The embodiment of the invention provides a traffic flow prediction method and device, equipment and a storage medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring traffic flow data of a plurality of position nodes to generate an initial flow matrix, carrying out data optimization on the initial flow matrix to obtain an input flow matrix, inputting the input flow matrix into a correlation prediction sub-model for correlation analysis to obtain a first output matrix, and outputting the first output matrix to a second output sub-model; performing data processing on the input traffic matrix by using the gated convolutional sub-networks to obtain corresponding first gated data, obtaining second gated data according to the two pieces of first gated data, inputting the second gated data and the input traffic matrix into the graph convolutional network together for data processing to obtain a first output matrix, and outputting the first output matrix to the graph convolutional network; and inputting the first output matrix into an output prediction layer for data processing to obtain a target traffic matrix corresponding to the position node. Prediction is carried out based on the bidirectional spatial correlation and the temporal correlation obtained by the traffic flow prediction model, and the accuracy of traffic flow prediction is improved.
本申请实施例提供交通流量预测方法、装置、设备和存储介质,涉及数据处理技术领域。该方法获取多个位置节点的交通流量数据生成始发流量矩阵,并对始发流量矩阵进行数据优化得到输入流量矩阵,将输入流量矩阵输入相关性预测子模型进行相关性分析,得到第一输出矩阵,分别利用门控卷积子网络对输入流量矩阵进行数据处理,得到对应的第一门控数据,根据两个第一门控数据得到第二门控数据,将第二门控数据和输入流量矩阵共同输入图卷积网络进行数据处理得到第一输出矩阵,将第一输出矩阵输入输出预测层进行数据处理,得到位置节点对应的目的流量矩阵。基于交通流量预测模型获取的双向空间相关性和时间相关性进行预测,提高交通流量预测的准确性。
Traffic flow prediction method and device, equipment and storage medium
交通流量预测方法、装置、设备和存储介质
2025-03-11
Patent
Electronic Resource
Chinese
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