  As an important part of intelligent transportation systems, traffic
forecasting has attracted tremendous attention from academia and industry.
Despite a lot of methods being proposed for traffic forecasting, it is still
difficult to model complex spatial-temporal dependency. Temporal dependency
includes short-term dependency and long-term dependency, and the latter is
often overlooked. Spatial dependency can be divided into two parts:
distance-based spatial dependency and hidden spatial dependency. To model
complex spatial-temporal dependency, we propose a novel framework for traffic
forecasting, named Spatial-Temporal Graph Convolutional Gated Recurrent Network
(STGCGRN). We design an attention module to capture long-term dependency by
mining periodic information in traffic data. We propose a Double Graph
Convolution Gated Recurrent Unit (DGCGRU) to capture spatial dependency, which
integrates graph convolutional network and GRU. The graph convolution part
models distance-based spatial dependency with the distance-based predefined
adjacency matrix and hidden spatial dependency with the self-adaptive adjacency
matrix, respectively. Specially, we employ the multi-head mechanism to capture
multiple hidden dependencies. In addition, the periodic pattern of each
prediction node may be different, which is often ignored, resulting in mutual
interference of periodic information among nodes when modeling spatial
dependency. For this, we explore the architecture of model and improve the
performance. Experiments on four datasets demonstrate the superior performance
of our model.
