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.

精确、高效的交通预测对于智能交通系统至关重要，在城市交通规划和管理中具有重要意义。我们介绍了 Criss-Crossed Dual-Stream Enhanced Rectified Transformer 模型（CCDSReFormer），该模型通过 Enhanced Rectified Spatial Self-attention（ReSSA）、Enhanced Rectified Delay Aware Self-attention（ReDASA）和 Enhanced Rectified Temporal Self-attention（ReTSA）三个创新模块，旨在降低计算需求、更好地理解交通动态，并通过独特的学习方法融合空间和时间的见解。对六个真实数据集的广泛测试显示 CCDSReFormer 具有更优越的性能，消融研究也证实了每个组件对模型预测准确性的显著影响，展示了我们模型有效预测交通流量的能力。