  Accurate traffic prediction is a key ingredient to enable traffic management
like rerouting cars to reduce road congestion or regulating traffic via dynamic
speed limits to maintain a steady flow. A way to represent traffic data is in
the form of temporally changing heatmaps visualizing attributes of traffic,
such as speed and volume. In recent works, U-Net models have shown SOTA
performance on traffic forecasting from heatmaps. We propose to combine the
U-Net architecture with graph layers which improves spatial generalization to
unseen road networks compared to a Vanilla U-Net. In particular, we specialize
existing graph operations to be sensitive to geographical topology and
generalize pooling and upsampling operations to be applicable to graphs.
