  Traffic forecasting is an important prerequisite for the application of
intelligent transportation systems in urban traffic networks. The existing
works adopted RNN and CNN/GCN, among which GCRN is the state of art work, to
characterize the temporal and spatial correlation of traffic flows. However, it
is hard to apply GCRN to the large scale road networks due to high
computational complexity. To address this problem, we propose to abstract the
road network into a geometric graph and build a Fast Graph Convolution
Recurrent Neural Network (FastGCRNN) to model the spatial-temporal dependencies
of traffic flow. Specifically, We use FastGCN unit to efficiently capture the
topological relationship between the roads and the surrounding roads in the
graph with reducing the computational complexity through importance sampling,
combine GRU unit to capture the temporal dependency of traffic flow, and embed
the spatiotemporal features into Seq2Seq based on the Encoder-Decoder
framework. Experiments on large-scale traffic data sets illustrate that the
proposed method can greatly reduce computational complexity and memory
consumption while maintaining relatively high accuracy.
