Abstract
In the realm of traffic prediction, emerging are methodologies founded on graph convolutional networks. Nonetheless, existing approaches grapple with issues encompassing insufficient sharing patterns, dependence on static relationship presumptions, and an inability to effectively grasp the intricate trends and cyclic attributes of traffic flow. To tackle this challenge, we introduce a novel framework termed Dynamic Graph Convolutional Networks with Temporal Representation Learning for Traffic Flow Prediction (DGCN-TRL). Specifically, a temporal graph convolution block is specifically devised, treating historical time slots as graph nodes and employing graph convolution to process dynamic time series. This approach effectively captures flexible global temporal dependencies, enhancing the model's ability to comprehend current traffic conditions. Subsequently, a novel dynamic graph constructor is introduced to explore spatial correlations between nodes at specific times and dynamic temporal dependencies across different time points. This meticulous exploration uncovers dynamic spatiotemporal relationships. Finally, a novel temporal representation learning module is developed utilizing a masked subsequence transformer to predict the content of masked subsequences from a fraction of unmasked subsequences and their temporal contexts in a pre-trained manner. This design encourages the model to adeptly learn temporal representations of contextual subsequences from extensive historical data. Empirical evaluations on four real datasets substantiate the superior performance of DGCN-TRL compared to existing methodologies.