基于时空图卷积循环神经网络的交通流预测

针对交通流预测模型中路网空间结构刻画和交通流时空特性挖掘不充分的问题,构建一种新型的有向时空图,通过定义节点相对临近度来表征路网结构关系,通过学习邻域节点对预测节点的影响权重来表征节点间时空维度的作用关系,从而能更好表达交通流的时空特性.将时空图作为预测模型的输入,采用图卷积获取交通流数据空间依赖关系,采用门控循环神经...

Traffic flow prediction based on STG-CRNN

Aiming at the problem of insufficient characterization of road network spatial structure and inadequate mining of traffic flow spatiotemporal characteristics in the traffic flow prediction model, a new type of directed spatiotemporal graph is constructed. In which the spatial relationship between nodes are characterized by defining the relative proximity, and the spatiotemporal relationship between nodes are characterized by learning the influence weights of neighborhood nodes on the prediction node, so as to better express the temporal and spatial characteristics of traffic flow. Taking spatiotemporal graphs as the input of the prediction model, graph convolution is used to obtain the spatial dependence of traffic flow data, and the gated recurrent neural network is used to obtain the spatiotemporal dependence of traffic flow data, and a traffic flow based on the spatiotemporal graph convolution recurrent neural network(STG-CRNN) is established. The model prediction effect is verified on the U.S. highway traffic data set, and the results show that the STG-CRNN model is better than the autoregressive moving average model, the gated recurrent unit model, and the diffusion convolutional recurrent neural network model in terms of the mean absolute error, root mean square error, and mean absolute percentage error.