Artificial Intelligence Integration in Air Traffic Management: A Qualitative Content Analysis of the SESAR Research

Abstract

The aim of the study is to explore the integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies within the Single European Sky Air Traffic Management Research (SESAR) projects. Using a qualitative content analysis approach, this research systematically reviewed 232 SESAR project documents and identified 32 projects that directly applied AI/ML models and techniques. These selected projects were further examined to categorize their focus into four key areas: situational awareness and human-AI teaming; trajectory prediction, traffic flow management, and network optimization; automation in communication, navigation, surveillance (CNS), and safety monitoring; and AI integration and ethical governance. The study contributes to the literature by offering a structured framework that highlights the current applications of AI/ML in air traffic management innovation, while also identifying emerging trends and potential future research directions.