  This technical report presents a solution for the 2020 Traffic4Cast
Challenge. We consider the traffic forecasting problem as a future frame
prediction task with relatively weak temporal dependencies (might be due to
stochastic urban traffic dynamics) and strong prior knowledge, \textit{i.e.},
the roadmaps of the cities. For these reasons, we use the U-Net as the backbone
model, and we propose a roadmap generation method to make the predicted traffic
flows more rational. Meanwhile, we use a fine-tuning strategy based on the
validation set to prevent overfitting, which effectively improves the
prediction results. At the end of this report, we further discuss several
approaches that we have considered or could be explored in future work: (1)
harnessing inherent data patterns, such as seasonality; (2) distilling and
transferring common knowledge between different cities. We also analyze the
validity of the evaluation metric.
