基于图卷积神经网络和注意力机制的短时交通流量预测

Short-term Traffic Flow Prediction Based on Graph Convolutional Neural Network and Attention Mechanism

本文提出一种基于图卷积神经网络和注意力机制的短时交通流量预测模型.该模型利用图卷积网络捕获路网流量空间特征;利用自注意力机制调整网络输出,提高最终预测结果的精确度.实验结果表明,相较于对比方法,本文提出的图卷积注意力网络模型可提升预测精度.

In this paper, we propose a hybrid method with graph convolutional neural networks and attention mechanism for traffic flow prediction. The graph convolution network is used to capture the spatial features of traffic flow, and the attention mechanism is introduced to adjust and assemble the output to improve the prediction accuracy. Experimental results show that the proposed model outperforms the competing methods and can improve prediction accuracy.