The main objective of delivering the right amount of capacity, at the right moment and with the maximum efficiency to better serve the air traffic demand, comprises the following sub-objectives:
1
Take full advantage of the airspace potential using sectors’ shapes that are suited for optimal workload distribution across a configuration, increasing the resiliency of sectors and sector configuration plans.
2
Improve the demand capacity balancing using machine learning to enhance traffic prediction at the 8–24-hour range, and to determine/compute the sectors’ configuration providing the required capacity.
3
Provide fine-tuned solutions to real-time operation exploring different traffic scenarios, based on traffic predictions constructed from flows.
4
Being able to automatically determine the optimum level of airspace usage, measured in terms of capacity, using artificial intelligence techniques.
Complementary to these goals,
5
Explore the use of machine learning and more in general artificial intelligence for demand capacity balancing, paving the way for its widespread usage
