  Our goal is to use overhead imagery to understand patterns in traffic flow,
for instance answering questions such as how fast could you traverse Times
Square at 3am on a Sunday. A traditional approach for solving this problem
would be to model the speed of each road segment as a function of time.
However, this strategy is limited in that a significant amount of data must
first be collected before a model can be used and it fails to generalize to new
areas. Instead, we propose an automatic approach for generating dynamic maps of
traffic speeds using convolutional neural networks. Our method operates on
overhead imagery, is conditioned on location and time, and outputs a local
motion model that captures likely directions of travel and corresponding travel
speeds. To train our model, we take advantage of historical traffic data
collected from New York City. Experimental results demonstrate that our method
can be applied to generate accurate city-scale traffic models.
