The prediction of regional traffic flows is important for traffic control and management in an intelligent traffic system. With the help of deep neural networks, the convolutional neural network or residual neural network, which can be applied only to regular grids, is adopted to capture the spatial dependence for flow prediction. However, the obtained regions are always irregular considering the road network and administrative boundaries; thus, dividing the city into grids is inaccurate for prediction. In this paper, we propose a new model based on multi-graph convolutional network and gated recurrent unit (MGCN-GRU) to predict traffic flows for irregular regions. Specifically, we first construct heterogeneous inter-region graphs for a city to reflect the relationships among regions. In each graph, nodes represent the irregular regions and edges represent the relationship types between regions. Then, we propose a multi-graph convolutional network to fuse different inter-region graphs and additional attributes. The GRU is further used to capture the temporal dependence and to predict future traffic flows. Experimental results based on three real-world large-scale datasets (public bicycle system dataset, taxi dataset, and dockless bike-sharing dataset) show that our MGCN-GRU model outperforms a variety of existing methods.

区域交通流量预测对于智能交通系统中的交通控制和管理具有重要意义。在深度神经网络的帮助下，采用只能应用于规则网格的卷积神经网络或残差神经网络来捕获流量预测的空间依赖性。然而，考虑到道路网络和行政边界，获得的区域总是不规则的；因此，将城市划分为网格对于预测是不准确的。在本文中，我们提出了一种基于多图卷积网络和门控循环单元（MGCN-GRU）的新模型来预测不规则区域的交通流量。具体来说，我们首先为一个城市构建异构区域间图来反映区域之间的关系。在每张图中，节点代表不规则区域，边代表区域之间的关系类型。然后，我们提出了一个多图卷积网络来融合不同的区域间图和附加属性。GRU 进一步用于捕获时间依赖性并预测未来的交通流量。基于三个真实世界的大规模数据集（公共自行车系统数据集、出租车数据集和无桩共享单车数据集）的实验结果表明，我们的 MGCN-GRU 模型优于各种现有方法。