  The problem of the effective prediction for large-scale spatio-temporal
traffic data has long haunted researchers in the field of intelligent
transportation. Limited by the quantity of data, citywide traffic state
prediction was seldom achieved. Hence the complex urban transportation system
of an entire city cannot be truly understood. Thanks to the efforts of
organizations like IARAI, the massive open data provided by them has made the
research possible. In our 2020 Competition solution, we further design multiple
variants based on HR-NET and UNet. Through feature engineering, the
hand-crafted features are input into the model in a form of channels. It is
worth noting that, to learn the inherent attributes of geographical locations,
we proposed a novel method called geo-embedding, which contributes to
significant improvement in the accuracy of the model. In addition, we explored
the influence of the selection of activation functions and optimizers, as well
as tricks during model training on the model performance. In terms of
prediction accuracy, our solution has won 2nd place in NeurIPS 2020,
Traffic4cast Challenge.
