MAST-GNN: A multimodal adaptive spatio-temporal graph neural network for airspace complexity prediction
文献类型：期刊论文
| 作者 | Li, Biyue1; Li, Zhishuai2 ; Chen, Jun3; Yan, Yongjie4; Lv, Yisheng2; Du, Wenbo5
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| 刊名 | TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
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| 出版日期 | 2024-03-01 |
| 卷号 | 160页码:20 |
| 关键词 | Airspace Complexity Prediction Air Traffic Management Spatio-temporal Graph Neural Network Graph Convolutional Neural Network Attention Mechanism |
| ISSN号 | 0968-090X |
| DOI | 10.1016/j.trc.2024.104521 |
| 通讯作者 | Du, Wenbo(wenbodu@buaa.edu.cn) |
| 英文摘要 | Airspace complexity is defined as an essential indicator to comprehensively measure the safety of air traffic operational situations. A reliable prediction of airspace complexity can provide practical guidance for formulating air traffic management strategies and resource allocation. Although extensive efforts have been devoted to computing airspace complexity, previous studies can rarely model the multi-dimensional and combined spatio-temporal features within airspace complexity data. In this paper, we propose a multimodal adaptive spatio-temporal graph neural network to simultaneously explore the spatio-temporal dependencies in the airspace sector network. Specifically, we design a multimodal adaptive graph convolution module to effectively learn the diverse spatial relationships and adaptively adjust the impact of different spatial modes on airspace complexity in a data-driven manner. To model dynamic long-short-term temporal patterns, we develop a dilated causal convolution layer with a multiple-time-step self-attention mechanism to accurately predict airspace complexity over a longer time horizon. Extensive experiments on real-world air traffic datasets show that the proposed approach can harness differing spatial modes in achieving higher generalization performance across different temporal patterns, outperforming state-of-the-art methods in all prediction time horizons. |
| WOS关键词 | AIR-TRAFFIC COMPLEXITY ; LEARNING APPROACH ; MANAGEMENT |
| 资助项目 | National Key Research and Development Program of China[2021YFB1407005] ; National Natural Science Foundation of China[U2333218] ; National Natural Science Foundation of China[52302398] ; Engineering and Physical Sciences Research Council (EPSRC)[EP/N029496/2] |
| WOS研究方向 | Transportation |
| 语种 | 英语 |
| WOS记录号 | WOS:001182366900001 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Engineering and Physical Sciences Research Council (EPSRC) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/57867]  |
| 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 | Du, Wenbo |
| 作者单位 | 1.China Agr Univ, Coll Informat & Elect Engn, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China 3.Queen Mary Univ London, Sch Engn & Mat Sci, London E1 4NS, England 4.Nanjing LES Informat Technol CO LTD, Nanjing, Peoples R China 5.Beihang Univ, Sch Elect & Informat Engn, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Biyue,Li, Zhishuai,Chen, Jun,et al. MAST-GNN: A multimodal adaptive spatio-temporal graph neural network for airspace complexity prediction[J]. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES,2024,160:20. |
| APA | Li, Biyue,Li, Zhishuai,Chen, Jun,Yan, Yongjie,Lv, Yisheng,&Du, Wenbo.(2024).MAST-GNN: A multimodal adaptive spatio-temporal graph neural network for airspace complexity prediction.TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES,160,20. |
| MLA | Li, Biyue,et al."MAST-GNN: A multimodal adaptive spatio-temporal graph neural network for airspace complexity prediction".TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 160(2024):20. |
入库方式： OAI收割
来源：自动化研究所
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