Network level spatial temporal traffic forecasting with Hierarchical-Attention-LSTM

Network level spatial temporal traffic forecasting with Hierarchical-Attention-LSTM

Traffic state data, such as speed, density, volume, and travel time collected from ubiquitous roadway detectors require advanced network level analytics for forecasting and identifying significant traffic patterns. This paper leverages diverse traffic state datasets from the Caltrans Performance Mea...

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Bibliographic Details
Main Author:	Tianya Zhang
Format:	Article
Language:	English
Published:	Maximum Academic Press 2024-12-01
Series:	Digital Transportation and Safety
Subjects:	long short-term memory traffic forecasting hierarchical feature learning attention pooling spatio-temporal modeling
Online Access:	https://www.maxapress.com/article/doi/10.48130/dts-0024-0021
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