The invention discloses an expressway traffic situation prediction method based on TCN-GCN, and the method comprises the steps: constructing a TCN-GCN model with a time feature extraction function and a spatial feature extraction function based on a time convolution network TCN and a graph convolution neural network GCN, and obtaining the historical traffic flow data of a to-be-predicted expressway section on a whole portal; the method comprises the following steps: acquiring historical traffic flow data, performing abnormal value elimination and normalization processing on the historical traffic flow data, constructing a training set Xinput and a corresponding label set Yout by adopting the processed historical traffic flow data, training a TCN-GCN model by adopting the training set Xinput and the label set Yout to obtain a trained TCN-GCN model, and then acquiring traffic flow data of a highway section to be predicted in real time. A TCN-GCN model is adopted for prediction, and a traffic situation prediction result of the highway section to be predicted is obtained; the method has the advantages of being simple in prediction process, small in calculation amount and high in prediction precision.
本发明公开了一种基于TCN‑GCN的高速公路交通态势预测方法,基于时间卷积网络TCN和图卷积神经网络GCN构建具有时间特征提取功能和空间特征提取功能的TCN‑GCN模型,获取待预测高速公路路段的全部门架上的历史交通流数据,并对历史交通流数据进行异常值排除和归一化处理,采用处理后的历史交通流数据构建训练集Xinput和对应的标签集Yout,采用训练集Xinput和标签集Yout对TCN‑GCN模型进行训练,得到训练后的TCN‑GCN模型,然后实时获取待预测高速公路路段的交通流数据,采用TCN‑GCN模型进行预测,得到待预测高速公路路段的交通态势预测结果;优点是预测过程简单,计算量小,且具有较高的预测精度。
Expressway traffic situation prediction method based on TCN-GCN
基于TCN-GCN的高速公路交通态势预测方法
2025-05-30
Patent
Electronic Resource
Chinese
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