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Artificial Intelligence Solutions to Meteo-Based DCB Imbalances for Network Operations Planning

Periodic Reporting for period 1 - ISOBAR (Artificial Intelligence Solutions to Meteo-Based DCB Imbalances for Network Operations Planning)

Reporting period: 2020-06-01 to 2021-05-31

European airspace capacity is near its limit, with all-causes ATFM (Air Traffic Flow Management) delays doubling from 2014 to 2018. It is critical for network management to address traffic demand and capacity balancing in an efficient way, and ensure that associated measures contribute to minimize delays at network and local levels.

Network prediction and performance is very sensitive to weather and the uncertainty in its prediction. In addition, current ATFCM operations are not evaluated from a systematic perspective. These two factors together lead to a strong dependency on the experience of human operators. ISOBAR addresses these challenges through the contribution to an Artificial Intelligence (AI)-based Network Operations Plan, by including in its scope an enhanced weather prediction tailored to ATFCM, ATM and weather data integration, demand and capacity (DC) imbalance characterization and imbalance mitigation prescription.

To achieve this vision, four objectives are set:
a) Reinforce collaborative ATFCM processes at pre-tactical and tactical levels into the LTM (local) and Network Management (network) roles integrating dynamic weather cells.
b) Characterisation of demand and capacity imbalances at pre-tactical level [-1D, -30min] depending on the input of probabilistic weather cells by using applied AI methods and ATM and weather data integration.
c) User-driven mitigation plan considering AUs priorities (and fluctuations in demand based on weather forecasts) and predicted effectiveness of ATFCM regulations, considering flow constraints and network effects.
d) Develop an operational and technical roadmap for the integration of ancillary services (providing AI-based hotspot detection and adaptative mitigation measures) into the NM platform, by defining interfaces, functional and performance requirements.

Aviation provides the only rapid worldwide transportation network, which makes it essential for global business: economic growth, creation of jobs and international trade and tourism. The project contributes to a more efficient air transport system, which consequently promotes an improved quality of life and helps to improve living standards.
During ISOBAR first year all the Work Packages have been kicked-off:
- WP01 "Collaborative ATFCM Operations" has completed the definition of ISOBAR Operational Framework, including the first round of refinement of the ATFCM Process functional description and the requirements of the services composing the ISOBAR solution.
- WP02 "Meteorological Engine" has performed a detailed analysis of the two high resolution Numerical Weather Prediction (NWPs) models considered in scope. WP2 has also developed an ATFCM-tailored storm predictive model able to produce forecast in line with the requirements in the enhanced ATFCM process defined.
- WP03 "Hotspot Identification" has delivered a demand prediction model producing alternative trajectories between main European city-pairs usable as re-routing options for mitigation of weather-related imbalances. The work has also started to identify the best-way of characterising capacity decay linked to weather-related imbalances.
- WP04 "Hotspot mitigation" has started the development of two parallel approaches for generating airspace users-driven mitigation strategies: one exploring hyper-heuristics and the other exploring reinforcement learning.
- WP05 "ISOBAR Service" tasks have mainly focussed on data management: identification of needs in terms of datasets, data gathering, transformation and/ or integration and distribution as required by the diverse Artificial Intelligence components. Training datasets for each model have been managed at component level, with minor needs of WP5 interaction. The HMI communication prototype has been delivered early in order to maximise the time for its use for dissemination and to have it available for fostering the extraction of requirements from stakeholders and internally in the project.
- WP06 "ATFCM Effectiveness and Evaluation" has defined a performance framework adapted to the measurement of the effectiveness of the type of mitigation measures that are prescribed by the ISOBAR solution. An exhaustive analysis of historical data has been conducted, resulting in a characterisation of performance over traffic associated to the geographical scope of ISOBAR and also to the occurrence of the type of meteo events that are the target of the solution. The task of the evaluation design has been almost completed, describing the planned evaluation activities: one per AI component and two general evaluation exercises (one focussed on operational feedback and other on estimation of performance benefits).
- WP07 "Exploitation, Dissemination and Communication" has delivered the DEC plan including concrete actions identified for attending target events, presentations and publications. In the first period of the project, the first Workshop was organised. The project also presented a poster in the SESAR Innovation Days 2020 and performed a presentation during the Engage Workshop for Thematic Challenge 3 - Efficient provision and use of meteorological information in ATM.
The innovation in ISOBAR concept applied to network management is focused in:
• Improved Collaborative ATFCM (ConOps and AUs involvement): New flight planning services with enriched DCB and MET information will give to civil Airspace Users the flexibility to optimize their operational flight plans and to accommodate their business needs without compromising optimum ATM system outcome and the performances of all stakeholders.
• Non-Nominal Weather Situations and Probabilistic Storm Prediction: In the ISOBAR project, forecasts of probability of convection will be improved for tactical lead times by increasing the update frequency and the spatial resolution.
• AI Demand Prediction: As a progress beyond analytical and deterministic methods, Machine Learning libraries will be developed to predict probabilistic demand variability associated to probabilistic forecasts of weather cells. Moreover, demand prediction will take into account AU’s needs and their possible reactions to weather forecasts.
• Meteo and ATM Data integration: The vision of ISOBAR is to transform forecasts in tailored and consistent meteorological information with the potential to be deployed as a NM service.
• Probabilistic DCB Imbalance: Automation will be introduced to reduce uncertainty in the process and to provide operators with better situational awareness thanks to the development of the technological components of ISOBAR.
• Learning-based DCB Solution: leverage the latest advances in machine learning to present a learning-based DCB solver where the uncertainty profiles of both demand and capacity predictions and the collaborative planning are taken into account. Expected benefits are in terms of AUs’ preferences compliances, more efficient flow patterns and reduction of ATC workload.

The expected outcomes of the project are grouped into three main fields:
• ISOBAR ConOps: Bundle composed by the ISOBAR ATFCM operational framework and the ISOBAR Performance framework.
• ISOBAR AI Core: Bundle composed by the libraries and processes covering the generation of the convective weather forecast, datasets, the imbalance identification, demand fluctuation and imbalance mitigation.
• ISOBAR B2B service: Bundle composed by the software products (requirements, interfaces and visualisation tools).
ISOBAR Concept diagram