基于时空融合图卷积的交通流数据修复方法

为了解决现有时空相关修复法挖掘交通流特性不充分的问题，提出基于时空融合图卷积网络的缺失数据修复方法. 该方法在分析交通流时空特性的基础上，采用2类函数分别计算交通流数据的时间自相关系数和空间关联度系数. 将交通检测器的部署位置作为节点构成几何拓扑图，通过线性融合规则构建时空融合矩阵，替代图卷积输入层的邻接矩阵，捕获交通流细粒化的时空关系. 利用轻量级一维卷积层学习多通道时序向量的时间特征，加快模型的收敛速度. 利用图卷积层学习交通流数据的空间特征，构建时空融合图卷积网络修复模型. 实验结果表明，与其他修复方法相比，该方法在多检测器场景中的修复精度和模型收敛速度均有所提升，可以有效地修复交通流缺失数据.

Traffic flow data repair method based on spatial-temporal fusion graph convolution

A missing data repair method based on spatio-temporal fusion graph convolutional network was proposed in order to solve the problem of insufficient traffic flow characteristics mining by existing spatio-temporal correlation repair method. Two types of functions were used to respectively calculate the temporal autocorrelation coefficient and spatial correlation coefficient of traffic flow data by analyzing the spatio-temporal characteristics of traffic flow. The deployment position of the traffic detector was used as a node to form a geometric topology graph, and a spatio-temporal fusion matrix was constructed by linear fusion rules, which replaced the adjacency matrix of the graph convolution input layer to capture the fine-grained spatio-temporal relationship of the traffic flow. The lightweight one-dimensional convolution layer was used to learn the temporal characteristics of multi-channel time series vectors in order to speed up the convergence speed of the model. The graph convolutional layer was used to learn the spatial characteristics of traffic flow data. A spatio-temporal fusion graph convolution network repair model was constructed. The experimental results show that the repair accuracy and model convergence speed of the method in multi-detector scenarios were improved compared with other repair methods, which can effectively repair the missing traffic flow data.