Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (5): 1123-1134.doi: 10.23919/JSEE.2022.000109

• SYSTEMS ENGINEERING • Previous Articles     Next Articles

Review on artificial intelligence techniques for improving representative air traffic management capability

Jun TANG1(), Gang LIU2,*(), Qingtao PAN1()   

  1. 1 College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
    2 School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang 414000, China
  • Received:2021-04-01 Online:2022-10-27 Published:2022-10-27
  • Contact: Gang LIU;;
  • About author:|TANG Jun was born in 1988. He received his Ph.D. degree from the Engineering School of the Autonomous University of Barcelona in 2015. He was dedicated to the Ph.D. studies in the technical innovation cluster on aeronautical management at the Universitat Autonoma de Barcelona, Sabadell, Spain. He is currently an associate professor with the College of Systems Engineering, National University of Defense Technology, China. His research interests include CPNs, state space, and air traffic management. E-mail:||LIU Gang was born in 1983. He received his B.S. and M.S. degrees from Naval Arms Command Institute, China in 2005 and 2008, respectively, and Ph.D. degree from National University of Defense Technology, China, in 2013. His research interests include intelligent information processing, optimization and path planning. E-mail:||PAN Qingtao was born in 1996. He received his B.S. degree from Ocean University of China in 2020. He is currently a researcher in the Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, majoring in control science and engineering. His research interests include intelligent optimization algorithm, system simulation and cluster control. E-mail:
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (62073330), the Natural Science Foundation of Hunan Province (2020JJ4339) and the Scientific Research Fund of Hunan Province Education Department (20B272).


The use of artificial intelligence (AI) has increased since the middle of the 20th century, as evidenced by its applications to a wide range of engineering and science problems. Air traffic management (ATM) is becoming increasingly automated and autonomous, making it lucrative for AI applications. This paper presents a systematic review of studies that employ AI techniques for improving ATM capability. A brief account of the history, structure, and advantages of these methods is provided, followed by the description of their applications to several representative ATM tasks, such as air traffic services (ATS), airspace management (AM), air traffic flow management (ATFM), and flight operations (FO). The major contribution of the current review is the professional survey of the AI application to ATM alongside with the description of their specific advantages: (i) these methods provide alternative approaches to conventional physical modeling techniques, (ii) these methods do not require knowing relevant internal system parameters, (iii) these methods are computationally more efficient, and (iv) these methods offer compact solutions to multivariable problems. In addition, this review offers a fresh outlook on future research. One is providing a clear rationale for the model type and structure selection for a given ATM mission. Another is to understand what makes a specific architecture or algorithm effective for a given ATM mission. These are among the most important issues that will continue to attract the attention of the AI research community and ATM work teams in the future.

Key words: artificial intelligence (AI), air traffic management (ATM), air traffic services (ATS), airspace management (AM), air traffic flow management (ATFM), flight operations (FO)