Techniques to create synthetic flight plans: The generation of synthetic data is an open area of research and is relevant to ATM because of the scarcity of ATM data. In ASTRA, synthetic data has two objectives: it overcomes the absence of detailed environmental data in the historical dataset; and it enriches the dataset with complex situations by controlling the intensity of the generated traffic.
Metrics to measure complexity: Measuring complexity is nontrivial and depends on many factors. Historically, an FMP assesses sector complexity based on an occupancy metric. In contrast, several metrics are defined in ASTRA; some are defined at sector level, while others characterise a cluster of aircraft. To have a unique complexity unit among the complexity metrics, each metric is scaled such that the complexity is expressed as a percentage. The metrics are then combined into a global complexity using a statistical model.
A complexity-based clustering algorithm to identify and track 4DARHACs: At each time step, the last known position of each aircraft, and its flight plan, are used to predict its future trajectory. Then, for each time step in the predicted future (up to one hour ahead), a clustering algorithm groups flights together and identifies 4DARHACs. A tracking algorithm then identifies the clusters found in the current time step that correspond to those in the previous time step.
A deep RL algorithm to resolve 4DARHACs: An RL agent is implemented to take 4DARHACs as input and to output actions representing solutions for each 4DARHAC. Proposed actions can be lateral, vertical or longitudinal clearances. These are ranked by probability, thus aiding the interpretability of the RL agent. Apart from the dissipation action itself, the agent also determines which aircraft should take the proposed action.
An HMI design for user interaction: This HMI allows FMPs to interact with the ASTRA solution. Amongst other things, it allows the FMP to get alerts; view and simulate the proposed dissipation actions; and select actions for implementation. The HMI is designed in a way that gives the user an insight into why the RL agent proposes certain clearances, and the impact (on complexity) of implementing a certain clearance.
These results will lead to improved predictability at local ATFCM level and improved confidence in sector DCB management.
More work will be needed to develop the solution beyond TRL2. Also, better regulatory standards are required to develop AI aspects of the solution. Future research includes:
- The application of ASTRA network-wide as a service that is offered by NM to local ATFCM. In this scenario, NM would identify 4DARHACs and send information about these events, together with proposed solutions, to local ATFCM;
- The reduction of the coordination required to carry out dissipation actions e.g. by automating coordination tasks between various stakeholders;
- The integration of the ASTRA HMI with existing FMP tools.
