The chair cooperates with national and international research institutions to develop solutions for the challenges in the aviation system. Current topics include efficient arrival management, automated airport aprons, and data-driven analysis of the air traffic system. As a result of these substantial collaborations, scientific publications have been and continue to be published in high-impact journals. These are the latest publications:


Towards a greener Extended-Arrival Manager in air traffic control: A heuristic approach for dynamic speed control using machine-learned delay prediction model
Lim Zhi Jun, Sameer Alam, Imen Dhief, Michael Schultz (2022). Journal of Air Transport Management 103, 102250

Data-driven airport management enabled by operational milestones derived from ADS-B messages
M Schultz, J Rosenow, X Olive (2022). Journal of Air Transport Management 99, 102164

Modeling Aircraft Departure at a Runway Using a Time-Varying Fluid Queue
E Itoh, M Mitici, M Schultz (2022). Aerospace 9 (3), 119

Implementation of a Long-Range Air Traffic Flow Management for the Asia-Pacific Region
M Schultz, D Lubig, E Asadi, J Rosenow, E Itoh, S Athota, V Duong (2021). IEEE Access 9, 124640-124659

Predictive classification and understanding of weather impact on airport performance through machine learning
M Schultz, S Reitmann, S Alam (2021). Transportation Research Part C: Emerging Technologies 131, 103119



Prof. Eri Itoh
The University of Tokyo

Prof. Eri Itoh

Eri Itoh received a Ph.D. degree in aeronautics and astronautics from The University of Tokyo, Japan, in 2007. After gaining experience at international research organizations, she currently holds the positions of an Associate Professor with the Aeronautics and Astronautics Department, The University of Tokyo, and a Chief Researcher with the Air Traffic Management Department, Electronic Navigation Research Institute, National Institute of Maritime, Port and Aviation Technology. Her research interests include the design of automation systems that work with human operators in air traffic management, including airspace and airport operations. Combining data-driven analysis, mathematical models, and simulation studies, she works toward realizing even more efficient and resilient air traffic operations.

Prof. Sameer Alam
Nanyang Technological University - NTU Singapore

Prof. Sameer Alam

Sameer Alam is passionate about how Machines can learn and how Machines can perform higher order Cognitive tasks in safety critical domains such as Air Traffic Management. Sameer is the Deputy Director of Air Traffic Management Research Institute (ATMRI) and Co-Director of SAAB-NTU Joint Research Lab, where he leads 30 research scientists and 6 PhD students in the area of AI/Machine Learning for Air Traffic and Airport Operations. Prof. Alam has over 20 years’ experience in the design and development of Artificial Intelligence/Machine Learning models for Air Traffic Management and Airport operations. He has worked with various organizations including ICAO Middle-East, NASA Ames USA, AirServices Australia, CAAS Singapore, Eurocontrol, Civil Aviation Affairs Bahrain, and ENAC France. He also serves as a technical advisor to the Middle East Regional Monitoring Agency of the ICAO, technical advisor to the Ministry of Transport Bahrain, Editorial Board Member of Elsevier Transportation Research Part-C, and Program Committee member for the US & Europe ATM R&D Seminar. His research interest is in AI-Human Hybrid systems, Generative Adversarial Networks, Multi-Agent Reinforcement Learning, Deep Learning, Evolutionary Computation, and Complex Networks. He has invented/co-invented, with his students, several AI/Machine Learning algorithms for open research problems in Air Transportation.

Dr. Xavier Olive
Office National d'Etudes et de Recherches Aérospatiales (ONERA)

Dr. Xavier Olive

Xavier Olive graduated from Supaero, Université de Toulouse, France, and holds a PhD from Kyoto University, Japan. He is currently a research scientist at ONERA, passionate about aviation, maps, and data. His research interests include Data Science, Machine Learning and Decision Science applied to aviation, with a particular focus on optimization, anomaly, and pattern detection applied to air traffic management, operations, predictive maintenance, safety analyses, and risk assessment. Xavier also teaches artificial intelligence and advanced programming in Python to graduate students. He is the main author of the open-source traffic Python library, and of the book Programmation Python avancée with Dunod editions.