The invention discloses a traffic flow prediction method based on a time-space synchronization embedded graph Transform model. The method comprises the following steps: acquiring historical traffic flow sequence data of a target area and preprocessing the historical traffic flow sequence data; defining a traffic network of a target area as an undirected graph, calculating a static adjacent matrix based on a graph structure, and obtaining spatial coding features through a full connection layer; extracting original traffic features through a full connection layer based on the preprocessed data; embedding the characteristic representations of the week cycle, the day cycle and the timestamp through a full connection layer to obtain week cycle, day cycle and timestamp characteristics; based on a space-time adaptive embedding mechanism, obtaining adaptive space-time features; hidden space-time representation is obtained through feature splicing; extracting space-time dependence characteristics of traffic flow along a time dimension and a space dimension through a double-channel attention mechanism to obtain enhanced space-time characteristic representation; and generating a traffic flow predicted value of each node in a future time period through a full-connection output layer. According to the invention, the traffic flow prediction precision is improved.
本发明公开了一种基于时空同步嵌入图Transformer模型的交通流预测方法,该方法包括:获取目标区域的历史交通流量序列数据并预处理;将目标区域的交通路网定义为无向图,基于图结构计算静态邻接矩阵,再通过全连接层得到空间编码特征;基于预处理后的数据,通过全连接层提取原始流量特征;通过全连接层将周周期、日周期和时间戳的特征表示进行嵌入,得到周周期、日周期和时间戳特征;基于时空自适应嵌入机制,得到自适应时空特征;通过特征拼接获得隐藏的时空表示;通过双通道注意力机制分别沿时间维度和空间维度提取交通流量的时空依赖特征,得到增强的时空特征表示;通过全连接输出层生成未来时段各节点的交通流量预测值。本发明提高了交通流预测的精度。
Traffic flow prediction method based on time-space synchronization embedded graph Transform model
基于时空同步嵌入图Transformer模型的交通流预测方法
2025-05-30
Patent
Electronic Resource
Chinese
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