemented at different layers including centralised traffic management, infrastructure and vehicles. First, simulations will be performed to find optimal infrastructure-assisted management solutions to control connected, automated and conventional vehicles at Transition Areas, taking into account traffic safety and efficiency metrics. Then, communication protocols for the cooperation between connected/automated vehicles and the road infrastructure are developed. Measures to detect and inform conventional vehicles will also be addressed. The most promising solutions will be implemented as real world prototypes and demonstrated under real urban conditions. Finally, guidelines for advanced infrastructure-assisted driving will be formulated. The guidelines will also include a roadmap defining activities and needed upgrades of road infrastructure in the upcoming 15 years in order to guarantee a smooth coexistence of conventional, connected and automated vehicles.

The mobility ecosystem is rapidly evolving, whereby we see the rise of new stakeholders and services. Examples of these are the presence of connected and automated vehicles, a large group of organisations that rally to establish various forms of share mobility, with the pinnacle being all of these incorporated into a large Maas ecosystem. As these new forms of mobility offerings start to appear within cities, so do the new ways in which data are being generated, collected, and stored. Analysing this (Big) data with suitable (artificial intelligence) techniques becomes more paramount, as it leads to insights in the performance of certain mobility solutions, and is able to highlight (mobility) needs of citizens in a broader context, in addition to a rise in new risks and various socio-economic impacts. Successfully integrating all these disruptive technologies and solutions with the designs of policy makers remains a challenge at current. let alone being able to analyse, monitor and, assess mobility solutions and their potential socio-economic impacts. nuMIDAS bridges this (knowledge) gap, by providing insights into what methodological tools, databases, and models are required, and how existing ones need to be adapted or augmented with new data. To this end, it starts from insights obtained through (market) research and stakeholders, as well as quantitative modelling. A wider applicability of the project’s results across the whole EU is guaranteed as all the research is validated within a selection of case studies in pilot cities, with varying characteristics, thereby giving more credibility to these results. Finally, through an iterative approach, nuMIDAS creates a tangible and readily available toolkit that can be deployed elsewhere, including a set of transferability guidelines, thus thereby contributing to the further adoption and exploitation of the project’s results.

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.

AIGGREGATE addresses the critical challenges of automated driving by enhancing safety, resilience, and human-like control in CCAM (Connected, Cooperative, and Automated Mobility) systems. While AI has advanced many driving functions, complete automation still faces significant hurdles, particularly in complex and dynamic traffic scenarios. AIGGREGATE will bridge these gaps by developing an integrated solution for the entire action chain for collective decision-making, using hybrid intelligence and human-like control. By integrating external data from vehicles, infrastructure, and other sources (V2X), the project will create a resilient collective situational awareness that goes beyond mere perception, and will include a comprehension and understanding of the traffic environment. This enhanced awareness will feed into algorithms for predicting the behaviour of road users (including the driver and VRUs) and collective decision-making. Combined, these solutions allow automated systems to anticipate and adapt to the actions of other road users, even in complex urban traffic. The user-centric design and ethical framework will ensure a human-like control which increases the acceptance. The project will develop new functionalities for automated driving, focusing on complex, real-life scenarios that require the integration of external data. This approach marks a shift from traditional systems reliant on onboard sensors only towards collective perception and decision-making. The project includes a development platform for the integration of software and physical demonstrations. The project partners include the Eindhoven University of Technology as the coordinator, along with Vicomtech, TNO, KU Leuven, University of Warwick, RWTH Aachen, IDIADA and CERTH as research partners, Infineon, Valeo France and Germany, Continental and MAPTM as industrial partners, and PAVE to support the dissemination.

The European transport faces major challenges in terms of traffic congestion, safety, greenhouse gas emissions and its derived costs. In addition, the development of disruptive technologies and emergence of new mobility solutions generate a revolution in transport network and traffic management. SYNCHROMODE aims to develop data driven ICT tools for improving the management of transport operations from a multimodal perspective and managing the overall transport network as a whole. SYNCHROMODE will provide to transport managers new predictive and network optimization capabilities for balancing the transport supply and demand, and capable of reacting to different types of events. The project will research in transport network supply& demand modelling, simulation and prediction of future states; optimization techniques for multimodal traffic optimisation, standards for data collection and storage; new governance models in transport management and new approaches for defining KPI for assessing the overall solution. SYNCHROMODE will deliver a suite of services for improving the overall transport network management, fostering the coordination of different agents involved in the provision and control of the transport services, the services are: 1) Transport network-wide data exchange and integration system; 2) Cooperative dashboard for real-time monitoring and prediction of network-wide multimodal transport and traffic; 3) Resilient multimodal transport network and traffic management support tool. SYNCHROMODE system will test and validate its results in 3 real Case studies, Thessaloniki (GR), Netherlands and Madrid (SP), with real data from various modes of transport under different traffic events, such as bottlenecks, accidents etc.