考虑时空相似性的动态图卷积神经网络交通流预测

高精度的交通流预测对于大型城市的交通管理和智慧出行具有重要作用,而交通流动态时空相关性的挖掘则是提高预测精度的关键.针对现有研究中存在的对交通流在不同时间尺度下呈现出的高度相似性,以及处于相似功能区的非邻近节点间交通流变化的相似性考虑不足的问题,构建考虑时空相似性的动态图卷积神经网络(dynamic graph convolution neural network considering spatio-temporal similarity,STS-DGCN).以相邻时段、日和周等多时间尺度下的数据输入张量表达交通流数据的时间相似性,以路网节点间距离度量、相似性度量、自适应嵌入、动态相关性等多属性特征的邻接矩阵表达交通流数据的时空相似性,进而基于这些邻接矩阵构建反映路网节点时空动态变化的动态图,并设计相应的时空特征挖掘算法.在公开数据集上的实验结果表明,所提出模型的预测结果优于目前较为先进的对比基线模型,具有更高的预测精度.

Traffic flow prediction based on STG-CRNN

High precision traffic flow prediction plays an important role in traffic management and intelligent travel in large cities. Mining the temporal and spatial correlation of traffic flow dynamics is the key to improve the prediction accuracy. In view of the insufficient consideration of the high similarity of traffic flow in different time scales and the similarity of traffic flow changes between non adjacent nodes in similar functional areas, a dynamic graph convolution neural network considering spatio-temporal similarity(STS-DGCN) is constructed. The time similarity of traffic flow data is expressed by the data input tensor under multiple time scales such as adjacent periods, days and weeks, and the time-space similarity of traffic flow data is expressed by the adjacency matrix with multi-attribute characteristics such as distance measurement, similarity measurement, adaptive embedding and dynamic correlation between road network nodes. Then, based on these adjacency matrices, a dynamic graph reflecting the temporal and spatial dynamic changes of road network nodes is constructed, the corresponding spatio-temporal feature mining algorithm is designed. The results show that the prediction result of the model is better than the more advanced baseline model and has higher prediction accuracy.