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

作　　者：Kaiquan CAI Shuo TANG Shengsheng QIAN Zhiqi SHEN Yang YANG 

机构地区：[1]School of Electronic and Information Engineering,Beihang University,Beijing 100191,China [2]State Key Laboratory of CNS/ATM,Beijing,100191,China [3]Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China [4]Research Institute for Frontier Science,Beihang University,Beijing 100191,China

出　　处：《Chinese Journal of Aeronautics》2024年第7期301-316,共16页中国航空学报（英文版）

基　　金：supported by the National Key Research and Development Program of China(No.2022YFB2602402);the National Natural Science Foundation of China(Nos.U2033215 and U2133210).

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 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.

关 键 词：Air traffic control Graph neural network Multi-faceted information Air traffic flow prediction Multi-airport system 

分 类 号：V355[航空宇航科学与技术—人机与环境工程]