首页|Multi-faceted spatio-temporal network for weather-aware air traffic flow prediction in multi-airport system

Multi-faceted spatio-temporal network for weather-aware air traffic flow prediction in multi-airport system

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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.,thunder-storms.Thirdly,to capture the latent connections of nodes,an adaptive graph connection construc-tor is designed.The experimental results with the real-world operational dataset in Guangdong-Hong 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.

Air traffic controlGraph neural networkMulti-faceted informationAir traffic flow predictionMulti-airport system

Kaiquan CAI、Shuo TANG、Shengsheng QIAN、Zhiqi SHEN、Yang YANG

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School of Electronic and Information Engineering,Beihang University,Beijing 100191,China

State Key Laboratory of CNS/ATM,Beijing,100191,China

Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China

Research Institute for Frontier Science,Beihang University,Beijing 100191,China

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National Key Research and Development Program of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of China

2022YFB2602402U2033215U2133210

2024

10.1016/j.cja.2024.03.003

中国航空学报(英文版)
中国航空学会

中国航空学报(英文版)

CSTPCDEI
影响因子：0.847
ISSN：1000-9361
年,卷(期)：2024.37(7)
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