Various Air Traffic Flow and Capacity Management (ATFCM) measures are implemented during the pre-tactical and tactical flow management phases to resolve traffic congestion (aka hotspots); however, these are generally based on flight plan data. On the day of operation, an aircraft's actual trajectory may differ significantly from its flight plan and, as a result, hotspots still occur and these have to be resolved by Air Traffic Controllers (ATCOs) without sufficient advance notice. With today's ATFCM tools, tactical Air Traffic Control (ATC) hotspots are only identified up to around 20 minutes in advance. The aim of ASTRA is to bridge the gap between the Flow Management Position (FMP) and the planner Controller Working Position (CWP) by developing a AI-based tool (to TRL2) for FMP personnel which can predict and resolve hotspots earlier than today, before they are within the scope of the sector planner. The objectives of the project are to: (a) develop an FMP function to predict hotspots at least 1 hour in advance, and to propose strategies to resolve them; (b) develop Human Machine Interface (HMI) concepts to allow interaction between operators and the tool; and (c) demonstrate and validate the tool by conducting human-in-the-loop Real-Time Simulations (RTS) in a representative operational environment. The benefits of this tool would include: increased capacity at ATC unit level; better adherence to efficient and green business trajectories; reduced ATCO workload; and more predictable operations. The project will be carried out by a multidisciplinary consortium of 5 partners from 4 countries - Malta, Spain, Italy and Switzerland - including an academic institution, an ANSP, an Air Traffic Management (ATM) technology provider, and two consulting/research entities. The partners are complementary to each other and bring a combination of academic, technical, human factor and operational skills and expertise to the project.

TACO aims to define an automated system sufficiently powerful to both accomplish complex tasks involved in the management of surface movements in a major airport and self-assess its own ability to deal with non-nominal conditions. When needed, such system should be sensitive enough to transfer responsibilities for traffic management back to the controller, in a timely and graceful manner and in way that makes him/her comfortable with the inherited tasks. Automation is one of the key solution proposed and adopted by SESAR to tackle the challenges coming from the increase of capacity and complexity of the future ATM system. On the one hand, the programme aims at substantially reducing controller task load per flight through a significant enhancement of integrated automation support, whilst simultaneously meeting the established safety and environmental goals. On the other hand, it is envisaged that human operators will remain at the core of the system (mainly with the role of overall system managers) using automated systems with the required degree of integrity and redundancy. TACO proposes a dove tailed process to facilitate the controller’s forward thinking, also in anticipation to A-CDM (Airport- Collaborative Decision Making) amongst others. Following the two grounding principles of automation in SESAR, TACO project aims at: defining algorithms and solutions to automate and optimize both the decision making and implementation tasks for the controller involved in the ground movement of airport vehicles and aircraft; identifying and providing the controller with suitable and usable tools to supervise (monitor, tune and re-program) the system; studying the interaction between the human actors and the automation. Main focus will be on the identification of sensitive state transaction from a (fully) automated management system to conditions where the human is brought into the loop to handle situations where his/her cognitive resources are essential.

Airline pilots today consider a variety of information sources to ensure a comprehensive flight preparation and a safe flight operation. In times of an increasingly complex working environment, the workload for pilots increases permanently. The likelihood of miscalculations as a consequence of poorly prepared information is increasing there as well. This project aims to validate Pilot State Monitoring system in the cockpit which is able to provide crucial feedback of the pilot state to yield faster decision making, reduce the probability of pilot errors and enhance the fatigue risk management. The project will collect operational data and experience during nominal operations for both short and long-haul flights. It will support the development of associate concept of operations. This concept will address envisioned use cases, identify benefits, operational constraints, risks and mitigation strategies and evaluate possible future use of the system from end users such as airlines, aircraft operators and training centres. The REPS project team consists of three partners with acknowledged expertise in the aviation domain. Air Traffic Management processes and technologies with special focus on support systems for cockpit-crews are core competencies of the Institute of Flight Guidance at TU Braunschweig. TU Braunschweig will lead the consortium. The second partner, Deep Blue SRL, has a good and acknowledged track record in research on human factors as well as in validation and evaluation activities. The third partner is a start-up called CACTUSpartners GmbH. CACTUS has been founded in 2019 by two active airline pilots with consulting background and two scientists to combine operational airline expertise with scientific knowledge in all aviation domains. The partners will sub-contract Etihad Airways as airline to install and test the pilot state monitoring devices.

The main objective of SynthAIR is to explore and define AI-based methods for synthetic data generation in the domain of ATM system due to the limitation of AI-based tools development by the lack of enough data available (e.g., safety-related data) and the problem of generalization of those AI-based models. We want to explore data-driven methods for synthetic data generation, since they require 1) less user knowledge expertise (no need to derive the explicit model of the distribution), 2) better generalization capabilities. More in detail, inspired by recent advancement in Computer vision and Language Technology, we propose the concept of Universal Time Series Generator (UTG). A UTG, is a model trained on several different time series, and able to generate a synthetic dataset representing a new dataset, simply conditioned by a compressed representation of it. In aviation domain, this generator can be trained on a certain set of data related, for example to few airports, and be used to generate synthetic data from a new airport. The same principle can be applied to define a universal time series forecaster (UTF) able to do prediction to a new environment (I.e., data from a new airport) without any new training.