Physics-informed deep learning with Kalman filter mixture for traffic state prediction
Accurate traffic forecasting is crucial for understanding and managing congestion for efficient transportation planning. However, conventional approaches often neglect epistemic uncertainty, which arises from incomplete knowledge across different spatiotemporal scales. This study addresses this chal...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2025-03-01
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| Series: | International Journal of Transportation Science and Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2046043024000376 |
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