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

文献类型：期刊论文

作者Du, Wenbo1,2; Chen, Shenwen1,2; Li, Haitao1; Li, Zhishuai3,4; Cao, Xianbin1,2; Lv, Yisheng4
刊名IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
出版日期2023
卷号24期号:1页码:615-630
关键词Airport capacity predictive model multi-channel fusion structure machine learning deep learning
ISSN号1524-9050
DOI10.1109/TITS.2022.3213029
通讯作者Cao, Xianbin(xbcao@buaa.edu.cn) ; Lv, Yisheng(yisheng.lv@ia.ac.cn)
英文摘要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.
WOS关键词TRAFFIC FLOW MANAGEMENT ; NEURAL-NETWORKS ; SYSTEMS
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]
WOS研究方向Engineering ; Transportation
语种英语
WOS记录号WOS:000928006100045
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China ; Shuohuang Railway Project
源URL[http://ir.ia.ac.cn/handle/173211/53224]  
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.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,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.

入库方式： OAI收割

来源：自动化研究所

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