The invention provides a traffic flow prediction method and device based on a space-time attention map neural network. The space-time attention map neural network comprises a space-time feature learning layer, a pooling layer and a prediction layer. Performing space-time two-dimensional information fusion learning on the fine-grained traffic data of each intersection in the road network in the preset time period by using the road network spatial topological structure diagram and the space-time feature learning layer to obtain the fine-grained traffic features of each intersection in the target path in the preset time period; pooling the fine-grained traffic characteristics according to the intersection connection condition and pooling layer of the target path to obtain coarse-grained traffic characteristics of the target path in a preset time period; and predicting the traffic of the target path in the target time period based on the prediction layer, the coarse-grained traffic features and the coarse-grained traffic data of the target path in the preset time period. According to the method, the coarse-grained path traffic is accurately predicted by utilizing the fine-grained intersection traffic and the space-time attention map neural network capable of capturing the traffic space-time association rules of different intersections and different time periods.
本发明提供一种基于时空注意力图神经网络的交通流量预测方法和装置,时空注意力图神经网络包含时空特征学习层、池化层和预测层;利用路网空间拓扑结构图和时空特征学习层,对路网中各路口在预设时段内的细粒度流量数据进行时空双维度的信息融合学习,得到目标路径中各路口在预设时段内的细粒度流量特征;根据目标路径的路口连接情况和池化层,池化处理细粒度流量特征,得到目标路径在预设时段内的粗粒度流量特征;基于预测层、粗粒度流量特征和目标路径在预设时段内的粗粒度流量数据,预测目标路径在目标时段的流量。本发明利用细粒度路口流量以及可捕捉不同路口、不同时段的流量时空关联规律的时空注意力图神经网络,精准预测粗粒度路径流量。
Traffic flow prediction method and device based on space-time attention graph neural network
基于时空注意力图神经网络的交通流量预测方法和装置
2024-02-20
Patent
Elektronische Ressource
Chinesisch
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