面向时空交通栅格流量预测的3D通道注意力网络

3D channel-wise attention network for spatio-temporal traffic raster flow prediction

城市交通流量预测对交通管理和公共安全具有重要意义。然而,交通栅格流量数据中的规律在时刻变化,在城市中存在全局范围的时空间关系,并且不同特征通道在每个城市区域上有不同的重要性。为解决这些挑战并做出更准确的预测,设计了一种新颖的时空神经网络模型--3D通道注意力网络(three-dimensional channel-wise attention networks,3D-CANet)。提出一个3D通道内注意力(three-dimensional inner channel attention,3D-InnerCA)单元来动态捕获各个通道中不同的全局时空相关性,同时设计通道间注意力(inter channel attention,InterCA)单元来自适应地重校准每个区域上不同特征通道的贡献。在3个真实交通栅格流量数据集上的实验结果表明,3D-CANet模型的预测能力优于其他对比方法,证明了模型的有效性。

Urban traffic flow forecasting is of great significance for traffic management and public safety.However,the correlations of traffic raster flow change with time.There are global spatio-temporal correlations in the city,and the contributions of channel-wise features vary on each city region.To tackle these challenges and make more accurate prediction,a novel spatio-temporal neural network model,named 3D-CANet(three-dimensional channel-wise attention network),was designed.A 3D-InnerCA(three-dimensional inner-channel attention)unit was proposed to dynamically capture the global spatio-temporal correlations for different channel-wise features.Meanwhile,an InterCA(inter-channel attention)unit was designed to adaptively recalibrate the contributions of different channel-wise features on each region.The experimental results on three real-world traffic raster flow datasets demonstrate that the predictive performance of the 3D-CANet model was better than the others,which proved the validity of the model proposed.