基于周期图卷积与多头注意力GRU组合的交通流量预测模型

为了捕获交通流量数据中复杂的时空动态变化关系以及周期性变化的特征，同时避免道路突发情况引起的误差累计效应，提出一种基于周期图卷积(periodic graph convolution network, PGCN)与多头注意力门控循环单元(multi-head attention gated recurrent unit, MAGRU)组合的交通流量预测模型。首先，模型的时空数据融合模块利用交通流量的周期相似性构建周期图，同时将空间和时间编码信息添加至交通流量序列数据；然后在时空特征提取模块中，GCN子模块捕获周期特征图中的空间特征，MAGRU子模块捕获序列数据中的时间特征；最后通过门控融合机制将两者提取的时空特征进行融合。模型在两个真实的交通流量数据集上进行了实验。结果表明，该模型相较于多个最新基准模型，在MAE、RMSE、MAPE三个预测误差指标上平均降低了5.4%、22.8%、10.3%,R2精确度指标平均提高了11.6%。说明模型在预测精度方面有显著的改进，并能有效减少误差累积效应。

Traffic flow prediction model based on combining periodic graph convolution network and multi-head attention GRU

To capture the complex spatial-temporal dynamics and periodic patterns in traffic flow data, and reduce the cumulative error effects caused by unexpected road conditions, this paper proposed a traffic flow prediction model based on combining PGCN and MAGRU. Firstly, the spatial-temporal data fusion module constructed periodic graphs using the property of periodic similarity in traffic flow data, and added spatial and temporal encoding information into the sequence data. Then, in the spatial-temporal feature extraction module, graph convolutional network(GCN) submodule captured spatial features from the periodic feature graphs, MAGRU submodule captured temporal features from the sequence data. Finally, the gated fusion mechanism fused the features extracted by both modules. It conducted the experiment on two real traffic flow datasets, the results indicate that compared to several recent baseline models, the model achieves average reduction of 5.4%, 22.8%, 10.3% in MAE, RMSE and MAPE, exhibites an average improvement of 11.6% in R2 accuracy metric. It confirms that the model can provide more accurate predictions and reduce cumulative error effects.