Abstract
Accurate traffic flow prediction serves as the foundation for urban traffic guidance and control, playing a crucial role in intelligent transportation management and regulation. However, current methods fail to fully capture the complex patterns and periodic characteristics of traffic flow, leading to significant discrepancies between predicted and actual values. This gap hampers the achievement of high-precision forecasting. To address this issue, this paper proposes a temporal representation learning enhanced dynamic adversarial graph convolutional network (TRL-DAG). Our approach utilizes a temporal representation learning strategy with masked reconstruction for pre-training, aiming to extract temporal representations from contextual subsequences in historical traffic data. We further introduce a dynamic graph generation network to enhance the flexibility of graph convolution, enabling the model to capture dynamic spatiotemporal correlations by integrating both current and historical states. Additionally, we design an adversarial graph convolutional framework, which optimizes the loss through adversarial training, thereby reducing the trend discrepancy between predicted and actual values. Experiments conducted on six real-world datasets demonstrate that TRL-DAG outperforms existing state-of-the-art methods, achieving superior performance in traffic flow prediction.