The invention discloses an urban traffic prediction method based on a hypergraph neural network under structural data loss. The urban traffic prediction method is composed of four main parts: data input, hypergraph construction, feature propagation, hypergraph convolution and traffic prediction. Based on multi-mode static topographic data and dynamic traffic data, two semantic hypergraphs and one geospatial graph are constructed and integrated to describe high-order semantic association and second-order geospatial association of fine granularity and coarse granularity of traffic conditions. In order to learn feature representation on a hypergraph, a new hypergraph convolution operator is obtained from graph convolution and hypergraph learning theories. The proposed hypergraph convolution is used as a deep network of a construction module, advanced feature representation of prediction is learned, and then the traffic condition is predicted. According to the method, feature propagation is carried out based on the hypergraph to process the missing features in the hypergraph learning model, and the structural missing features are reconstructed at a high missing rate by propagating the features among the nodes with the known features.
本发明公开了一种结构性数据缺失下基于超图神经网络的城市交通预测方法,由四个主要部分组成:数据输入、超图构建、特征传播、超图卷积和交通预测。基于多模式的静态地形数据和动态交通数据,构建并整合了两个语义超图和一个地理空间图,以描述交通状况的细粒度和粗粒度的高阶语义关联以及二阶地理空间关联。为了学习超图上的特征表示,从图卷积和超图学习理论中得到了一个新的超图卷积算子。通过使用所提出的超图卷积作为构建模块的深度网络,学习预测的高级特征表示,然后预测交通状况。本发明基于超图进行特征传播来处理超图学习模型中的缺失特征,通过在已知特征的节点之间传播特征,实现了在高缺失率下重建结构性缺失特征。
Urban traffic prediction method based on hypergraph neural network under structural data missing
结构性数据缺失下基于超图神经网络的城市交通预测方法
2024-02-02
Patent
Electronic Resource
Chinese
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS |
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