  We propose and study a data-driven framework for identifying traffic
congestion functions (numerical relationships between observations of
macroscopic traffic variables) at global scale and segment-level granularity.
In contrast to methods that estimate a separate set of parameters for each
roadway, ours learns a single black-box function over all roadways in a
metropolitan area. First, we pool traffic data from all segments into one
dataset, combining static attributes with dynamic time-dependent features.
Second, we train a feed-forward neural network on this dataset, which we can
then use on any segment in the area. We evaluate how well our framework
identifies congestion functions on observed segments and how it generalizes to
unobserved segments and predicts segment attributes on a large dataset covering
multiple cities worldwide. For identification error on observed segments, our
single data-driven congestion function compares favorably to segment-specific
model-based functions on highway roads, but has room to improve on arterial
roads. For generalization, our approach shows strong performance across cities
and road types: both on unobserved segments in the same city and on zero-shot
transfer learning between cities. Finally, for predicting segment attributes,
we find that our approach can approximate critical densities for individual
segments using their static properties.
