Multi-faceted spatio-temporal network for weather-aware air traffic flow prediction in multi-airport system
文献类型：期刊论文
| 作者 | Cai, Kaiquan1,2; Tang, Shuo1,2; Qian, Shengsheng3 ; Shen, Zhiqi1,2; Yang, Yang2,4
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| 刊名 | CHINESE JOURNAL OF AERONAUTICS
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| 出版日期 | 2024-07-01 |
| 卷号 | 37期号:7页码:301-316 |
| 关键词 | Air traffic control Graph neural network Multi-faceted information Air traffic flow prediction Multi-airport system |
| ISSN号 | 1000-9361 |
| DOI | 10.1016/j.cja.2024.03.003 |
| 通讯作者 | Yang, Yang(buaayangyang@buaa.edu.cn) |
| 英文摘要 | As one of the core modules for air traffic flow management, Air Traffic Flow Prediction (ATFP) in the Multi-Airport System (MAS) is a prerequisite for demand and capacity balance in the complex meteorological environment. Due to the challenge of implicit interaction mechanism among traffic flow, airspace capacity and weather impact, the Weather-aware ATFP (Wa-ATFP) is still a nontrivial issue. In this paper, a novel Multi-faceted Spatio-Temporal Graph Convolutional Network (MSTGCN) is proposed to address the Wa-ATFP within the complex operations of MAS. Firstly, a spatio-temporal graph is constructed with three different nodes, including airport, route, and fix to describe the topology structure of MAS. Secondly, a weather-aware multi-faceted fusion module is proposed to integrate the feature of air traffic flow and the auxiliary features of capacity and weather, which can effectively address the complex impact of severe weather, e.g., thunderstorms. Thirdly, to capture the latent connections of nodes, an adaptive graph connection constructor is designed. The experimental results with the real-world operational dataset in GuangdongHong Kong-Macao Greater Bay Area, China, validate that the proposed approach outperforms the state-of-the-art machine-learning and deep-learning based baseline approaches in performance. The case study of convective weather scenarios further proves the adaptability of the proposed approach. (c) 2024 Production and hosting by Elsevier Ltd. on behalf of Chinese Society of Aeronautics and Astronautics. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/). |
| WOS关键词 | TRAJECTORY PREDICTION ; ALGORITHM |
| 资助项目 | National Key Research and Development Program of China[2022YFB2602402] ; National Natural Science Foundation of China[U2033215] ; National Natural Science Foundation of China[U2133210] |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001279298700001 |
| 出版者 | ELSEVIER SCIENCE INC |
| 资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/59370]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队 |
| 通讯作者 | Yang, Yang |
| 作者单位 | 1.Beihang Univ, Sch Elect & Informat Engn, Beijing 100191, Peoples R China 2.State Key Lab CNS ATM, Beijing 100191, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 4.Beihang Univ, Res Inst Frontier Sci, Beijing 100191, Peoples R China |
| 推荐引用方式 GB/T 7714 | Cai, Kaiquan,Tang, Shuo,Qian, Shengsheng,et al. Multi-faceted spatio-temporal network for weather-aware air traffic flow prediction in multi-airport system[J]. CHINESE JOURNAL OF AERONAUTICS,2024,37(7):301-316. |
| APA | Cai, Kaiquan,Tang, Shuo,Qian, Shengsheng,Shen, Zhiqi,&Yang, Yang.(2024).Multi-faceted spatio-temporal network for weather-aware air traffic flow prediction in multi-airport system.CHINESE JOURNAL OF AERONAUTICS,37(7),301-316. |
| MLA | Cai, Kaiquan,et al."Multi-faceted spatio-temporal network for weather-aware air traffic flow prediction in multi-airport system".CHINESE JOURNAL OF AERONAUTICS 37.7(2024):301-316. |
入库方式： OAI收割
来源：自动化研究所
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