Advanced automation support developed in Wave 1 of SESAR IR includes using of automatic speech recognition (ASR) to reduce the amount of manual data inputs by air-traffic controllers. Evaluation of controllers’ feedback has been subdued due to the limited recognition performance of the commercial of the shell ASR engines that were used, even in laboratory conditions. The reasons for the unsatisfactory conclusions include e.g. inability to distinguish controllers’ accents, deviations from standard phraseology and limited real-time recognition performance. Past exploratory research funded project MALORCA, however, has shown (on restricted use-cases) that satisfactory performance can be reached with novel data-driven machine learning approaches. Based on the results of MALORCA HAAWAII project aims to research and develop a reliable, error resilient and adaptable solution to automatically transcribe voice commands issued by both air-traffic controllers and pilots. The project will build on very large collection of data, organized with a minimum expert effort to develop a new set of models for complex environments of Icelandic en-route and London TMA. HAAWAII aims to perform proof-of-concept trials in challenging environments, i.e. to be directly connected with real-life data from ops room. As pilot read-back error detection is the main application, HAAWAII aims to significantly enhance the validity of the speech recognition models. The proposed work goes far beyond the work planned for the Wave 2 IR programme and will improve both safety and reduce controllers’ workload. The digitization of controller and pilot voice utterances can be used for a wide variety of safety and performance related benefits including, but not limiting to pre-fill entries into electronic flight strips and CPDLC messages. Another application demonstrated during proof-of-concept will be to objectively estimate controllers’ workload utilising digitized voice recordings of the complex London TMA.

This project addresses the topic “Environment and Meteorology for ATM”. The framework for this project is the integration of meteorological forecast uncertainty information into the decision-making process for Flow Management Position (FMP). FMP is an operational position located in Area Control Centres (ACC) which serves as an interface between Air Traffic Control (ATC) and the Network Manager (NM) Operations Centre. FMP monitors the level of traffic in ATC sectors, adjusts the value of capacity in view of unexpected events, and coordinates possible traffic flow measures with the ACC Supervisor and the NM when an excess of demand over capacity is detected. The presence of storms challenges ATC: it makes the sector demand not easy to predict and increases the complexity, thus reducing the sector capacity. The overall objective of FMP-Met is to provide the FMP with an intuitive and interpretable probabilistic assessment of the impact of convective weather on the operations, up to 8 hours in advance, coming from the combination of the probabilistic sector demand, complexity and capacity reduction, to allow better-informed decision making. FMP-Met has the following specific objectives: Tailor multi-scale, multi-source convective weather information for FMP application; forecast multi-sector demand and complexity under convective weather; translate convective weather forecasts into predictions of reduced airspace capacity; and produce guidelines on the use of probabilistic forecasts for FMP application. The expected impact of this project is the enhancement of ATM efficiency by improving decision making in traffic flow management under convective weather. The provision of a trustworthy forecast of the future sector demand and of a reliable estimation of the impact of the convective weather in the sector capacity will support the FMP in taking anticipated, appropriate, and timely tactical flow measures, which as a consequence will lead to a reduction of delays.

One essential goal of the Single European Sky is to improve the Interoperability of the Air Traffic Management (ATM) network. The SESAR approach towards this goal is the introduction of a 4D-trajectory, which is common across all ATM stakeholders, called Reference Business Trajectory (RBT). The Flight Object concept is seen as a key enabler for implementing the RBT for Air Navigation Service Providers. The main objective of PJ27 is the demonstration that Flight Object Interoperability works in a real world upper En-route airspace environment of Air Traffic Control Centers (ACCs) from DFS, DSNA, ENAV and EUROCONTROL over spanning a large area of European core airspace. The demonstration will be executed as a set of Shadow Mode trials during which Air Traffic Controllers (ATCOs) will use Flight Object and SWIM equipped systems and perform flight coordination and transfer operations on the current air traffic as well as fallback operations. The only difference to real operations is that the ATCOs clearances will not be sent to the real aircraft. The project will assess the benefits of the Flight Object to improve ATM Network performance and situational awareness supporting demand and capacity balancing between and within the participating ACCs. The demonstration is expected to provide a proof that the Flight Object Concept is ready for deployment as required in the AF#5 of the PCP Regulation (EU) No 716/2014. This demonstration is an important milestone for Air Navigation Service Providers and the Network Manager, because it will constitute an ultimate step of validation before these stakeholders will start PCP AF#5 deployment activities. Finally the demonstration will provide valuable input into the SESAR Deployment Programme which will facilitate the coordination of PCP AF#5 deployment roadmaps and deployment scenarios.

<< Background >>The dynamic job market in air transport will require a highly qualified work force with multidisciplinary knowledge and skills for current and emerging smart occupations.In this context,universities and organisations need to better collaborate to link profesional pathways.KAAT project has fostered a new attitude towards university-business cooperation in aviation,anticipating the future demands of the aviation labour market in terms of skills and competences,ensuring a better skilled workforce<< Objectives >>1.To ensure a high quality workforce in air transport by improving the level of learning outcomes;2. To foster innovative and multidisciplinary approaches to teaching and learning and facilitate the exchange, flow and co-creation of knowledge;3. To improve the transparency and recognition tools for HE and VET;4. To enhance the international dimension of education and training through cooperation;5.To improve the dialog between education and aviation industry by the creation of ENATE association<< Implementation >>KAAT project was implemented during with the participation of 6 universities, 3 training providers and 7 aviation companies from 6 countries, with support from associated partners: NA/E ICAO Office and ACI Europe and by direct agreements with representatives from Ukraine, Republic of Moldova, Morocco and Serbia who have already expressed interest in the project results.All expected results have been achieved and even exceeded, as presented in the technical report and on KAAT website.<< Results >>Air Transport Occupations Report; Air Transport Qualifications Report; Master program “Information Technologies Applied in Aviation” (ITAA); 7 reviewed curricula for 7 existing study programs; 3 reviewed curricula for 3 existing trainings; A dual learning study program implemented; Methodology for a Sectoral Qualifications Framework in Air Transport; Methodology for Recognition of Prior Learning and Work Experience in Aviation; European Network for Aviation Training and Education

The Project encompasses the industrial research aimed to timely and efficiently create and use airspace capacity, in combination with targeted, effective demand and/or capacity measures. As such, it will focus on advanced levels of dynamic airspace configuration, Leveraging different virtualization models, digital INAP applications as well as Network-wide monitoring, all with high levels of automation. The project addresses the R&I need for on-demand air traffic services reflective of traffic demand, and the continuity of ATM service despite disruption. The project exploits the latest advancements in artificial intelligence and machine learning, to supply a variety of supporting toolsets to ATM stakeholders that enable rapid exploration of options for the deployment of capacity-on-demand solutions, whenever and wherever required. The benefits include increased en-route capacity and improved cost-efficiency of ATS provision, without compromising the current safety levels.