Enhancing Air Traffic Complexity Assessment through Deep Metric Learning: A CNN-Based Approach

Introduction As global air traffic continues to rise, managing airspace complexity efficiently is more crucial than ever. Traditional air traffic management (ATM) systems rely on predefined metrics and human expertise to assess complexity, but these methods often fall short in handling dynamic and intricate scenarios. With the advent of artificial intelligence, deep learning techniques—especially Convolutional Neural Networks (CNNs) and Deep Metric Learning (DML) —offer promising solutions to enhance air traffic complexity assessment. The Challenges of Air Traffic Complexity Air traffic complexity is influenced by multiple factors, including: Traffic density – The number of aircraft within a given airspace. Separation requirements – The distance and altitude constraints between aircraft. Weather conditions – Turbulence, storms, and visibility affecting flight paths. Airspace structure – The complexity of flight corridors, restricted zones, and control points. Traditional ATM model...