  Accurate, and effective traffic forecasting is vital for smart traffic
systems, crucial in urban traffic planning and management. Current
Spatio-Temporal Transformer models, despite their prediction capabilities,
struggle with balancing computational efficiency and accuracy, favoring global
over local information, and handling spatial and temporal data separately,
limiting insight into complex interactions. We introduce the Criss-Crossed
Dual-Stream Enhanced Rectified Transformer model (CCDSReFormer), which includes
three innovative modules: Enhanced Rectified Spatial Self-attention (ReSSA),
Enhanced Rectified Delay Aware Self-attention (ReDASA), and Enhanced Rectified
Temporal Self-attention (ReTSA). These modules aim to lower computational needs
via sparse attention, focus on local information for better traffic dynamics
understanding, and merge spatial and temporal insights through a unique
learning method. Extensive tests on six real-world datasets highlight
CCDSReFormer's superior performance. An ablation study also confirms the
significant impact of each component on the model's predictive accuracy,
showcasing our model's ability to forecast traffic flow effectively.
