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