Temporal Graph Convolutional Network for Urban Traffic Flow Prediction Method

Accurate and real-time traffic forecasting plays an important role in the Intelligent Traffic System (ITS) and is of great significance for urban traffic planning, traffic management, and traffic control. However, traffic forecasting has always been considered an open scientific issue, owing to the constraints of urban road network topological structure and the law of dynamic change with time, namely, spatial dependence and temporal dependence. To capture the spatial and temporal dependences simultaneously, we propose a novel neural network-based traffic forecasting method, the temporal graph convolutional network (T-GCN) model, which is in combination with the graph convolutional network (GCN) and gated recurrent unit (GRU). Specifically, the graph convolutional network is used to learn complex topological structures to capture the spatial dependence and the gated recurrent unit is used to learn dynamic changes of traffic flow to capture the temporal dependence. Then, the T-GCN model is employed to forecast traffic based on the urban road network. Experiments demonstrate that our T-GCN model can obtain the spatiotemporal correlation from traffic data and the predictions outperform state-of-art baselines on real-world traffic datasets. Our tensorflow implementation of T-GCN is available at https://github.com/lehaifeng/T-GCN.