SFADNet：用于交通预测的基于注意力解耦网络的时空融合图
SFADNet: Spatio-temporal Fused Graph based on Attention Decoupling Network for Traffic Prediction
AI总结：
针对静态空间建模难以捕捉动态时空关系的问题，本文提出 SFADNet，通过多交通模式划分、交叉注意力自适应融合图及残差图卷积与时序模块提升交通流预测性能。
摘要：
近年来，交通流预测在智能交通系统管理中发挥着关键作用。然而，传统预测方法往往受限于静态空间建模，难以准确捕捉时间与空间之间动态且复杂的关系，从而影响预测精度。本文提出了一种创新的交通流预测网络 SFADNet，该网络基于时间和空间特征矩阵将交通流划分为多种交通模式。对于每种模式，我们基于交叉注意力机制构建独立的自适应时空融合图，并采用残差图卷积模块和时间序列模块，以更好地捕捉不同细粒度交通模式下的动态时空关系。大量实验结果表明，SFADNet 在四个大规模数据集上的表现优于当前最先进的基线方法。
英文摘要：
In recent years, traffic flow prediction has played a crucial role in the management of intelligent transportation systems. However, traditional prediction methods are often limited by static spatial modeling, making it difficult to accurately capture the dynamic and complex relationships between time and space, thereby affecting prediction accuracy. This paper proposes an innovative traffic flow prediction network, SFADNet, which categorizes traffic flow into multiple traffic patterns based on temporal and spatial feature matrices. For each pattern, we construct an independent adaptive spatio-temporal fusion graph based on a cross-attention mechanism, employing residual graph convolution modules and time series modules to better capture dynamic spatio-temporal relationships under different fine-grained traffic patterns. Extensive experimental results demonstrate that SFADNet outperforms current state-of-the-art baselines across four large-scale datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
