DyAdapTransformer: Dynamic Adaptive Spatial-Temporal Graph Transformer for Traffic Prediction

The transformer-based method is a popular choice for medium and long-term traffic prediction. However, it still suffers from some problems. The first is that spatial position embedding has poor interpretability. Additionally, the spatial-temporal correlation learning can struggle to reflect the actual complexity of traffic networks relationships. To address the above problems, we propose a traffic prediction framework for dynamic adaptive spatial-temporal graph transformer (DyAdapTransformer). Our method uses the method of random walk to embed the spatial position. The analyzability between transition probability and spatial position representation enhances the interpretability of the model. When learning spatial-temporal correlation, a method of dynamic adaptive graph attention network is proposed. We compared with our framework with four baselines on three datasets. The results show that DyAdapTransformer has a better predictive performance.