DyAdapTransformer: dynamic adaptive spatial–temporal graph transformer for traffic flow prediction

This paper focuses on a mid-to-long-term traffic flow prediction method that integrates spatial–temporal graph neural networks with the Transformer. It systematically improves upon existing methods, which struggle to effectively capture the spatial–temporal evolution patterns of traffic flow in complex traffic scenarios. The current research faces three key issues: (1) The temporal position embedding model significantly lacks effectiveness in modeling short-term evolution features of traffic flow. (2) The spatial position embedding lacks a verifiable mechanism, making it difficult to confirm its validity in representing actual road network topologies. (3) The spatial–temporal graph has a single spatial structure, limiting the ability of spatial–temporal graph neural networks to capture multistage evolution patterns of traffic flow. To address these issues, we propose a traffic flow prediction framework based on a dynamic adaptive spatial–temporal graph Transformer. The framework first improves the temporal position embedding model's expressiveness by introducing attributes closely related to short-term traffic flow changes. Second, a random walk-based spatial embedding method is designed, where the transition probability matrix and node vector space mapping have a verifiable mathematical relationship, ensuring theoretical interpretability in the spatial position modeling process. Finally, a dynamic adaptive spatial–temporal graph neural network model is proposed. This model learns time-varying spatial structures in a data-driven manner based on an adaptive mechanism and combines temporal self-attention with dynamic adaptive graph attention networks to collaboratively capture multiscale spatial–temporal dependencies. Comparative experiments with six baseline methods on the real-world PEMS08 dataset demonstrate that the proposed framework exhibits significant performance advantages in traffic flow prediction.