Bi-STDGCN: A Bidirectional Spatiotemporal Diffusion Graph Convolutional Network With Multiscale Anomaly-Guided Attention for Traffic Forecasting

Accurate traffic flow prediction is a key application in Internet of Things (IoT)-enabled intelligent transportation systems (ITS). However, complex spatiotemporal dependencies and non-stationary anomalies make reliable forecasting particularly challenging. To address these issues, we propose Bi-STDGCN, a Bidirectional Spatiotemporal Diffusion Graph Convolutional Network with Multi-Scale Anomaly-Guided Attention. The core idea is to jointly model anomaly-aware temporal variations and bidirectional spatial diffusion within a unified multi-source dynamic graph framework. First, an Anomaly-Guided Feature Enhancement Module (AGFEM) employs a difference-driven attention mechanism guided by multi-scale temporal patterns derived from wavelet-based decomposition, enhancing the model’s ability to perceive periodic disruptions and sudden traffic fluctuations. Second, Bi-STDGCN captures directional dependencies in both time and space by integrating a bidirectional diffusion graph convolutional recurrent unit (BDDGCRU) for temporal modeling to reduce prediction lag under anomalies, and a bidirectional diffusion GCN (BD-GCN) to model upstream propagation and downstream feedback patterns in the spatial domain. Third, the Multi-source Dynamic Attention Graph Generator (DAGG) incorporates static topology, temporal attributes, real-time traffic states, and hidden node states to construct robust time-varying spatial graphs. Extensive experiments on five real-world datasets demonstrate that Bi-STDGCN achieves state-of-the-art accuracy and robustness, while ablation and visualization analyses further validate the effectiveness of each component.