基于知识图谱和时空扩散图卷积网络的港口交通流量预测  

Port traffic flow prediction based on knowledge graph and spatio-temporal diffusion graph convolutional network

作　　者：薛桂香 王辉 周卫峰 刘瑜 李岩 XUE Guixiang;WANG Hui;ZHOU Weifeng;LIU Yu;LI Yan(School of Civil and Transportation Engineering,Hebei University of Technology,Tianjin 300401,China;School of Artificial Intelligence,Hebei University of Technology,Tianjin 300401,China;Intelligent Transportation Monitoring Center of Tianjin,Tianjin 300250,China;Tianjin Port Information Technology Development Company Limited,Tianjin 300461,China)

机构地区：[1]河北工业大学土木与交通学院,天津300401 [2]河北工业大学人工智能与数据科学学院,天津300401 [3]天津市智能交通运行监测中心,天津300250 [4]天津港信息技术发展有限公司,天津300461

出　　处：《计算机应用》2024年第9期2952-2957,共6页journal of Computer Applications

基　　金：国家自然科学基金资助项目(52208240);天津市科技计划项目(23ZGCXQY00030)。

摘　　要：由于港口交通流量具有随机不确定性、时间不平稳特征,因此港口交通流量的精准预测是一项具有挑战性的任务。为了提高港口交通流量预测精度,考虑气象条件和港口相邻高速公路开闭状态等外部干扰因素,提出了一种基于知识图谱和时空扩散图卷积网络的港口交通流量预测算法KG-DGCN-GRU。知识图谱表示港口交通网络相关因素,知识表示方法从港口知识图谱中学习各外部因素的语义信息,扩散图卷积网络(DGCN)和门控循环单元(GRU)能有效挖掘港口交通流量的时空依赖特征。基于天津港交通数据集的实验结果表明,KG-DGCN-GRU能通过知识图谱和扩散图卷积有效提高预测精度,在单步预测(15 min)中与时间图卷积网络(T-GCN)和扩散卷积递归神经网络(DCRNN)相比,均方根误差(RMSE)分别降低了4.85%和7.04%,平均绝对误差(MAE)分别降低了5.80%和8.17%。Accurate prediction of port traffic flow is a challenging task due to its stochastic uncertainty and timeunsteady characteristics.In order to improve the accuracy of port traffic flow prediction,a port traffic flow prediction model based on knowledge graph and spatio-temporal diffusion graph convolution network,named KG-DGCN-GRU,was proposed,taking into account the external disturbances such as meteorological conditions and the opening and closing status of the portadjacent highway.The factors related to the port traffic network were represented by the knowledge graph,and the semantic information of various external factors were learned from the port knowledge graph by using the knowledge representation method,and Diffusion Graph Convolutional Network(DGCN)and Gated Recurrent Unit(GRU)were used to effectively extract the spatio-temporal dependency features of the port traffic flow.The experimental results based on the Tianjin Port traffic dataset show that KG-DGCN-GRU can effectively improve the prediction accuracy through knowledge graph and diffusion graph convolutional network,the Root Mean Squared Error(RMSE)is reduced by 4.85%and 7.04%and the Mean Absolute Error(MAE)is reduced by 5.80%and 8.17%,compared with Temporal Graph Convolutional Network(T-GCN)and Diffusion Convolutional Recurrent Neural Network(DCRNN)under single step prediction(15 min).

关 键 词：港口交通流量预测 知识图谱 时空依赖 门控循环单元 图卷积网络 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]