Evolution of AI Techniques in Air Traffic Flow Management in Europe Since the 1990s

Abstract

Air traffic management (ATM) systems have constantly evolved to accommodate the ever-growing air traffic to prevent overwhelming the finite airspace capacities and to ensure safe and efficient flight operations. To further advance these systems, incorporating artificial intelligence (AI) technologies is the most effective way for their continued development. These advancements include the adoption of more sophisticated technologies and models. Over the past 30 years AI technologies have advanced increasingly, with early interest to integrate AI methods into ATM systems. The techniques and models of ever developing AI promises to provide more accurate and optimized traffic management solutions.

The purpose of this thesis is to explore how artificial intelligence techniques applied to air traffic flow management have evolved in Europe since the 1990s, and where they stand at this day. It briefly examines the history and background of AI, and how it was incorporated into the heuristic and largely rule-based systems of Air Traffic Flow Management. The findings underscore the critical role AI now plays in enhancing operational efficiency and safe-ty, with reducing flight delays, fuel consumption and operational costs. The research was done mainly as a literature review, and sources used were mainly recent peer reviewed scientific articles and literature on the subject.