基于动态扩散图卷积的交通流量预测算法

Traffic flow prediction algorithm based on dynamicdiffusion graph convolution

为了得到准确的交通流量预测结果,提出一种基于动态扩散图卷积的交通流量预测模型。首先,利用扩散图卷积模型对不同节点间的空间特征进行学习;其次,通过引入动态邻接矩阵,以确保各节点在不同时刻间的特征都得到充分学习;再次,采用门控循环单元,对交通流量数据进行时间特征提取;最后,通过模型层级间的残差连接,传递更多原始信息以增强模型的稳定性。在4个公开数据集上的实验结果证明本文算法在交通流量预测任务中的有效性。

In order to obtain accurate traffic flow prediction results,a traffic flow prediction algorithm based on dynamic diffusion graph convolution was proposed.Firstly,the model used the diffusion graph convolution model to learn the spatial characteristics between different nodes.Secondly,the dynamic adjacency matrix was introduced to ensure that the characteristics of each node at each time can be learned.Once more,the model used the gated recurrent unit to extract the time characteristics of traffic flow data.Finally,residual connection between model levels was used to transfer more original information and enhance the stability of the model.The experimental results on four open data sets can prove the effectiveness of the algorithm in traffic flow prediction tasks.