Abstract: Traffic is said to follow `hidden rules' that can be transferred across domain shifts. Our competition sets out to explore this meta topic with two few-shot learning tasks: predictions across a temporal shift brought about by COVID-19 and across a spatio-temporal shift in hitherto unseen cities. We provide an unprecedented, large data set from $10^{12}$ real world GPS probes in $10$ cities binned in space and time into multi-channel movie frames, as well as static data on the basic road connections of the underlying road network. Thus participants can approach these transfer tasks using graph based approaches encoding knowledge about the road network or approaches from computer vision like U-nets, which were highly successful in our previous competitions. Any advance in these questions will have a large impact on smart city planning, on mobility in general and thus, ultimately, our way of living more sustainably.