Dueling DQN优化下的航班延误自适应图卷积循环网络预测方法

doi:10.12305/j.issn.1001-506X.2025.02.23

Abstract

Abstract:

To fully explore the spatio-temporal dynamic correlation between airport network flights to reduce prediction errors, a multi-component adaptive graph convolutional recurrent network flight delay prediction model based on dueling deep Q network (Dueling DQN) optimization is proposed. Firstly, by combining adaptive graph convolutional network (GCN) with multi-head spatial attention mechanisms, parallel capture and fusion of delay information from multiple subspaces is achieved, fully exploiting nonlinear spatial dynamic features. Secondly, grated recurrent unit (GRU) is used as the basis for the time feature extraction module, and time attention mechanism is introduced to learn the attention weights between historical delay data. Then, multiple time dimension input components are set up to increase the diversity of constructing different time patterns. Finally, Dueling DQN is used to optimize the hyperparameters of the multi-componet adaptive graph convolution-GRU (MAGC-GRU) model. The experimental results show that the mean absolute error (MAE) of the proposed model decreased by 10.6%, 6.07%, 9.18%, 3.79%, and 3.12% compared to historical average, random forest, gradient boosting regression tree, GRU, and spatial-temporal GCN, respectively.

Key words: flight delay prediction, deep learning, reinforcement learning, multi-component fusion, graph convolution network (GCN)

CLC Number:

V351

Xiaolin LIU, Mengjiao GUO, Zhuo LI. Adaptive graph convolutional recurrent network prediction method for flight delay based on Dueling DQN optimization[J]. Systems Engineering and Electronics, 2025, 47(2): 568-579.

Figures/Tables 15

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时间/天	评价指标	模型
时间/天	评价指标	HA	RF	GBRT	GRU	STGCN	MAGC-GRU
1	RMSE	8.907	8.404	8.471	8.219	8.186	8.003
	MAE	6.589	6.271	6.486	6.122	6.080	5.890
	MAPE/%	8.685	8.280	8.315	8.118	8.065	7.864
2	RMSE	8.907	8.652	8.471	8.621	8.688	8.320
	MAE	6.589	6.490	6.486	6.313	6.493	6.180
	MAPE/%	8.685	8.602	8.613	8.494	8.647	8.236
3	RMSE	8.907	8.742	8.784	8.679	8.786	8.476
	MAE	6.589	6.565	6.565	6.541	6.565	6.239
	MAPE/%	8.685	8.716	8.729	8.661	8.762	8.364
4	RMSE	8.907	8.772	8.802	8.753	8.821	8.546
	MAE	6.589	6.597	6.593	6.686	6.662	6.334
	MAPE/%	8.685	8.762	8.768	8.794	8.846	8.472
5	RMSE	8.907	8.793	8.823	8.797	8.861	8.645
	MAE	6.589	6.623	6.627	6.703	6.697	6.463
	MAPE/%	8.685	8.793	8.806	8.838	8.895	8.611

优化方法	评价指标
优化方法	MAE	RMSE	MAPE/%
PSO	5.965	8.073	8.067
DDQN	5.893	8.042	8.023
Dueling DQN	5.890	8.003	7.864

预测模型	评价指标
预测模型	MAE	RMSE	MAPE/%
HA	6.418	8.788	8.682
GRU	6.080	8.260	8.235
RF	6.248	8.448	8.416
GBRT	6.243	8.506	8.435
STGCN	5.967	8.140	8.087
MAGC-GRU	5.874	8.061	8.010

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Adaptive graph convolutional recurrent network prediction method for flight delay based on Dueling DQN optimization

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