ATM application-oriented Research for Capacity-on-demand and Dynamic Airspace

Project results are expected to contribute to the following expected outcomes.

• Capacity. Project results are expected to contribute to capacity by introducing new digital services (e.g. enhanced network traffic prediction and shared complexity representation, improved demand–capacity balancing (DCB) processes, improved airline operations).
• Cost-efficiency. Project results are expected to improve the training process for ATCOs and their performance in highly automated environments, and thus their productivity.
• Operational efficiency. Project results are expected to improve predictions of the evolution of the network in terms, for example, of traffic flows, complexity assessment, calibration of airspace/sector capacity, to improve airspace users’ (AUs’) decision-making processes and the accuracy of short-term predictions of risk of propagation of disruption at network level.
• Safety. Project results are expected to maintain at least the same level of safety as the current ATM system.

The SESAR 3 JU has identified the following innovative research elements that could be used to achieve the expected outcomes. The list is not intended to be prescriptive; proposals for work on areas other than those listed below are welcome, provided they include adequate background and justification to ensure clear traceability with the R&I needs set out in the SRIA for the capacity on demand and dynamic airspace flagship.

• Digital network services (R&I need: on-demand ATS). This research will address:
  • the improvement of DCB by enhancing trajectory prediction, integrating uncertainty assessment, robust planning and cost-efficiency assessment at network level;
  • the integration of network and local tools used by AUs, airports and ANSPs (e.g. flow management position (FMP) and integrated network management and ATC planning (INAP)) in a rolling and dynamic process, including further automation support in the coordination of DCB actions from long-term to execution phases;
  • hotspot management using traffic monitoring values to meet various objectives (safety, rate optimisation, critical situations, etc.) and to identify and address different types of spots (regions of interest);
  • the use of modelling and operational data to understand typical resolutions to network planning and traffic management problems, with the aim of developing optimisation capabilities that are less human-centric.
• Future digital network services (R&I need: on-demand ATS). The research will address:
  • ML to identify and exploit information patterns, and AI to identify and design new elementary basic sector volumes for complexity detection and resolution, while balancing workloads and optimising resources;
  • the use of big data analysis, ML and digital-twin techniques to better plan the reactions of various actors (ATCOs, FMPs, AUs) to potential operational improvements based on emerging trends (e.g. incentives);
  • innovative DCB resolution algorithms, for example using radically different algorithms from those currently in use or using alternative approaches;
  • the use of new data sources (e.g. big data), ML algorithms (including neural networks), AI-based decision support tools, behavioural economics, improved market modelling, complexity science, etc., to support network operations (e.g. models and methods for improving demand, flow and complexity forecasting and resolution);
  • the use of big data and ML to identify best practices regarding regulation strategies for particular traffic-load patterns based on historical data, and the development of optimised strategies for the most frequent traffic-load situations in the European air traffic flow and capacity management (ATFCM) network.
• Digital airline operations. This element will cover improvements to airline operations based on the use of digital technologies (e.g. big data, ML algorithms, AI, IoT, behavioural economics, improved market modelling, complexity science, etc.) to support airline decision-making processes in disruption scenarios; the integration of airline operations into the network; collaboration between flight operations centres (FOCs), the network management function and ATC; and the better consideration of airspace users’ preferences in DCB and sequencing processes. The use of new data sources (big data), ML algorithms, AI-based decision support tools, etc., to support airline decision-making in disruption scenarios is expected to increase the resilience of the system (R&I need: on-demand ATS).
• New trajectory pricing schemes. This element will cover the development and initial validation of new trajectory pricing schemes to support more flexible distribution of the demand (R&I need: on-demand ATS).
• Flexible flight level (FL) structure. The aim of the research is to support the NM’s calculation of the optimal division of flight level structure for specific periods of time, develop processes for agreement with ATSUs and create tools to integrate flexible division of flight levels into ATM systems (R&I need: on-demand ATS).
• Improved prediction of network evolution. The research is about the use of ML techniques for the identification and prediction of major traffic flows, complexity assessment, calibration of airspace/sector capacity, flight delays, estimated arrival and overflight times, etc., with the objective of reducing the network’s capacity buffers and improving the handling of AU priorities/preferences and disruption management. In addition, the improvement of ATFM processes through the inclusion of convective weather information should be addressed (R&I need: ATM continuity of service despite disruption).
• Improved decision-making processes among AUs. Enriched DCB information and enhanced what-ifs available to improve AUs’ decision-making processes when planning or replanning trajectories should be addressed through this element. Enriched DCB information will encompass DCB constraints/measures, information such as ATFCM regulations / calculated take-off time (CTOT) / short-term ATFCM measures (STAM), and additional DCB information such as hotspots and congestion level indicators (R&I need: future data services and applications for airport and network).
• Full integration and connectivity of ATM operations. This research will investigate the integration of connectivity into the loop of ATM operations and the new datasets available through A-CDM, UDPP, airport operations plan (AOP) / the network operations plan (NOP), target time over/arrival and extended AMAN demand in order to further develop the rules for ATFCM and queuing priorities (R&I need: future data services and applications for airport and network).
• New operational and social indicators for airspace users. The scope of this element includes the definition and validation of new operational and social indicators for airspace users. These should be integrated into the overall R&I performance framework, building on the results of SESAR validations, identifying gaps in the required knowledge and determining the steps to be taken from research to pre-industrialisation and deployment (with full integration of operational processes and systems’ interoperability) (R&I need: future data services and applications for airport and network).
• Short-term prediction of risk of propagation of problems through the network and identification of cost-effective solutions. This element focuses on the availability of an online platform to assess the risk of expansion of local emergencies to other regions or countries in the world through the air transport network. The platform will also identify the components of the network (airports, routes, airlines, etc.) that could have a major impact on the risk of expansion of an emergency and the reasons why they could do so. The platform will support the assessment of solutions that could be implemented to prevent the expansion of an emergency at different scales (airport- or aircraft-related measures and measures at regional or country level). A multi-objective performance framework will make it possible to analyse each solution from various perspectives: on the one hand, emergency-related metrics will quantify the effectiveness of the solution and the resilience to changes in the evolution of the emergency around the world, while, on the other hand ATM-related metrics will quantify the impact on capacity, efficiency and the environment. Both perspectives will be combined in cost-related metrics that will determine the cost of implementing each solution, and also the economic implications for the air transport sector. This approach will facilitate joint decision-making among airlines, airports, regions and countries, resulting in decisions that are more efficient and less aggressive for the air transport sector than simply closing airports or airspaces (R&I need: ATM continuity of service despite disruption).
• Formation of contingency plans. This research will help in developing and integrating ANSP contingency plans for emergency operations in the face of recurring and sudden events (weather and other hazards), as well as potential responses from operators (R&I need: ATM continuity of service despite disruption).