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).

本文介绍了一种用于运输网络中估算出发时间的图神经网络模型，该模型使用了 MetaGradients 等算法优化方法，经过实际生产和现实情景分析的验证，在 Sydney 等城市的应用中相较之前的基础模型获得了显著的预测值准确度提高（最高达 40%）。