基于多时间尺度时空图网络的交通流量预测模型

Multi-time Scale Spatial-Temporal Graph Neural Network for Traffic Flow Prediction

交通流预测在智能交通系统的建设中起着关键作用。但由于其复杂的时空依赖性和本身的不确定性使得研究变得极具挑战性。现有的一些方法主要是将单一的时间序列输入到循环神经网络以捕获时间依赖性,而且多数模型仅对时间模块和空间模块进行简单的堆叠,导致不能有效地融合时间和空间特征。为了解决以上问题,文中提出了一个多时间尺度时空图网络模型。模型先将序列数据划分为3种时间尺度序列,然后将序列输入到时空块(ST-Block)中提取数据的时空依赖性,最后进行预测。在时空块中使用图卷积网络和变体Transformer分别捕获数据中的时间和空间依赖性,并通过门控融合机制将两者提取到的特征进行融合。在两个真实的数据集上分别进行了短期和长期的预测实验,结果表明了MTSTGNN模型在交通流预测任务上的优秀性能。

Traffic flow prediction plays a key role in intelligent transportation system.However,due to its complex spatial-temporal dependence and its uncertainty,research becomes extremely challenging.Some existing methods mainly use a single time series and input it into a recurrent neural network to capture temporal dependency.Moreover,most models only simply stack temporal modules and spatial modules,resulting in ineffective feature fusion.To address these issues,this paper proposes a multi-time scale spatial-temporal graph neural network model.The model divides the sequence data into three time-scale sequences,then puts sequence data into the ST-Blocks to extract the spatial-temporal features of data,and finally makes the prediction.In ST-Block,graph convolutional network and variant Transformer are used to capture spatial dependency and temporal dependency respectively,and the output feature of the two sub-modules are fused through a gate mechanism.A large number of experiments are conducted on two real data sets in this paper,including short-term and long-term prediction,and the results show the excellent performance of MTSTGNN model on the task of traffic flow prediction.