在Google Maps中使用图神经网络进行ETA预测
ETA Prediction with Graph Neural Networks in Google Maps
摘要：
旅行时间预测在交通网络中是一项非常重要的任务，谷歌地图等网络地图服务定期为用户和企业提供大量的旅行时间查询。此外，这样的任务需要考虑复杂的时空相互作用（对道路网络的拓扑特性和预测未来可能发生的事件（如高峰时间）进行建模）。因此，它是大规模图表示学习的理想目标。在这里，我们提出了一个图神经网络估计估计的到达时间（ETA），我们已经部署在谷歌地图的生产。虽然我们的主要架构由标准的GNN构建块组成，但我们进一步详细介绍了训练计划方法（如MetaQuotes）的使用，以使我们的模型具有鲁棒性和生产就绪性。我们还提供规范性研究：对各种架构决策和培训制度进行消融，并对我们的模型提供竞争优势的真实情况进行定性分析。我们的GNN在部署时被证明是强大的，与之前的生产基线相比，它在几个地区显著减少了负面的ETA结果（在悉尼等城市为40%以上）。
英文摘要：
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).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
