Fast Track Innovation and Uptake Capacity-on-Demand and Dynamic Airspace

The introduction of dynamicity in the management of airspace and the capacity-on-demand service will unlock performance benefits in the following areas.

• Environment. More precise trajectories for arrival and departure will make it possible to reduce noise impact.
• Capacity. Capacity is measured in terms of either ATFM delays (lack of capacity) or throughput. En-route capacity aims to maintain ATFM delays at 0.5 minutes per flight or less. TMA and peak runway throughput will be increased according to traffic forecasts. A more stable and predictable level of capacity will be achieved in all-weather operations. In addition, by providing capacity dynamically where and when it is needed and reconfiguring the airspace to match traffic flows, overall system resilience will be significantly increased.
• Cost-efficiency. Dynamic airspace configurations, capacity on demand and ATCO training programmes will provide scalability. ATCO productivity is expected to increase significantly.
• Operational efficiency. Trajectory management and dynamic airspace configurations will provide further improvements in vertical flight efficiency and cruising/taxiing fuel consumption when flights are subject to queueing. Overall improvements in capacity and trajectory management and use of dynamic airspace configurations are expected to reduce the average delay in block-to-block times by 4 minutes per flight.
• Safety. Safety levels will be maintained.

To achieve the expected outcomes, all or some of the following should be addressed.

• On-demand ATS. This will require the development of digital platforms and services leveraging state-of-the-art technologies to enable capacity on demand (R&I need: on-demand ATSs). It will involve, for example:
  • a cloud-based, cybersecure digital platform for on-demand capacity management enabling all airspace users and all types of vehicles (traditional, hydrogen, electric, military) to access airspace in the context of dynamic airspace management, remote provision of air ATS and ATM–U-space convergence;
  • offering an increased level of capacity while accepting a much higher level of complexity so that optimised flight-efficient trajectories do not result in structural limitations on capacity;
  • offering increased levels of capacity and flexibility to allow capacity variations in time and space to meet levels of demand;
  • offering airspace users at all times the most environmentally friendly options when there is a need to constrain traffic, particularly when queueing aircraft at the arrival or departure runways, so that holding no longer exists as part of normal operations, and, if and when there is a need for conflict resolution, offering real-time options to airspace users so that they can select the least penalising trajectory;
  • enhanced network traffic prediction and shared complexity representation using data science techniques such as AI and ML to improve the quality of traffic forecasts and increase network stakeholders’ confidence in planning information;
  • use of 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.
• ATM continuity of service despite disruption. This element will require the development of digital platforms and services leveraging state-of-the-art technologies to enable ATM continuity of service despite disruption (e.g. during capacity bottlenecks, adverse weather, national system breakdowns or disruptive social actions) (R&I need: ATM continuity of service despite disruption). This will involve, for example:
  • smart digital solutions for predicting adverse situations (in relation to weather, capacity, etc.) and distributed decision-making between involved stakeholders, minimising the risk and impact of disruption, and including facilitation of cross-border dynamic and remote ATS
  • 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) to support airline decision-making processes in disruption scenarios; the integration of airline operations into the network; collaboration between FOCs, the network management function and ATC; and better consideration of airspace user preferences in DCB and sequencing processes; etc.;
  • enriched DCB information and enhanced what-ifs available to improve AUs’ decision-making processes when planning or replanning trajectories, encompassing information on DCB constraints/measures such as ATFCM regulations / CTOT / STAM and additional DCB information, for example on hotspots and congestion level indicators;
  • use of new data sources (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);
  • use of big data and ML to identify best practices regarding regulation strategies for particular traffic-load patterns based on historical data and to develop optimised strategies for the most frequent traffic-load situations in the European ATFCM network;
  • 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.
  • use of new data sources (big data), ML algorithms, AI-based decision support tools, etc., to support airline decision-making in disruption scenarios in order to improve the resilience of the system.
• Future data services and applications for airport and network. This refers to the development of digital platforms and services leveraging state-of-the-art technologies to enable future data services and applications for airports and the network (R&I need: future data services and applications for airport and network). It will involve, for example:
  • an advanced user-driven prioritisation process, which provides airspace users with more control over the selection of flights that are delayed in order to prioritise them based on business needs, and which can gradually be extended to new ATFCM rules and queueing techniques;
  • the integration of connectivity into the loop of ATM operations and the new datasets available through A-CDM, UDPP, AOP/NOP data, target time over/arrival and extended AMAN demand in order to further develop the rules for ATFCM and queuing priorities;
  • enriched DCB information and enhanced what-ifs available to improve AUs’ decision-making processes when planning or replanning trajectories, encompassing information on DCB constraints/measures such as ATFCM regulations / CTOT / STAM and additional DCB information, for example on hotspots and congestion level indicators.