The invention provides a traffic flow prediction method based on a space-time neural network, and relates to the technical field of traffic flow prediction. The method specifically comprises the steps of obtaining traffic flow data, and obtaining a traffic flow sequence through data preprocessing according to the traffic flow data; building a space-time dynamic graph convolutional neural network model; training the spatial-temporal dynamic graph convolutional neural network model by using the traffic flow sequence to obtain a trained spatial-temporal dynamic graph convolutional neural network model; and obtaining a test data set, and inputting the trained space-time dynamic graph convolutional neural network model to carry out traffic flow prediction to obtain a prediction result. In order to consider the global dynamic spatial correlation, an improved attention mechanism is designed by introducing a local Moran index in geography for the first time, and a more accurate expression of the global dynamic spatial correlation is obtained by using the attention mechanism.
本发明提供一种基于时空神经网络的交通流量预测方法,涉及交通流量预测技术领域。该方法具体包括:获取交通流量数据,根据该交通流量数据通过数据预处理得到交通流量序列;搭建时空动态图卷积神经网络模型;利用交通流量序列训练时空动态图卷积神经网络模型,得到训练好的时空动态图卷积神经网络模型;获取测试数据集,并输入训练好的时空动态图卷积神经网络模型进行交通流量预测,得到预测结果。为了考虑全局的动态空间相关性,本发明方法通过首次引入地理学中的局部莫兰指数的方法设计了一个改进的注意力机制,并利用该注意力机制得到了更准确的全局动态空间相关性的表达。
Traffic flow prediction method based on space-time neural network
一种基于时空神经网络的交通流量预测方法
2024-08-02
Patent
Electronic Resource
Chinese
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