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Traffic forecasting is a particularly challenging application ofspatiotemporal forecasting, due to the time-varying traffic patterns and thecomplicated spatial dependencies on road networks. To address this challenge,we learn the traffic network as a graph and propose a novel deep learningframework, Traffic Graph Convolutional Long Short-Term Memory Neural Network(TGC-LSTM), to learn the interactions between roadways in the traffic networkand forecast the network-wide traffic state. We define the traffic graphconvolution based on the physical network topology. The relationship betweenthe proposed traffic graph convolution and the spectral graph convolution isalso discussed. An L1-norm on graph convolution weights and an L2-norm on graphconvolution features are added to the model's loss function to enhance theinterpretability of the proposed model. Experimental results show that theproposed model outperforms baseline methods on two real-world traffic statedatasets. The visualization of the graph convolution weights indicates that theproposed framework can recognize the most influential road segments inreal-world traffic networks.

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