基于图神经网络的交通流量预测方法研究

Research on Traffic Flow Prediction Method Based on Graph Neural Network

文章提出了一种新型的交通运输流预测方法。首先,提出了一个基于时间空间特征的交通流预测模型,其中道路交通流的空间特性的提取是使用图形卷积网络(GCN)进行的。其次,通过一个简单而强大的可变触发周期单元GRU来实现一种随时序改变的道路网络;在此基础上,利用sequence-to-sequence模型对道路各个阶段的时间序列进行了评估,并利用该模式来获取最终的预测结果。最后,以sequence-to-sequence模型为基础,采用了自动编码机制对模型进行了结构优化,使预报准确率有了很大提高。

In this paper,a new method for traffic flow prediction is proposed.First,a traffic flow prediction model based on spatial-temporal features is proposed.Here,the extraction of spatial features of road traffic flows is implemented using a graphical convolutional network(GCN).In addition,a simple but powerful variable trigger period unit GRU is used to implement a timevarying road network.Based on this,a sequence-to-sequence model is used to predict multiple phases of the sequence and the final predictions are obtained from this model.Finally,on the basis of the sequence-to-sequence model,the structure of the model is optimized using an automatic coding mechanism,which significantly improves the accuracy of the predictions.