The invention belongs to the technical field of intelligent traffic, and discloses a traffic prediction method and system based on a graph neural network. The method comprises the following steps of: performing data preprocessing on a training data set and a test data set which are initialized and configured by a system; a self-adaptive decoupling technology is adopted, a reasonable weight is given to historical data, the historical data is decoupled into a diffusion signal and an inherent signal, and self-adaptive decision making is effectively carried out on the influence of the historical data on the current data in subsequent steps; for diffusion signals, the frequency characteristics of the traffic data are accurately extracted by introducing a Fourier graph convolutional network, and the prediction precision is further improved; aiming at inherent signals, a traffic prediction model is constructed by adopting a double-layer recurrent neural network, time sequence characteristics are effectively captured, and accurate prediction of future traffic conditions is realized; meanwhile, a dynamic diffusion intensity model is established according to different diffusion intensities in different time periods among the nodes; experimental verification is carried out on the method in combination with real traffic data, and the result shows that the method has high prediction precision and stability in a traffic prediction task. The invention provides a new method for the field of traffic prediction, and the method has a wide application prospect and an important practical value.
本发明属于智慧交通技术领域,公开了一种基于图神经网络的交通预测方法及系统。该方法对系统初始化和配置的训练数据集以及测试数据集进行数据预处理;采用自适应解耦技术,为历史数据赋予合理的权重,并将历史数据解耦为扩散信号与固有信号,有效的为后续步骤中历史数据对当前数据的影响进行自适应决策;针对扩散信号,通过引入傅里叶图卷积网络,实现对交通数据的频率特性进行精准提取,进一步提升预测精度;针对固有信号,采用双层递归神经网络构建交通预测模型,有效捕捉时序特性,实现了对未来交通状况的精准预测;同时根据节点间不同时段扩散强度不同,建立了动态扩散强度模型;对本发明结合真实交通数据进行实验验证,结果表明所提方法在交通预测任务中具有较高的预测精度和稳定性。本发明为交通预测领域提供了一种新的方法,具有广阔的应用前景和重要的实用价值。
Traffic flow prediction method based on graph neural network
基于图神经网络的交通流量预测方法
2025-04-25
Patent
Electronic Resource
Chinese
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