  Traffic prediction is the cornerstone of an intelligent transportation
system. Accurate traffic forecasting is essential for the applications of smart
cities, i.e., intelligent traffic management and urban planning. Although
various methods are proposed for spatio-temporal modeling, they ignore the
dynamic characteristics of correlations among locations on road networks.
Meanwhile, most Recurrent Neural Network (RNN) based works are not efficient
enough due to their recurrent operations. Additionally, there is a severe lack
of fair comparison among different methods on the same datasets. To address the
above challenges, in this paper, we propose a novel traffic prediction
framework, named Dynamic Graph Convolutional Recurrent Network (DGCRN). In
DGCRN, hyper-networks are designed to leverage and extract dynamic
characteristics from node attributes, while the parameters of dynamic filters
are generated at each time step. We filter the node embeddings and then use
them to generate a dynamic graph, which is integrated with a pre-defined static
graph. As far as we know, we are the first to employ a generation method to
model fine topology of dynamic graph at each time step. Further, to enhance
efficiency and performance, we employ a training strategy for DGCRN by
restricting the iteration number of decoder during forward and backward
propagation. Finally, a reproducible standardized benchmark and a brand new
representative traffic dataset are opened for fair comparison and further
research. Extensive experiments on three datasets demonstrate that our model
outperforms 15 baselines consistently.
