Artificial Intelligence Integration in Air Traffic Management: A Qualitative Content Analysis of the SESAR Research
Year: 2025
Type: article
Abstract: The aim of the study is to explore the use and integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies within the SESAR projects. Using a qualitative content analysis approach, this research systematically reviewed 232 SESAR project documents and identified 37 projects that directly applied AI/MLmodels 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.
Author: TUNCAL, Arif
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7092623762
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