  We describe the submission of the Quo Vadis team to the Traffic4cast
competition, which was organized as part of the NeurIPS 2019 series of
challenges. Our system consists of a temporal regression module, implemented as
$1\times1$ 2d convolutions, augmented with spatio-temporal biases. We have
found that using biases is a straightforward and efficient way to include
seasonal patterns and to improve the performance of the temporal regression
model. Our implementation obtains a mean squared error of $9.47\times 10^{-3}$
on the test data, placing us on the eight place team-wise. We also present our
attempts at incorporating spatial correlations into the model; however,
contrary to our expectations, adding this type of auxiliary information did not
benefit the main system. Our code is available at
https://github.com/danoneata/traffic4cast.
