  Over the last years, the transportation community has witnessed a tremendous
amount of research contributions on new deep learning approaches for
spatio-temporal forecasting. These contributions tend to emphasize the modeling
of spatial correlations, while neglecting the fairly stable and recurrent
nature of human mobility patterns. In this short paper, we show that a naive
baseline method based on the average weekly pattern and linear regression can
achieve comparable results to many state-of-the-art deep learning approaches
for spatio-temporal forecasting in transportation, or even outperform them on
several datasets, thus contrasting the importance of stationarity and recurrent
patterns in the data with the importance of spatial correlations. Furthermore,
we establish 9 different reference benchmarks that can be used to compare new
approaches for spatio-temporal forecasting, and provide a discussion on best
practices and the direction that the field is taking.
