  Travel-time prediction constitutes a task of high importance in
transportation networks, with web mapping services like Google Maps regularly
serving vast quantities of travel time queries from users and enterprises
alike. Further, such a task requires accounting for complex spatiotemporal
interactions (modelling both the topological properties of the road network and
anticipating events -- such as rush hours -- that may occur in the future).
Hence, it is an ideal target for graph representation learning at scale. Here
we present a graph neural network estimator for estimated time of arrival (ETA)
which we have deployed in production at Google Maps. While our main
architecture consists of standard GNN building blocks, we further detail the
usage of training schedule methods such as MetaGradients in order to make our
model robust and production-ready. We also provide prescriptive studies:
ablating on various architectural decisions and training regimes, and
qualitative analyses on real-world situations where our model provides a
competitive edge. Our GNN proved powerful when deployed, significantly reducing
negative ETA outcomes in several regions compared to the previous production
baseline (40+% in cities like Sydney).
