The overall goal of EvoATM is to build a framework to better understand and model how architectural and design choices influence the ATM system and its behaviors, and vice versa how the expected ATM overall performances drive the design choices. The EvoATM project will model a specific part of ATM system combining the agent based paradigms with evolutionary computing. Specifically it will define a solver which finds an optimal tuning of the design of new/modified ATM components to accomplish the expected performances. It will adopt sensitivity analysis strategies in order to understand the influence of ATM components parameters on the behaviours at component performances level (behaviours of other components) and at whole system performances level. It will test the framework by using known scenarios and quantitative indicators to validate its effectiveness in terms of: change impact assessment, support to design and support to strategic thinking. The project will provide methodological guidelines to extend the proposed frameowrk to any part of ATM to be modelled. The EvoATM consortium involves a multidisciplinary group of experts representing the different expertise classes: complex systems modelling, evolutionary computing, ATM modelling, human aspects, aerospace engineering, verification and validation. The chosen Advisory Board will guarantee the coordination/dialogue with the impacted stakeholders domains: ASDA (Association for the Scientific Development of ATM in Europe),ENAV (Italian air navigation service provider), EUROCONTROL, LEONARDO (Italian industry).

Automation effects on arousal could be predicted differently depending on the Attentional Theory. The classical Theory (Kahneman, 1973) considers the level of arousal reliant only on psychological factors (stress, fatigue and emotions). Automation would only affect the task complexity by allocating part of the cognitive processing to the system. Alternative theories such as Malleable Attentional Resources Theory (MART) (Young and Stanton, 2002) assumes that automation would also affect the level of arousal and be dependent on controller´s expectations: when the ATCo expects that the task is easy in the near future, she/he will reduce the arousal levels and get bored or sleepy (overconfidence on automation). On the contrary, fears of automation failing would increase stress and also the level of arousal causing disorientation, overacting or erratic behaviour. Based on these theories, AUTOPACE proposes basic research on a Psychological Model to quantitatively predict how automation would impact on human performance based on cognitive resources modeling (demanded and available), tasks characteristics (automation), psychological factors modeling (fatigue, stress and emotions) and ATCo expectations (overconfidence vs fears of automation). A catalogue of training strategies to support the controller being “in-the-loop” will be explored. For the classical Theory, the strategies only for keeping attention on the main task avoiding out-of-the-loop effect. For the MART the coach will be also for coping with stress. A reviewed Curricula and ATCo Selection will be initiated. Expert Judgment from Psychologists, ATM Experts and Controllers Trainers supported by Literature Research will look at future competences and training strategies. The research on Psychological Modeling will be also sustained with Analytical Studies by using an existing prototype for demanded resources. AUTOPACE points at research paths suggested in “Ergonomics in design” Issue (Hancock et all, April 2013).

Capacity Management proposed by SESAR 2020 is achieved by applying advanced airspace management processes materialised in the management of Dynamic Airspace Configurations (DAC) solution. DAC solution develops sector design, sector configurations and opening schemes processes to optimise the use of the available capacity and balance the ATC workload. Flight Centric ATC solution strengthens Capacity Management by providing additional flexibility and cost-effectiveness as it proposes that controllers are no longer in charge of managing the entire traffic within a given sector. Capacity Management processes benefit from combining these two solutions increasing flexibility in case of “sudden” demand/capacity changes. Trajectory-Based Operations (TBO) allows obtaining reliable information related to trajectory uncertainty thanks to the better reliability of the available trajectory information. Assessment and definition of a proper use of this trajectory uncertainty information within Capacity Management processes will significantly enforce its effectiveness. Optimisation of the Capacity Management processes can be achieved not only incorporating the abovementioned trajectory uncertainty into their associated demand and capacity model, but also developing trajectory-based complexity metrics more suitable to the most innovative aspects of DAC and Flight Centric solutions. COTTON aim is to maximise the effectiveness of the Capacity Management processes in TBO taking full advantage of the available trajectory information. Three sub-objectives are identified: 1. Improve the use of trajectory-based complexity and workload assessment to support Capacity Management enabled by Trajectory-Based Operations (TBO) including uncertainty. 2. Identify and promote the benefits of Trajectory-Based Operations to develop innovative demand/capacity models based on Dynamic Airspace Configuration and Flight Centric ATC solutions. 3. Explore DAC and Flight Centric ATC solutions integration.

The increasing interest in Synthetic Vision (SV) and Augmented Reality (AR) technologies has led various analysts to positively esteem the adoption of new tools enabling pilots and controllers to seamlessly operate under Visual Meteorological Conditions and Instrument Meteorological Conditions. The RETINA project will investigate the potential and applicability of SV tools and Virtual/Augmented Reality (V/AR) display techniques for the Air Traffic Control (ATC) service provision by the airport control tower. Within the project, several concepts and basic principles that have been observed in different areas (e.g. Remote Tower, Synthetic Vision Systems, AR, Information Technologies, etc.) will be brought to the level of maturity required for the Applied Research that will be conducted in SESAR V1-V3 (Applied Research, Industrial Research & Validation). To this end, a 3D airport model will be developed, along with V/AR based human-computer interfaces. The digital model will provide controllers with precise positioning for both aerial and terrestrial objects, drawing information from multiple, simulated, data sources, such as the System Wide Information Management (SWIM) network, Remote Towers sensing technologies and other well-established surveillance systems – e.g. Airport Surveillance Radar (ASR) and the Surface Movement Radar (SMR). The interface design will be based on the Ecological Interface Design approach. Finally, the project will investigate the impact of the newly conceived tools on the control tower air traffic management procedures. On the whole, those tasks that are negatively affected by poor visibility conditions, such as bad weather, fog, smoke, dust or any other kind of environmental occlusion, will become weather-independent. The RETINA project primarily relates to SESAR ER-06-2015 - High Performing Airport Operations - Improved Visualisation and Awareness, but also has a secondary relationship to SESAR ER-03-2015 - Information Management in ATM.