基于时空融合图卷积的交通流数据修复方法  

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

作　　者：侯越[1] 韩成艳 郑鑫 邓志远 HOU Yue;HAN Cheng-yan;ZHENG Xin;DENG Zhi-yuan(School of Electronics and Information Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China)

机构地区：[1]兰州交通大学电子与信息工程学院,甘肃兰州730070

出　　处：《浙江大学学报（工学版）》2022年第7期1394-1403,共10页Journal of Zhejiang University：Engineering Science

基　　金：国家自然科学基金资助项目(62063014);甘肃省自然基金资助项目(20JR5RA407);甘肃省教育科技创新项目(2021CYZC-04);兰州交通大学“百名青年优秀人才培养计划”基金资助项目(1520220227).

摘　　要：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.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.

关 键 词：交通工程 时空融合 交通流数据修复 图卷积网络 一维卷积 

分 类 号：U491[交通运输工程—交通运输规划与管理]