及时准确的交通预测对城市交通控制和引导至关重要。由于交通数据的复杂性和非平稳变化,传统的预测方法不能满足中长期预测任务的要求,往往忽略了交通流的时空依赖性。文章采用了一种新的深度学习框架——动态图卷积网络(DGCN)来解决交通领域的时间序列预测问题。我们没有使用常规的卷积和循环单元,而是在图上表达问题,该网络引入潜在网络提取时空特征,用于自适应构建动态路网图矩阵。实验表明,我们的模型DGCN有效捕获了全面的时空相关性,并在各种真实交通数据集上始终优于最先进的基线。Timely and accurate traffic forecasting is very important for urban traffic control and guidance. Due to the complexity and non-stationary changes of traffic data, traditional forecasting methods can not meet the requirements of medium and long-term forecasting tasks and often ignore the temporal and spatial dependence of traffic flow. In this paper, a new deep learning framework—Dynamic Graph Convolution Network (DGCN), is adopted to solve the problem of time series prediction in the traffic field. We don’t use the conventional convolution and circulation unit, but express the problem on the graph. The network introduces the potential network to extract the spatio-temporal features and is used to adaptively construct the dynamic road network graph matrix. Experiments show that our model DGCN effectively captures the comprehensive spatial-temporal correlation and is always superior to the most advanced baseline on various real traffic data sets.
