The invention belongs to the technical field of traffic flow prediction, and discloses a traffic flow prediction method and system, a storage medium, computer equipment and a terminal, traffic flow data is used as input, and information interaction of spatio-temporal data on a feature channel and a time dimension is enhanced through a feature enhancement mechanism; the enhanced data is transmitted into a down-sampling graph convolutional network, so that multi-resolution analysis of the time sequence data is realized, and a receptive field is expanded; a dynamic graph convolutional network is embedded in a down-sampling convolutional network, dynamic association generated along with time among nodes is simulated by using a generated dynamic graph structure, and hidden spatial correlation is better captured; the data processed by the down-sampling dynamic graph convolutional network are respectively transmitted into a multi-head attention network and a diffusion graph convolutional network for further extracting global time features and spatial features; and finally, inputting a result extracted by the modules to an output layer, and finally outputting a predicted value.
本发明属于交通流量预测技术领域,公开了一种交通流预测方法、系统、存储介质、计算机设备及终端,将交通流量数据作为输入,经过特征增强机制,增强了时空数据在特征通道和时间维度上的信息交互;增强后的数据将被传入下采样图卷积网络中,实现了对时间序列数据的多分辨率分析,扩大感受野其中;在下采样卷积网络中嵌入了动态图卷积网络,利用生成的动态图结构,模拟节点之间随时间生成的动态关联,更好的捕捉隐藏的空间相关性;经过下采样动态图卷积网络处理后的数据被分别传入多头注意力网络和扩散图卷积网络,用来进一步提取全局时间特征和空间特征;最后将上述模块提取到的结果输入到输出层,最后输出预测值。
Traffic flow prediction method and system, storage medium, computer equipment and terminal
交通流预测方法、系统、存储介质、计算机设备及终端
2024-07-30
Patent
Electronic Resource
Chinese
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen |
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