Usage of Machine Learning Algorithms in Flexible Use of Airspace Concept

Civil Aviation Authorities (CAA), Air Navigation Service Providers (ANSP) and Military Forces share the common airspace and operate regarding their own needs. With an effective usage of airspace which is the use of military restricted and limited zones for civil flights, national boundaries without constraints will be decreased the cost of fuel of aircrafts, increase the airspace capacity and lead to less time consumption for flights. Implementation of Flexible Use of Airspace (FUA) concept to national Air Traffic Management in three levels that ICAO and EUROCONTROL determined in their standards is considered as a complete system solution that is now available in various countries. The FUA concept is based on Level-1 Strategic, Level-2 Pre-tactical and Level-3 Tactical levels. After civil and military organizations determined their strategic requirements according to the Level-1 Strategic Management of Airspace, Level-2 and Level-3 activities could be achieved based on this high-level requirements using by advanced technologies. In Level-1 Strategic Management of Airspace level, machine-learning algorithms are used for ensuring the reliability and availability of services to get continuous and daily allocation readiness for Level-2 and Level-3 activities. Actual and historical data of the flights and airspace information gathered from civil and military parts of the concept can be used as an input of machine learning training for development of the Level-1 Strategical Management of Airspace proposed application. When the rules from the Level-l implemented on the training data, FUA specified computers start categorizing the FIRs as per capacity, traffic density, weather conditions, fuel consumption, distance and time characteristics of routes and proposed applications start learning how to allocate airspace according to predetermined user requirements. Machine Learning algorithms will be used as an assistant for Flight Planning and it leads to optimize air traffic management. The output of the Level-1 Strategic Management of Airspace proposed application is ready to use for Air Traffic Controllers to achieve Level-2 and Level-3 Management of Airspace activities.