  The IARAI Traffic4cast competitions at NeurIPS 2019 and 2020 showed that
neural networks can successfully predict future traffic conditions 1 hour into
the future on simply aggregated GPS probe data in time and space bins. We thus
reinterpreted the challenge of forecasting traffic conditions as a movie
completion task. U-Nets proved to be the winning architecture, demonstrating an
ability to extract relevant features in this complex real-world geo-spatial
process. Building on the previous competitions, Traffic4cast 2021 now focuses
on the question of model robustness and generalizability across time and space.
Moving from one city to an entirely different city, or moving from pre-COVID
times to times after COVID hit the world thus introduces a clear domain shift.
We thus, for the first time, release data featuring such domain shifts. The
competition now covers ten cities over 2 years, providing data compiled from
over 10^12 GPS probe data. Winning solutions captured traffic dynamics
sufficiently well to even cope with these complex domain shifts. Surprisingly,
this seemed to require only the previous 1h traffic dynamic history and static
road graph as input.
