AI-Enhanced Techniques for Air Traffic Management

Dear Colleagues,

It is well known that air traffic management (ATM) is fundamental for the air transportation industry; any effort to improve air traffic safety and efficiency, from any aspect, deserves support. Fortunately, thanks to the large amount of available industrial data storage and widespread applications of information technology, it is possible to obtain extra real-time traffic information to make contributions to air traffic operation. Recently, AI-based techniques have attracted widespread attention in research all over the world, as evident from the high level of publications on this Topic in the Nature journals[1,2]. The published works explore advanced research topics that seek to combine the AI and ATC fields. In addition, other techniques have also been considered to improve air traffic safety and efficiency, including air traffic controller training, automatic planning, etc. This Special Issue focuses on applying artificial intelligence approaches to research topics related to air traffic management, including but not limited to the following: 1) Traffic dynamic perception, such as the understanding of spoken instructions; 2) Understanding of traffic situations, such as conflict detection and trajectory processing; 3) Traffic planning, such as TBO; 4) Automatic decisions, such as reinforcement learning; 5) Air traffic safety enhancements: system, techniques, or case studies; 6) Other research topics related to air traffic and machine learning. We enthusiastically seek contributions from those with expertise in air traffic management and computer science to present their papers on this Topic and to share their knowledge and experiences with both academic and industry audiences.

[1] D. Guo, Z. Zhang, B. Yang, J. Zhang, H. Yang, Y. Lin, Integrating spoken instructions into flight trajectory prediction to optimize automation in air traffic control. Nat. Commun. 15, 9662 (2024).
[2] Z. Zhang, D. Guo, S. Zhou, J. Zhang, Y. Lin, Flight trajectory prediction enabled by time-frequency wavelet transform. Nat. Commun. 14, 5258 (2023).

Dr. Yi Lin
Dr. Honggang Chen
Topic Editors