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

doi:10.23919/JSEE.2022.000109

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 LIU^2,*(), Qingtao PAN¹()

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 E-mail:tangjun06@nudt.edu.cn;liugang@hnist.edu.cn;panqingtao@nudt.edu.cn
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: tangjun06@nudt.edu.cn||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: liugang@hnist.edu.cn||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: panqingtao@nudt.edu.cn
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).

Abstract

Abstract:

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)

Jun TANG, Gang LIU, Qingtao PAN. Review on artificial intelligence techniques for improving representative air traffic management capability[J]. Journal of Systems Engineering and Electronics, 2022, 33(5): 1123-1134.

Figures/Tables 6

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Time	Control technology	Flight characteristic	Navigation characteristic
1929?1934	Visual flight rules	Fewer planes, shorter voyages and slower speeds	Flag and gun
1934?1945	Procedure control system	More aircraft, faster speed, mainly military flights	Air traffic control center, tower, terminal
1945?1980s	Radar control	Fast speed, long voyages, more flights	Primary radar, secondary surveillance radar
1980s?	Air-ground cooperative ATC	Airway/airport congestion, developed airborne equipment	Satellite technology

Method	Expert system	Knowledge engineering	Agent-model	Machine learning/ deep learning	Mathematical	Others (distributed, IT, etc.)	Year
Gosling [30]	√	√	?	?	?	√	1990
Li et al. [31]	√	√	?	?	?	?	1997
Krishnan et al. [32]	?	?	?	√	?	√	2012
Findler et al. [33]	?	√	?	?	?	√	1991
Mever et al. [34]	?	√	?	?	?	√	2013
Kuchar et al. [35]	?	?	?	?	√	?	2000
Radanovic et al. [36]	?	?	?	?	√	?	2018
Jilkov et al. [37]	?	?	?	?	√	?	2018
Isaacson et al. [38]	√	√	?	?	?	√	2001
Tran et al. [39]	?	?	√	?	?	√	2019
Kulkani et al. [40]	?	?	?	√	?	√	2015
Klüver et al. [41]	?	?	?	√	?	√	2017

Method	[Multi-agent, machine learning]	[Centralized, decentralized]	Collaboration with airspace users	Multi-objective optimization	[Time uncertainty, small training set]	Intelligent optimization algorithm	Multilevel grid spatiotemporal index	[Multi-agent, machine learning]	Year
Jarvis et al. [45]	[√, ?]	[√, ?]	√	?	[?, ?]	√	?	[√, ?]	2010
Schefers et al. [46]	[?, ?]	[√, ?]	?	√	[√, ?]	?	?	[?, ?]	2018
Wu et al. [47]	[?, ?]	[√, ?]	?	√	[?, ?]	√	?	[?, ?]	2018
Cao et al. [48]	[?,√]	[√, ?]	?	?	[?,√]	?	?	[?,√]	2018
Miao et al. [49]	[?, ?]	[√, ?]	?	?	[?, ?]	?	√	[?, ?]	2019
Agogino et al. [50]	[√,√]	[?,√]	?	?	[?,?]	?	?	[√,√]	2012
McCrea et al. [51]	[?, ?]	[√, ?]	?	?	[?, ?]	?	?	[?, ?]	2008
Cruciol et al. [52]	[?,√]	[√, ?]	?	?	[?, ?]	?	?	[?,√]	2015
Yu et al. [53]	[?,√]	[?, ?]	?	?	[?, ?]	?	?	[?,√]	2019
Wang et al. [54]	[?,√]	[√, ?]	?	?	[?, ?]	?	?	[?,√]	2017
Schirmer et al. [55]	[?, ?]	[√, ?]	?	?	[?, ?]	√	?	[?, ?]	2018
Gerdes et al. [56]	[?, ?]	[√, ?]	?	?	[?, ?]	√	?	[?, ?]	2018
Insaurralde et al. [57]	[?, ?]	[?, ?]	?	?	[?, ?]	√	?	[?, ?]	2017
Kravaris et al. [58]	[?, √]	[√, ?]	?	?	[?, ?]	?	?	[?, √]	2017
Cai et al. [59]	[?, ?]	[√, ?]	?	?	[?, ?]	√	?	[?, ?]	2012

Method	Reinforcement learning	Automata theory	Intelligent agents	Swarm theory	[Environment, Human]	Capacity	Delay	Cost	Year
Pechoucek et al. [69]	√	?	?	?	[√, √]	?	?	?	2006
Tumer et al. [70]	√	?	?	?	[?, ?]	√	?	?	2007
Wolfe et al. [71]	√	?	?	?	[?, √]	?	?	?	2009
Li et al. [72]	√	?	?	?	[√, ?]	?	√	?	2010
Crespo et al. [73]	√	?	?	?	[?, ?]	√	√	?	2017
Cruciol et al. [74]	√	?	?	?	[?, √]	√	?	?	2013
Bayen et al. [75]	?	√	?	?	[√, ?]	√	√	?	2003
Wolfe et al. [76]	?	?	√	?	[?, ?]	?	√	√	2007
Torres et al. [77]	?	?	?	√	[√, ?]	?	√	√	2012

Method	[Machine learning, neural network]	Agent	Data fusion	Others	Airplane	UAV	Year
Apiecionek et al. [84]	[?, ?]	?	√	√	√	?	2015
Sanchez-Lopez et al. [85]	[?, ?]	√	?	√	?	√	2016
Bouwmeester et al. [86]	[?, ?]	?	√	?	√	√	2015
Sinopoli et al. [87]	[?, ?]	?	√	?	?	√	2001
Khansari-Zadeh et al. [88]	[√, √]	?	√	?	√	?	2011
Wu et al. [89]	[?, ?]	?	√	√	?	√	2005
Zhilenkov et al. [90]	[?, √]	?	√	?	?	√	2018
Popova et al. [91]	[?, ?]	?	√	√	?	√	2016
Kochenderfer et al. [92]	[√, ?]	?	?	?	√	?	2012
Durand et al. [93]	[?, √]	?	?	?	√	?	2000
Sislak et al. [94]	[?, ?]	√	?	?	√	?	2011
Schetinin et al. [95]	[√, ?]	?	?	√	√	?	2018

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

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