  Traffic prediction is a fundamental and vital task in Intelligence
Transportation System (ITS), but it is very challenging to get high accuracy
while containing low computational complexity due to the spatiotemporal
characteristics of traffic flow, especially under the metropolitan
circumstances. In this work, a new topological framework, called Linkage
Network, is proposed to model the road networks and present the propagation
patterns of traffic flow. Based on the Linkage Network model, a novel online
predictor, named Graph Recurrent Neural Network (GRNN), is designed to learn
the propagation patterns in the graph. It could simultaneously predict traffic
flow for all road segments based on the information gathered from the whole
graph, which thus reduces the computational complexity significantly from O(nm)
to O(n+m), while keeping the high accuracy. Moreover, it can also predict the
variations of traffic trends. Experiments based on real-world data demonstrate
that the proposed method outperforms the existing prediction methods.
