ImAFUSA focuses on quantifying a variety of understudied factors influencing societal acceptance of UAM in urban, peri-urban and inter-urban areas, especially when these are deployed using U3 and U4 services. The Project will deliver an “Impact and capacity Assessment Framework for U-space Societal Acceptance” (ImAFUSA) to assist Local Authorities and other U-space stakeholders and users with the delivery of socially acceptable and beneficial UAM deployment in cities. The framework and its tools will provide insights into 3 major areas influencing societal acceptance: environmental impact (including noise, visual pollution and air quality), safety and socioeconomic impact (including affordability, accessibility, economic development, public space use and connectivity). In each area, innovative performance indicators will be described while mathematical formulas and algorithms will be developed to quantify them. Data on citizen noise perceptions, perceptions of visual pollution, safety perceptions and overall UAM acceptance will be collected during 3 immersive citizen experiences of UAM applications in the city of Athens, Greece. The usability of the developed indicators will be tested in a simulation environment simulating real future use cases with a varying volume and configuration of movements in U-space.

The mission of RefMap is to develop a digital service aimed at quantifying the environmental footprints of air mobility for airliners and unmanned aircraft systems (UAS) at a "multi-scale" level, where single-trajectories (micro) and the flow traffic of multiple vehicles (macro) are optimised to minimise their environmental impact in a wide range of communities. RefMap investigates how the aviation business models will be affected by the availability of environmental data for each type and route of air vehicle, as this will enable stricter evidence-based Green policy making in the sector. This will be achieved via the development of the RefMap analytics platform processing environmental and weather data such as wind, noise, CO2 and non-CO2 emissions for both U-space and ATM. This platform will rely on a number of technical solutions, including numerical simulation, predictive models, and deep-learning methods. The latter will be used to construct accurate non-intrusive prediction frameworks and to optimize the trajectories of the various vehicles given the predicted flow conditions via deep reinforcement learning (DRL). These will enable the development of a new aviation business models aligned with EU’s Green Agenda.