  We propose an automated method to estimate a road segment's free-flow speed
from overhead imagery and road metadata. The free-flow speed of a road segment
is the average observed vehicle speed in ideal conditions, without congestion
or adverse weather. Standard practice for estimating free-flow speeds depends
on several road attributes, including grade, curve, and width of the right of
way. Unfortunately, many of these fine-grained labels are not always readily
available and are costly to manually annotate. To compensate, our model uses a
small, easy to obtain subset of road features along with aerial imagery to
directly estimate free-flow speed with a deep convolutional neural network
(CNN). We evaluate our approach on a large dataset, and demonstrate that using
imagery alone performs nearly as well as the road features and that the
combination of imagery with road features leads to the highest accuracy.
