Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting

Traffic forecasting is a particularly challenging application of spatiotemporal forecasting, due to the complicated spatial dependencies on roadway networks and the time-varying traffic patterns. To address this challenge, we learn the traffic network as a graph and propose a novel deep learning framework, Traffic Graph Convolutional Long Short-Term Memory Neural Network (TGC-LSTM), to learn the interactions between roadways in the traffic network and forecast the network-wide traffic state. We define the traffic graph convolution based on the physical network topology. The relationship between traffic graph convolution and the spectral graph convolution is also discussed. The proposed model employs L1-norms on the graph convolution weights and L2-norms on the extracted features to identify the most influential roadways in the traffic network. Experiments show that our TGC-LSTM network is able to capture the complex spatial-temporal dependencies efficiently present in a vehicle traffic network and consistently outperforms state-of-the-art baseline methods on two heterogeneous real-world traffic datasets. The visualization of graph convolution weights shows that the proposed framework can accurately recognize the most influential roadway segments in real-world traffic networks.