  The construction of spatiotemporal networks using graph convolution networks
(GCNs) has become one of the most popular methods for predicting traffic
signals. However, when using a GCN for traffic speed prediction, the
conventional approach generally assumes the relationship between the sensors as
a homogeneous graph and learns an adjacency matrix using the data accumulated
by the sensors. However, the spatial correlation between sensors is not
specified as one but defined differently from various viewpoints. To this end,
we aim to study the heterogeneous characteristics inherent in traffic signal
data to learn the hidden relationships between sensors in various ways.
Specifically, we designed a method to construct a heterogeneous graph for each
module by dividing the spatial relationship between sensors into static and
dynamic modules. We propose a network decentralization attention based
heterogeneity-aware graph convolution network (HAGCN) method that aggregates
the hidden states of adjacent nodes by considering the importance of each
channel in a heterogeneous graph. Experimental results on real traffic datasets
verified the effectiveness of the proposed method, achieving a 6.35%
improvement over the existing model and realizing state-of-the-art prediction
performance.
