Overview of Research of Machine Learning in Air Traffic Management

Abstract

As the demand for air traffic has grown at a fast pace in recent years, the efficiency and safety of air traffic management is facing greater challenges with limited airspace resources. As an important part of the civil aviation air traffic system, the existing air traffic management capability is no longer able to meet the demand of air traffic growth. Machine learning, as an advanced method of current computer modelling, has shown good application value and promise for application in air traffic management. This paper starts by introducing the application methods and modelling process for machine learning in air traffic management, followed by the current research status of machine learning in three areas, namely air traffic flow management, air traffic services and airspace management, respectively, and finally points out the challenges and further development outlook of applying machine learning to air traffic management. Overall, the introduction of machine learning into air traffic management represents a major trend with significant implications for its development.