As air traffic continues to grow, managing congestion and complexity in our skies becomes an ever-greater challenge. The SESAR JU ASTRA is developing an innovative solution designed to tackle these issues by harnessing the power of artificial intelligence and machine learning.
A recent paper explores ASTRA’s ability to predict and resolve 4D areas of relatively high air traffic control complexity up to 60 minutes before they occur. By using AI to forecast and address airspace congestion in advance, ASTRA aims to provide flow management position operators with the tools they need to manage air traffic more effectively, reduce operational complexity, and decrease controller workload. ASTRA builds on existing tools that predict 4DARHAC events only 20 minutes ahead. However, this innovative system pushes the envelope by offering a one-hour lead time, allowing for proactive management of airspace complexity. Not only does the approach predict these events, but it also provides resolution strategies to help manage the flow of air traffic more smoothly. The research was validated through workshops and interviews with operational experts, ensuring it addresses real-world challenges faced by FMP operators. The feedback collected has been incorporated into the system’s design, ensuring its relevance and usability in the field.
The significance of ASTRA’s AI-powered solution cannot be overstated. In a time when airspace congestion is on the rise, the ability to anticipate and address complexity before it impacts operations is crucial for maintaining safety and efficiency in the skies. The findings offer promising insights into how AI can transform the management of en-route air traffic, supporting the move towards smarter, more efficient airspace management.
More about the project.