ETA Prediction with Graph Neural Networks in Google Maps

TL;DR

This paper introduces a graph neural network model for ETA prediction in Google Maps, effectively capturing complex spatiotemporal interactions and improving accuracy in real-world deployment.

Contribution

The paper presents a scalable GNN-based ETA estimator with advanced training techniques, deployed in production at Google Maps, and demonstrates significant performance improvements.

Findings

Reduced negative ETA outcomes by over 40% in some cities

Effective modeling of complex spatiotemporal interactions

Deployment in real-world Google Maps system

Abstract

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).