Can I read DuETA: Traffic Congestion Propagation Pattern Modeling via Efficient Graph Learning for ETA Prediction at Baidu Maps on EtoBox?

DuETA: Traffic Congestion Propagation Pattern Modeling via Efficient Graph Learning for ETA Prediction at Baidu Maps by Jizhou Huang; Zhengjie Huang; Xiaomin Fang; Shikun Feng; Xuyi Chen; Jiaxiang Liu; Haitao Yuan; Haifeng Wang is a scholarly article available to read on EtoBox.

What is DuETA: Traffic Congestion Propagation Pattern Modeling via Efficient Graph Learning for ETA Prediction at Baidu Maps about?

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 [7]) and driving behavior (SSML [6]). In this work, we believe that modeling traffic congestion propagation patterns is of great importance toward accurately performing ETA prediction, and we focus on this factor 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

Author

Jizhou Huang; Zhengjie Huang; Xiaomin Fang; Shikun Feng; Xuyi Chen; Jiaxiang Liu; Haitao Yuan; Haifeng Wang

Publisher

ACM

Published

2022

Language

EN