Dynamic Dual-Channel Asynchronous Graph Neural Network for Traffic Prediction with Missing Values

Traffic prediction has gained significant attention due to its crucial role in advancing intelligent transportation systems. Most traffic prediction models rely on idealized complete-data assumptions, yet real-world data often contains missing values from sensor or communication failures. Existing traffic prediction methods fail to model robust spatio-temporal features under missing data conditions, and current data imputation techniques cannot adequately handle incomplete traffic data with complex spatio-temporal dependencies. To this end, we propose a Dynamic Dual-Channel Asynchronous Graph Neural Network (D 2 AGNN) for traffic prediction with missing values. Our model aims to explore a joint multi-task training that leverages the synergy of imputing missing data and traffic predictions to enhance the accuracy of traffic predictions under incomplete data. First, D 2 AGNNdesigns a Dual-Channel Asynchronous Graph Neural Network (DAGNN) to generate enhanced node representations, effectively capturing hidden information transfers between nodes, even in the presence of missing data. Secondly, we present a dynamic graph learning that uses these representations to construct a dynamic traffic graph at each time interval, capturing the evolving states of traffic. Finally, a ST-Block processes inherent and diffusion traffic data separately, significantly enhancing prediction accuracy. Extensive experiments on two real-world traffic datasets demonstrate that our model consistently outperforms state-of-the-art models, especially in scenarios with substantial missing data. Compared with SOTA baseline, D 2 AGNNachieves average improvements in MAE, RMSE and MAPE of 9.47%, 11.35%, and 9.76%, respectively, on PEMS-BAY, and 9.40%, 9.46%, and 11.22% on METR-LA.