Estimated time of arrival (ETA) prediction, also known as travel time estimation, is a fundamental task for a wide range of intelligent transportation applications, such as navigation, route planning, and ride-hailing services. To accurately predict the travel time of a route, it is essential to take into account both contextual and predictive factors, such as spatial-temporal interaction, driving behavior, and traffic congestion propagation inference. The ETA prediction models previously deployed at Baidu Maps have addressed the factors of spatial-temporal interaction (ConSTGAT) and driving behavior (SSML). In this work, we focus on modeling traffic congestion propagation patterns to improve ETA performance. Traffic congestion propagation pattern modeling is challenging, and it requires accounting for impact regions over time and cumulative effect of delay variations over time caused by traffic events on the road network. In this paper, we present a practical industrial-grade ETA prediction framework named DuETA. Specifically, we construct a congestion-sensitive graph based on the correlations of traffic patterns, and we develop a route-aware graph transformer to directly learn the long-distance correlations of the road segments. This design enables DuETA to capture the interactions between the road segment pairs that are spatially distant but highly correlated with traffic conditions. Extensive experiments are conducted on large-scale, real-world datasets collected from Baidu Maps. Experimental results show that ETA prediction can significantly benefit from the learned traffic congestion propagation patterns. In addition, DuETA has already been deployed in production at Baidu Maps, serving billions of requests every day. This demonstrates that DuETA is an industrial-grade and robust solution for large-scale ETA prediction services.

翻译：抵达预计时间(ETA)预测,也称为旅行时间估计,对于导航、路线规划和乘车服务等各种智能运输应用而言,是一系列广泛的智能运输应用,如导航、路线规划和乘车服务等,一项基本任务。为了准确预测路线的旅行时间,必须考虑到背景因素和预测因素,如空间时际互动、驾驶行为和交通拥堵传播推推推。先前在Baidu地图上部署的ETA预测模型,涉及空间时际互动(ConSTGAT)和驾驶行为(SSML)等因素。在这项工作中,我们侧重于建立交通拥堵传播模式模型,以改善ET的运行模式。交通拥堵传播模式具有挑战性,需要计及时间影响地区和时间因公路网络交通事件造成的时间延迟变化的累积影响。在本论文中,我们介绍了一个实用的工业级ETA预测框架(DuETA)。具体地说,我们根据交通流量模式的相互关系构建了一个对拥挤敏感的图表,我们开发了一个路面图变换图,以直接了解公路生产模式的长距离关联。交通模式的模型在路段上展示了甚远地段的准确路路路路段,使得DuETA能够对路路段进行大路路路路路段进行观测。