| Autor: |
Archie J. Huang, Shaurya Agarwal |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
IEEE Open Journal of Intelligent Transportation Systems, Vol 3, Pp 503-518 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2687-7813 |
| DOI: |
10.1109/OJITS.2022.3182925 |
| Popis: |
We present a physics-informed deep learning (PIDL) approach to tackle the challenge of data sparsity and sensor noise in traffic state estimation (TSE). PIDL strengthens a deep learning (DL) neural network with the knowledge of traffic flow theory to accurately estimate traffic conditions. The ‘physics’—a priori information of the system—acts as a regularization agent during training. We illustrate the implementation of the proposed approach with two commonly used models representing traffic physics: Lighthill-Whitham-Richards (LWR) model and the cell transmission model (CTM). The LWR implementation is illustrated with Greenshields’ and inverse-lambda fundamental diagrams; whereas, CTM model implementation works with any fundamental diagram of choice. Two case studies validate the approach by reconstructing the velocity-field. Case study-I uses synthetic data generated to resemble the trajectory of connected and autonomous vehicles as captured by roadside units. Case study-II employs NGSIM data mimicking scant probe vehicle observations. We observe that the proposed PIDL approach is particularly better in state estimation with a lower amount of training data, illustrating the capability of PIDL in making precise and timely TSE even with sparse input. E.g., With 10% CAV penetration rate and a 15% added-noise, relative error for PIDL was at 22.9% compared to 30.8% for DL. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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