近几年，时空图卷积网络（spatial-temporal graph convolutional network, STGCN）被引入交通流量预测中，具有良好的时空交通数据建模能力，取得了先进的性能，但是仍存在两个问题：①交通流量数据具有很强的时空相关性；②静态的预定义图难以捕获交通流随时间动态变化的时空依赖关系。为解决以上问题，提出了一种新的时空分解框架（spatial-temporal decomposed framework, STDF），它使用了残差连接、遗忘门、更新门，将时间模块和空间模块有机连接起来，以将输入信息进行多层次双维度的分解和预测。此外将STDF进行实例化，提出一种新的基于输入交通信号分解的动态时空融合的交通预测模型（decomposed dynamic spatial-temporal graph convolutional network, DDSTGCN），它捕捉了交通的时空相关性，并设计了一个动态图学习模块，考虑了空间依赖的动态性质。最后利用两个真实交通流量的数据（在PEMS04和PEMS08的数据集），与现有的交通流量预测算法进行对比。实验结果证明，所提方法在交通流量预测的准确率有良好的性能表现，能够有效地完成真实场景下的交通流量预测。

In recent years, spatial-temporal graph convolutional network (STGCN) has been introduced into traffic flow prediction, which has good spatial-temporal traffic data modeling ability and has achieved advanced performance, but there are still two problems:①Traffic flow data have strong temporal and spatial correlation; ②Static pre-defined graphs are difficult to capture the spatio-temporal dependence of dynamic changes in traffic flow over time. To solve the above problems, a new spatial-temporal decomposed framework(STDF) was proposed, which used residual connection, forgetting gate and update gate to organically connect time module and space module to decompose and predict input information in multiple dimensions. In addition, by instantiating STDF, a new traffic prediction model based on input traffic signal decomposition decomposed dynamic spatial-temporal graph convolutional network (DDSTGCN) was proposed. It captured the spatiotemporal dependencies of traffic and designed a dynamic graph learning module that takes into account the dynamic nature of spatial dependencies. Finally, two real traffic flow data were used to compare with the existing traffic flow prediction algorithms. The experimental results show that the proposed method has good performance in the accuracy of traffic flow prediction and can effectively complete the traffic flow prediction in the real scenario.