  Research in deep learning models to forecast traffic intensities has gained
great attention in recent years due to their capability to capture the complex
spatio-temporal relationships within the traffic data. However, most
state-of-the-art approaches have designed spatial-only (e.g. Graph Neural
Networks) and temporal-only (e.g. Recurrent Neural Networks) modules to
separately extract spatial and temporal features. However, we argue that it is
less effective to extract the complex spatio-temporal relationship with such
factorized modules. Besides, most existing works predict the traffic intensity
of a particular time interval only based on the traffic data of the previous
one hour of that day. And thereby ignores the repetitive daily/weekly pattern
that may exist in the last hour of data. Therefore, we propose a Unified
Spatio-Temporal Graph Convolution Network (USTGCN) for traffic forecasting that
performs both spatial and temporal aggregation through direct information
propagation across different timestamp nodes with the help of spectral graph
convolution on a spatio-temporal graph. Furthermore, it captures historical
daily patterns in previous days and current-day patterns in current-day traffic
data. Finally, we validate our work's effectiveness through experimental
analysis, which shows that our model USTGCN can outperform state-of-the-art
performances in three popular benchmark datasets from the Performance
Measurement System (PeMS). Moreover, the training time is reduced significantly
with our proposed USTGCN model.
