The Evolution of AI in Air Traffic Management

From Machine Learning to Agentic Autonomy
Erscheinungsjahr 2026
ISBN: 978-3-032-34798-5
Verlag: Springer

Buch, Englisch, 287 Seiten, Format (B × H): 155 mm x 235 mm

Reihe: Studies in Systems, Decision and Control

This book provides a comprehensive, structured journey through how AI is reshaping ATM, beginning with the historical evolution of the system and the urgency for innovation, followed by a detailed grounding in the data sources that enable AI applications and the core machine learning methods used to extract insights from them. Readers will find accessible explanations of key techniques—including supervised learning, reinforcement learning for real-time decision-making, and deep learning and large language models—paired with concrete aviation use cases such as delay prediction, conflict detection and resolution, rerouting, and procedural automation. The book places strong emphasis on human-centered design, with dedicated chapters exploring how air traffic controllers interact with AI, how trust and cognition are affected, and how effective human-AI hybrid teams can be designed for safety-critical environments. It then looks ahead to emerging paradigms, including agentic AI and increasingly autonomous ATM systems, while maintaining a clear focus on the human role as supervisor and ethical anchor. Concluding with an integrated discussion of operational, technical, legal, and ethical challenges, the book equips readers with both practical insights into current capabilities and a forward-looking roadmap for achieving safe, efficient, and trustworthy AI-enabled air traffic management.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Pham, Duc-Thinh

Guleria, Yash

Lim, Zhi Jun

Fachgebiete

• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Astronautik
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Regelungstechnik
• Technische Wissenschaften Verkehrstechnik | Transportgewerbe Luft- und Raumfahrttechnik, Luftverkehr

Inhaltsverzeichnis

1. Introduction: The Evolution and Urgency of AI in Air Traffic Management.- 2. Data in Air Traffic Management.- 3. Machine Learning and Its Applications in Aviation.- 4. Reinforcement Learning for Decision-Making and Optimization.- 5. Artificial Intelligence in Air Traffic Management.- 6. Human Factors in AI-Augmented ATM.- 7. Human-AI Hybrids in Air Traffic Management.- 8. Agentic AI in Air Traffic Management: Opportunities, Transformations, and Challenges.- 9. Conclusion: Challenges and Opportunities.- Glossary.

Über Autor(innen)

Dr. Duc-Thinh Pham is an Assistant Professor at the Center for AI Research (CAIR), VinUniversity, Vietnam, and a Principal Research Scientist and Chief Scientist at the Air Traffic Management Research Institute (ATMRI), Nanyang Technological University (NTU), Singapore. His work focuses on developing trustworthy and intelligent AI systems for future aviation operations, with particular emphasis on air traffic management, transportation, and human-AI collaboration.
He leads research projects that translate advances in machine learning, deep reinforcement learning, and multi-agent systems into operational applications, including digital controller assistants, AI-assisted air traffic flow management, data-driven airport and airspace optimization, and human-AI trust. He received his Ph.D. degree in Computer Science from Université Paris Sciences et Lettres (PSL), France, in 2019. His broader research interests include human-AI teaming, multimodal data analysis, trajectory prediction, and intelligent decision-support systems for aviation.

Dr. Yash Guleria is an Assistant Professor at the Indian Institute of Technology Mandi, India. His research lies at the intersection of artificial intelligence, decision-making, intelligent transportation systems, and management, with a particular emphasis on the development of AI-enabled solutions for complex, safety-critical environments. Before joining IIT Mandi, he was a postdoctoral research fellow at the Air Traffic Management Research Institute (ATMRI), Nanyang Technological University (NTU), Singapore, where he led research on generative AI applications for air traffic conflict resolution, and digital assistants for air traffic controllers. He completed his Ph.D. at ATMRI, NTU, in 2024 under the supervision of Professor Sameer Alam. His doctoral research focused on improving air traffic conflict resolution through machine learning, conformal automation, and flow-centric paradigms. Earlier, Dr. Guleria worked as a research associate at ATMRI, developing air traffic delay prediction models. He has also served as a visiting researcher at École Nationale de l’Aviation Civile (ENAC), France. 

Dr. Zhi Jun Lim is a Lecturer in the School of Mechanical and Aerospace Engineering at Nanyang Technological University (NTU), Singapore. She received her B.Eng. in Aerospace Engineering and B.A. in Economics, and later earned her Ph.D. from NTU. She is the Co-Principal Investigator of the Flexible Extended Arrival Management (FlexMAN) project at the Air Traffic Management Research Institute (ATMRI), where she leads the development of AI-enabled air traffic management solutions for Southeast Asia. Her research interests include artificial intelligence for air traffic management, trajectory optimisation, and human-centred decision support. At NTU, she teaches aviation-related courses including Air Traffic Management, Airport Operations, and Aircraft Navigation.