• Project ID PJ.09-W2 DNMS
  • Project duration 2019-12-01 > 2022-12-31
  • Cost
    • Total EUR 32 361 297
    • EU Contr. EUR 4 433 102,79
  • Status Ongoing

Objectives

Project PJ.09, Digital Network Management Services, aims improve network traffic predictability and shared complexity representation for all demand capacity balancing (DCB), dynamic airspace configuration, integrated network management, air traffic control planning (INAP) and collaborative network performance management actors.

The project focuses on improving and sharing the local and regional air traffic intelligence to:

  • optimise the use of airspace capacity,
  • improve air traffic controller (ATCO) productivity,
  • reduce delays,
  • improve predictability and 
  • improve service quality for airspace users and passengers.


The partners are developing three solutions to achieve the goal of creating a powerful distributed network management function and enabling a more flexible response to traffic demand and regional/local performance objectives.

Dynamic airspace configurations (DAC)

This solution aims to improve the use of airspace capacity for both civil and military users by increasing the granularity and the flexibility in the airspace configuration within ANSPs’ areas of responsibility. It will address the integration of concepts and procedures to allow flexible sectorisation to be dynamically modified based on demand. This includes potential impact assessment for ATCO licenses, international boundaries and IOP and A/G multi-datalink communication capabilities.

In Wave 1, the project teams successfully validated the viability of the required technologies and operational processes for Dynamic Airspace Configuration (one of the key solutions aiming to bring the most significant airspace capacity improvements) and INAP. However, the validations were conducted separately and only for some of the basic elements proposed in the operational concept. Wave 2 will integrate both concepts and develop a DAC tool fully integrated with INAP, which takes into account workload and complexity values.

Enhanced network traffic prediction and shared complexity

This solution aims to improve the quality of the pre-tactical traffic forecast to allow extending the planning in pre-flight phase for all network stakeholders. It will address this challenge by further integrating the local tools from airspace users, airports and ANSPs (FMP/INAP) with the EUROCONTROL Network Manager in a rolling and dynamic process. It will also provide traffic demand prediction enhanced with AI/ML modelling through use of advanced data science techniques to improve the predicted flight data (PFD) in the pre-tactical and early D-day before flight plans or extended flight plans (EFPL) are filed.

In the tactical phase, the prediction of imbalances or DCB constraints is influenced by traffic complexity in addition to traffic demand and capacity that are the basis in pre-tactical phase. This solution aims to consolidate the outcome of local traffic complexity assessments via agreed common shareable complexity indicator to improve the network or the regional complexity assessment. It will also further improve and validate the use of probabilistic occupancy count algorithm developed in Wave 1.

Collaborative network performance management

This solution refers to prediction algorithm which anticipates the performance degradation in identified areas within the network. It will also validate the transition from local to regional measures in critical state. The project partners will validate of shareable performance indicators and monitoring functions, which enable decision makers to drill down shortfalls and opportunities in their operations and encourage network collaboration to benefit of the overall network performance.

By developing the Network Performance Management Dashboard, this solution enables the EUROCONTROL Network Manager to build a network performance view allowing focusing on areas of particular interest subject to performance degradations.

This project has received funding from the SESAR Joint Undertaking under the European Union's Horizon 2020 research and innovation programme under grant agreement No 874463

European Union