基于注意力解耦的时空图网络，提升交通流量预测精度。
SFADNet: Spatio-temporal Fused Graph based on Attention Decoupling Network for Traffic Prediction
- 按时空特征分段建模，每类模式独立构建自适应图结构。
- 在四个大数据集上均超越现有最佳模型，显著提升预测准确率。
- 适合需要高精度交通流预测的智能交通系统研究者。
近年来，交通流量预测在智能交通系统管理中扮演着关键角色。然而，传统方法常受限于静态空间建模，难以准确捕捉时空之间的动态复杂关系，从而影响预测精度。本文提出一种创新的交通流量预测网络 SFADNet，根据时间与空间特征矩阵将交通流划分为多个交通模式。针对每种模式，基于交叉注意力机制构建独立的自适应时空融合图，结合残差图卷积模块与时序模块，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均优于当前最先进的基线模型。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。