ETA Prediction with Graph Neural Networks in Google Maps

By Austin Derrow-Pinion, Jennifer She, David Wong, Oliver Lange, Todd Hester, L. Pérez, Marc Nunkesser, Seongjae Lee, Xueying Guo, Brett Wiltshire and 7 more. International Conference on Information and Knowledge Management, 2021

295 citations recorded by the source corpus.

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).

Subfields: Building and Construction, Transportation, Signal Processing

View Paper: https://www.semanticscholar.org/paper/5822490cf59df7f7ccb92b8901f244850b867a66

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Citations in the corpus

Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey (2023, background): “So far, large technology companies such as Baidu [47], [48], Google [49], and DiDi [50] havedevelopedpractical travel timepredictionfunctions ontheir online platforms.”
Benchmarking Graph Neural Networks (2023, background): “, 2019), transportation (Derrow-Pinion et al., 2021), knowledge graphs (Schlichtkrull et al.”
Large-Scale Representation Learning on Graphs via Bootstrapping (2021, unknown): “Graphs provide a powerful abstraction for complex datasets that arise in a variety of applications such as social networks, transportation networks, and biological sciences (Hamilton et al., 2017; Derrow-Pinion et al., 2021; Zitnik & Leskovec, 2017; Chanussot et al., 2021).”
Everything is Connected: Graph Neural Networks (2023, background): “, 2020), serve estimated travel times in Google Maps (Derrow-Pinion et al., 2021), power content recommendations in Pinterest (Ying et al.”