Multi-graph Spatio-temporal Graph Convolutional Network for Traffic Flow
  Prediction
Weilong Ding Tianpu Zhang Jianwu Wang Zhuofeng Zhao
Inter-city highway transportation is significant for urban life. As one of
the key functions in intelligent transportation system (ITS), traffic
evaluation always plays significant role nowadays, and daily traffic flow
prediction still faces challenges at network-wide toll stations. On the one
hand, the data imbalance in practice among various locations deteriorates the
performance of prediction. On the other hand, complex correlative
spatio-temporal factors cannot be comprehensively employed in long-term
duration. In this paper, a prediction method is proposed for daily traffic flow
in highway domain through spatio-temporal deep learning. In our method, data
normalization strategy is used to deal with data imbalance, due to long-tail
distribution of traffic flow at network-wide toll stations. And then, based on
graph convolutional network, we construct networks in distinct semantics to
capture spatio-temporal features. Beside that, meteorology and calendar
features are used by our model in the full connection stage to extra external
characteristics of traffic flow. By extensive experiments and case studies in
one Chinese provincial highway, our method shows clear improvement in
predictive accuracy than baselines and practical benefits in business.