دورية أكاديمية
Physics-Informed Deep Learning for Traffic State Estimation: Illustrations With LWR and CTM Models
| العنوان: | Physics-Informed Deep Learning for Traffic State Estimation: Illustrations With LWR and CTM Models |
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| المؤلفون: | Archie J. Huang, Shaurya Agarwal |
| المصدر: | IEEE Open Journal of Intelligent Transportation Systems, Vol 3, Pp 503-518 (2022) |
| بيانات النشر: | IEEE, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Transportation engineering LCC:Transportation and communications |
| مصطلحات موضوعية: | Physics informed deep learning, traffic state estimation, LWR model, CTM model, TSE, PIDL, Transportation engineering, TA1001-1280, Transportation and communications, HE1-9990 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2687-7813 |
| Relation: | https://ieeexplore.ieee.org/document/9795676/; https://doaj.org/toc/2687-7813 |
| DOI: | 10.1109/OJITS.2022.3182925 |
| URL الوصول: | https://doaj.org/article/73ec527f6dbf423899fbfcfb483c8ebe |
| رقم الأكسشن: | edsdoj.73ec527f6dbf423899fbfcfb483c8ebe |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 26877813 |
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| DOI: | 10.1109/OJITS.2022.3182925 |