Airport Capacity Prediction With Multisource Features: A Temporal Deep Learning Approach

doi:10.1109/TITS.2022.3213029

	Airport Capacity Prediction With Multisource Features: A Temporal Deep Learning Approach
	Du, Wenbo 1,2; Chen, Shenwen 1,2; Li, Haitao 1; Li, Zhishuai3,4 ; Cao, Xianbin 1,2; Lv, Yisheng3
发表期刊	IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
ISSN	1524-9050
	2023
卷号	24 期号:1 页码:615-630
通讯作者	Cao, Xianbin(xbcao@buaa.edu.cn) ; Lv, Yisheng(yisheng.lv@ia.ac.cn)
摘要	Accurate airport capacity estimation is crucial for the secure and orderly operation of the aviation system. However, such estimation is a non-trivial task as capacity depends on various meteorological and operational features. The complex coupling characteristics among these multi-source features have proved to be challenging for most of the traditional regression models. Recently, enhanced by its excellent ability to mine nonlinear relationships, the machine learning methods trigger widely applications. However, due to the imbalance of features scatter and the neglect of temporal dependences in aviation systems, existing machine learning methods for airport capacity prediction still have room for improvement. In light of these, this paper presents a novel airport capacity prediction method based on the multi-channel fusion Transformer model (MF-Transformer). Besides the commonly used aviation features, we unprecedentedly harness the power of the high-dimensional meteorological feature for accurate prediction. As to the model, we construct a multi-channel feature fusion structure, which includes a three-channel network for multi-source features extraction and an attention-based feature fusion module between channels. In each channel, the Transformer-based model is utilized to capture the temporal dependences of features. We conduct experiments on the capacity prediction tasks of the Beijing Capital International Airport which is the largest airport in China and verify that the proposed MF-Transformer outperforms benchmarks under different prediction horizons.
关键词	Airport capacity predictive model multi-channel fusion structure machine learning deep learning
DOI	10.1109/TITS.2022.3213029
关键词[WOS]	TRAFFIC FLOW MANAGEMENT ; NEURAL-NETWORKS ; SYSTEMS
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] ; Shuohuang Railway Project[GJNY-19-90]
项目资助者	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Shuohuang Railway Project
WOS研究方向	Engineering ; Transportation
WOS类目	Engineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS记录号	WOS:000928006100045
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：2[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/173211/53225
专题	多模态人工智能系统全国重点实验室
通讯作者	Cao, Xianbin; Lv, Yisheng
作者单位	1.Beihang Univ, Sch Elect & Informat Engn, Beijing 100191, Peoples R China 2.Beihang Univ, Key Lab Adv Technol Near Space Informat Syst, Beijing 100191, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Du, Wenbo,Chen, Shenwen,Li, Haitao,et al. Airport Capacity Prediction With Multisource Features: A Temporal Deep Learning Approach[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2023,24(1):615-630.
APA	Du, Wenbo,Chen, Shenwen,Li, Haitao,Li, Zhishuai,Cao, Xianbin,&Lv, Yisheng.(2023).Airport Capacity Prediction With Multisource Features: A Temporal Deep Learning Approach.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,24(1),615-630.
MLA	Du, Wenbo,et al."Airport Capacity Prediction With Multisource Features: A Temporal Deep Learning Approach".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 24.1(2023):615-630.