A Spatiotemporal Hybrid Model for Airspace Complexity Prediction

doi:10.1109/MITS.2022.3204099

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 平行智能技术与系统团队

	A Spatiotemporal Hybrid Model for Airspace Complexity Prediction
	Du, Wenbo 1; Li, Biyue 1; Chen, Jun 2; Lv, Yisheng3,4 ; Li, Yumeng 1
发表期刊	IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE
ISSN	1939-1390
	2022-09-28
页码	8
通讯作者	Li, Yumeng(liyumeng@buaa.edu.cn)
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.
关键词	Complexity theory Atmospheric modeling Spatiotemporal phenomena Predictive models Deep learning Correlation Air traffic control
DOI	10.1109/MITS.2022.3204099
收录类别	SCI
语种	英语
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]
项目资助者	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
WOS研究方向	Engineering ; Transportation
WOS类目	Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS记录号	WOS:000862362900001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：5[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/50447
专题	多模态人工智能系统全国重点实验室_平行智能技术与系统团队
通讯作者	Li, Yumeng
作者单位	1.Beihang Univ, Sch Elect & Informat Engn, Natl Engn Lab Big Data Applicat Technol Comprehen, Beijing 100191, Peoples R China 2.Queen Mary Univ London, Sch Engn & Mat Sci, London E1 4NS, England 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
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.