面向时空交通栅格流量预测的3D通道注意力网络

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。

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

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.