Accurately forecasting traffic flows is critically important to many real applications including public safety and intelligent transportation systems. The challenges of this problem include both the dynamic mobility patterns of the people and the complex spatial-temporal correlations of the urban traffic data. Meanwhile, most existing models ignore the diverse impacts of the various traffic observations (e.g. vehicle speed and road occupancy) on the traffic flow prediction, and different traffic observations can be considered as different channels of input features. We argue that the analysis in multiple-channel traffic observations might help to better address this problem. In this paper, we study the novel problem of multi-channel traffic flow prediction, and propose a deep \underline{M}ulti-\underline{V}iew \underline{C}hannel-wise \underline{S}patio-\underline{T}emporal \underline{Net}work (MVC-STNet) model to effectively address it. Specifically, we first construct the localized and globalized spatial graph where the multi-view fusion module is used to effectively extract the local and global spatial dependencies. Then LSTM is used to learn the temporal correlations. To effectively model the different impacts of various traffic observations on traffic flow prediction, a channel-wise graph convolutional network is also designed. Extensive experiments are conducted over the PEMS04 and PEMS08 datasets. The results demonstrate that the proposed MVC-STNet outperforms state-of-the-art methods by a large margin.

翻译：准确预测交通流对公共安全、智能交通系统等众多实际应用至关重要。该问题的挑战既包括人群动态移动模式，也包括城市交通数据复杂的时空相关性。同时，现有模型大多忽略了不同交通观测数据（如车辆速度和道路占用率）对交通流预测的差异化影响，而不同交通观测数据可视为输入特征的不同通道。我们认为，多通道交通观测分析可能有助于更好地解决这一问题。本文研究多通道交通流预测这一新问题，提出了一种深度多视角通道时空网络（MVC-STNet）模型进行有效处理。具体而言，我们首先构建局部化和全局化空间图，通过多视角融合模块有效提取局部和全局空间依赖关系；随后利用LSTM学习时间相关性。为有效建模不同交通观测数据对交通流预测的差异化影响，还设计了通道图卷积网络。基于PEMS04和PEMS08数据集的大量实验表明，提出的MVC-STNet模型显著优于现有最优方法。