  Accurate traffic prediction is crucial to the guidance and management of
urban traffics. However, most of the existing traffic prediction models do not
consider the computational burden and memory space when they capture
spatial-temporal dependence among traffic data. In this work, we propose a
factorized Spatial-Temporal Tensor Graph Convolutional Network to deal with
traffic speed prediction. Traffic networks are modeled and unified into a graph
that integrates spatial and temporal information simultaneously. We further
extend graph convolution into tensor space and propose a tensor graph
convolution network to extract more discriminating features from
spatial-temporal graph data. To reduce the computational burden, we take Tucker
tensor decomposition and derive factorized a tensor convolution, which performs
separate filtering in small-scale space, time, and feature modes. Besides, we
can benefit from noise suppression of traffic data when discarding those
trivial components in the process of tensor decomposition. Extensive
experiments on two real-world traffic speed datasets demonstrate our method is
more effective than those traditional traffic prediction methods, and meantime
achieves state-of-the-art performance.
