A Spatiotemporal Hybrid Model for Airspace Complexity Prediction

doi:10.1109/MITS.2022.3204099

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 先进控制与自动化团队

	A Spatiotemporal Hybrid Model for Airspace Complexity Prediction
	Du, Wenbo 3; Li, Biyue 3; Chen, Jun 4; Lv, Yisheng 1,2; Li, Yumeng 3
刊名	IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE
	2022-09-28
页码	8
关键词	Complexity theory Atmospheric modeling Spatiotemporal phenomena Predictive models Deep learning Correlation Air traffic control
ISSN号	1939-1390
DOI	10.1109/MITS.2022.3204099
通讯作者	Li, Yumeng(liyumeng@buaa.edu.cn)
英文摘要	Airspace complexity is a key indicator that reflects the safety of airspace operations in air traffic management systems. Furthermore, to achieve efficient air traffic control, it is necessary to accurately predict the airspace complexity. In this article, we propose a novel spatiotemporal hybrid deep learning model for airspace complexity prediction to efficiently capture spatial correlations as well as temporal dependencies pertaining to the airspace complexity data. Specifically, we apply convolutional networks to discover the short-term temporal patterns and skip long short-term memory networks to model the longterm temporal patterns of airspace complexity data. Furthermore, it is observed that the graph attention network in our proposed model, which emphasizes capturing the spatial correlations of the airspace sectors, can significantly improve the prediction accuracy. Extensive experiments are conducted on the real data of six airspace sectors in Southwest China. The experimental results show that our spatiotemporal deep learning approach is superior to state-of-the-art methods.
资助项目	National Key Research and Development Program of China[2019YFF0301400] ; National Natural Science Foundation of China[61961146005] ; National Natural Science Foundation of China[62088101] ; National Natural Science Foundation of China[61827901] ; Postdoctoral Science Foundation[2021M700332] ; Shuohuang Railway Project[GJNY-19-90] ; Engineering and Physical Sciences Research Council[EP/N029496/2]
WOS研究方向	Engineering ; Transportation
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000862362900001
资助机构	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Postdoctoral Science Foundation ; Shuohuang Railway Project ; Engineering and Physical Sciences Research Council
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/50446]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者	Li, Yumeng
作者单位	1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.Beihang Univ, Sch Elect & Informat Engn, Natl Engn Lab Big Data Applicat Technol Comprehen, Beijing 100191, Peoples R China 4.Queen Mary Univ London, Sch Engn & Mat Sci, London E1 4NS, England
推荐引用方式 GB/T 7714	Du, Wenbo,Li, Biyue,Chen, Jun,et al. A Spatiotemporal Hybrid Model for Airspace Complexity Prediction[J]. IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE,2022:8.
APA	Du, Wenbo,Li, Biyue,Chen, Jun,Lv, Yisheng,&Li, Yumeng.(2022).A Spatiotemporal Hybrid Model for Airspace Complexity Prediction.IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE,8.
MLA	Du, Wenbo,et al."A Spatiotemporal Hybrid Model for Airspace Complexity Prediction".IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE (2022):8.