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.

中文翻译：

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CCDSReFormer：使用十字交叉双流增强型整流变压器模型进行交通流预测

准确、有效的交通预测对于智能交通系统至关重要，对于城市交通规划和管理至关重要。当前的 Spatio-Temporal Transformer 模型尽管具有预测能力，但在平衡计算效率和准确性、偏向全局信息而不是局部信息以及单独处理空间和时间数据方面存在困难，从而限制了对复杂交互的洞察。我们介绍了十字交叉双流增强型整流变压器模型（CCDSReFormer），该模型包括三个创新模块：增强型整流空间自注意力（ReSSA）、增强型整流延迟感知自注意力（ReDASA）和增强型整流时间自注意力（ReDASA）。注意力（ReTSA）。这些模块旨在通过稀疏注意力来降低计算需求，专注于本地信息以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洞察力。对六个真实世界数据集的广泛测试凸显了 CCDSReFormer 的卓越性能。消融研究还证实了每个组件对模型预测准确性的显着影响，展示了我们的模型有效预测交通流的能力。