Delay Prediction of Flight Operation Network Based on Deep Learning Combination Model
Publication Date
1-1-2023
Document Type
Conference Proceeding
Publication Title
CICTP 2023: Innovation-Empowered Technology for Sustainable, Intelligent, Decarbonized, and Connected Transportation - Proceedings of the 23rd COTA International Conference of Transportation Professionals
DOI
10.1061/9780784484869.074
First Page
761
Last Page
772
Abstract
Due to the correlation between flights in the airline’s flight operation network, when a flight delay occurs, it usually leads to a wide range of delay propagation. In this paper, a GCN-GRU combined prediction model is proposed to predict the level of departure delay owing to the constraints of the airline flight operation network topological structure and the law of dynamic change with time. Specifically, a graph convolution network (GCN) is used to capture the spatial characteristics of flight delay propagation and the gated recurrent unit (GRU) is used to capture the temporal characteristics of flight delay. Experiments show that the GCN-GRU model can obtain the spatio-temporal features from flight delay data. And on real-world flight delay data sets, the prediction results are better than state-of-art baselines. In addition, the experimental results are analyzed by using complex network theory, and the applicability of the model is obtained.
Funding Number
U1933118
Funding Sponsor
National Natural Science Foundation of China
Department
Aviation and Technology
Recommended Citation
Jiaxin Chen, Weiwei Wu, Wenbin Wei, and Jiahui Yu. "Delay Prediction of Flight Operation Network Based on Deep Learning Combination Model" CICTP 2023: Innovation-Empowered Technology for Sustainable, Intelligent, Decarbonized, and Connected Transportation - Proceedings of the 23rd COTA International Conference of Transportation Professionals (2023): 761-772. https://doi.org/10.1061/9780784484869.074
