  Because of increased urban complexity and growing populations, more and more
challenges about predicting city-wide mobility behavior are being organized.
Traffic Map Movie Forecasting Challenge 2020 is secondly held in the
competition track of the Thirty-fourth Conference on Neural Information
Processing Systems (NeurIPS). Similar to Traffic4Cast 2019, the task is to
predict traffic flow volume, average speed in major directions on the
geographical area of three big cities: Berlin, Istanbul, and Moscow. In this
paper, we apply the attention mechanism on U-Net based model, especially we add
an attention gate on the skip-connection between contraction path and expansion
path. An attention gates filter features from the contraction path before
combining with features on the expansion path, it enables our model to reduce
the effect of non-traffic region features and focus more on crucial region
features. In addition to the competition data, we also propose two extra
features which often affect traffic flow, that are time and weekdays. We
experiment with our model on the competition dataset and reproduce the winner
solution in the same environment. Overall, our model archives better
performance than recent methods.
