Online Traffic Density Estimation using Physics-Informed Neural Networks
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866915228285927424 |
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| author | Wilkman, Dennis Morozovska, Kateryna Johansson, Karl Henrik Barreau, Matthieu |
| author_facet | Wilkman, Dennis Morozovska, Kateryna Johansson, Karl Henrik Barreau, Matthieu |
| contents | Recent works on the application of Physics-Informed Neural Networks to traffic density estimation have shown to be promising for future developments due to their robustness to model errors and noisy data. In this paper, we introduce a methodology for online approximation of the traffic density using measurements from probe vehicles in two settings: one using the Greenshield model and the other considering a high-fidelity traffic simulation. The proposed method continuously estimates the real-time traffic density in space and performs model identification with each new set of measurements. The density estimate is updated in almost real-time using gradient descent and adaptive weights. In the case of full model knowledge, the resulting algorithm has similar performance to the classical open-loop one. However, in the case of model mismatch, the iterative solution behaves as a closed-loop observer and outperforms the baseline method. Similarly, in the high-fidelity setting, the proposed algorithm correctly reproduces the traffic characteristics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_03483 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Online Traffic Density Estimation using Physics-Informed Neural Networks Wilkman, Dennis Morozovska, Kateryna Johansson, Karl Henrik Barreau, Matthieu Machine Learning Optimization and Control Recent works on the application of Physics-Informed Neural Networks to traffic density estimation have shown to be promising for future developments due to their robustness to model errors and noisy data. In this paper, we introduce a methodology for online approximation of the traffic density using measurements from probe vehicles in two settings: one using the Greenshield model and the other considering a high-fidelity traffic simulation. The proposed method continuously estimates the real-time traffic density in space and performs model identification with each new set of measurements. The density estimate is updated in almost real-time using gradient descent and adaptive weights. In the case of full model knowledge, the resulting algorithm has similar performance to the classical open-loop one. However, in the case of model mismatch, the iterative solution behaves as a closed-loop observer and outperforms the baseline method. Similarly, in the high-fidelity setting, the proposed algorithm correctly reproduces the traffic characteristics. |
| title | Online Traffic Density Estimation using Physics-Informed Neural Networks |
| topic | Machine Learning Optimization and Control |
| url | https://arxiv.org/abs/2504.03483 |