The invention provides an equivalent hypergraph construction method for traffic flow prediction. The equivalent hypergraph construction method comprises the following steps: S1, data collection: collecting traffic flow data of each region through a sensor of an intelligent traffic system; s2, data processing: carrying out preprocessing and normalization processing on the collected traffic flow data, and constructing a graph structure of a traffic system and a training graph neural network model; s3, constructing a graph neural network: selecting a GCN as a framework of the graph neural network; s4, client node construction: performing feature fusion on the graph constructed for the local jurisdiction by each client through an attention mechanism; and S5, constructing an equivalent hypergraph: for n clients formed by traffic flow information of n regions, repeating the step S4 to obtain n client nodes, adding edges and weights according to a region adjacency principle to obtain an initial equivalent hypergraph, and continuously training and comparing based on the initial equivalent hypergraph to finally obtain the equivalent hypergraph. The risk of privacy disclosure is reduced, the efficiency of model training is improved, and the prediction precision of the model is improved.
本发明提供一种用于交通流预测的等价超图构建方法,包括S1数据采集:通过智能交通系统的传感器收集各个地区的交通流数据;S2数据处理:对收集到的交通流数据进行预处理和归一化处理,构建交通系统的图结构及训练图神经网络模型;S3构建图神经网络:选择GCN作为图神经网络的架构;S4客户端节点构建:通过注意力机制针对每一个客户端为本地辖区构建的图进行特征融合;S5构建等效超图:对于n个地区的交通流信息形成的n个客户端,重复步骤S4,得到n个客户端节点,根据区域相邻原则添加边和权重得到初始等效超图,基于初始等效超图不断训练对比,最终获得等效超图。本发明降低了隐私泄露的风险、提高了模型训练的效率,提升了模型的预测精度。
Equivalent hypergraph construction method for traffic flow prediction
一种用于交通流预测的等价超图构建方法
2025-02-11
Patent
Electronic Resource
Chinese
IPC: | G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS |
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