  Traffic forecasting is a problem of intelligent transportation systems (ITS)
and crucial for individuals and public agencies. Therefore, researches pay
great attention to deal with the complex spatio-temporal dependencies of
traffic system for accurate forecasting. However, there are two challenges: 1)
Most traffic forecasting studies mainly focus on modeling correlations of
neighboring sensors and ignore correlations of remote sensors, e.g., business
districts with similar spatio-temporal patterns; 2) Prior methods which use
static adjacency matrix in graph convolutional networks (GCNs) are not enough
to reflect the dynamic spatial dependence in traffic system. Moreover,
fine-grained methods which use self-attention to model dynamic correlations of
all sensors ignore hierarchical information in road networks and have quadratic
computational complexity. In this paper, we propose a novel dynamic multi-graph
convolution recurrent network (DMGCRN) to tackle above issues, which can model
the spatial correlations of distance, the spatial correlations of structure,
and the temporal correlations simultaneously. We not only use the
distance-based graph to capture spatial information from nodes are close in
distance but also construct a novel latent graph which encoded the structure
correlations among roads to capture spatial information from nodes are similar
in structure. Furthermore, we divide the neighbors of each sensor into
coarse-grained regions, and dynamically assign different weights to each region
at different times. Meanwhile, we integrate the dynamic multi-graph convolution
network into the gated recurrent unit (GRU) to capture temporal dependence.
Extensive experiments on three real-world traffic datasets demonstrate that our
proposed algorithm outperforms state-of-the-art baselines.
