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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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
Maximum Academic Press
2024-12-01
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| Series: | Digital Transportation and Safety |
| Subjects: | |
| Online Access: | https://www.maxapress.com/article/doi/10.48130/dts-0024-0021 |
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