The invention discloses a traffic flow prediction method, apparatus and device, and a storage medium. The method comprises the steps of obtaining historical traffic flow of a to-be-predicted traffic scene collected by a sensor; inputting the historical traffic flow of the to-be-predicted traffic scene into a preset traffic flow prediction model, so that the traffic flow prediction model obtains the future traffic flow of the to-be-predicted traffic scene according to the historical traffic flow of the to-be-predicted traffic scene; wherein the traffic flow prediction model is composed of a data layer, a plurality of layers of graph convolutional network blocks and a prediction layer, and the graph convolutional network blocks learn specific parameter modes of all nodes through input data based on time attention and space attention mechanisms; and the adaptive correlation of the edge is captured through an adaptive self-attention mechanism. By implementing the method, the potential spatial-temporal correlation in the road network can be captured, and the error during traffic flow prediction is reduced.
本发明公开了一种交通流流量预测方法、装置、设备及存储介质,所述方法包括:获取传感器采集的待预测交通场景的历史交通流流量;将所述待预测交通场景的历史交通流流量输入至预设的交通流预测模型中,以使所述交通流预测模型根据所述待预测交通场景的历史交通流流量,获得待预测交通场景的未来交通流流量;其中,所述交通流预测模型由数据层、若干层级的图卷积网络块以及预测层构成,所述图卷积网络块基于时间注意力、空间注意力机制,通过输入数据来学习所有节点的特定参数模式,并通过自适应的自注意力机制来捕捉边缘的自适应相关性。通过实施本发明,可以捕获路网中潜在的时空相关性,降低交通流预测时的误差。
Traffic flow prediction method and device, equipment and storage medium
一种交通流流量预测方法、装置、设备及存储介质
2024-05-03
Patent
Elektronische Ressource
Chinesisch
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