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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.11336 |
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| _version_ | 1866911442313150464 |
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| author | Baloul, Nail Hayat, Amaury Liard, Thibault Lissy, Pierre |
| author_facet | Baloul, Nail Hayat, Amaury Liard, Thibault Lissy, Pierre |
| contents | We propose an efficient method for reconstructing traffic density with low penetration rate of probe vehicles. Specifically, we rely on measuring only the initial and final positions of a small number of cars which are generated using microscopic dynamical systems. We then implement a machine learning algorithm from scratch to reconstruct the approximate traffic density. This approach leverages learning techniques to improve the accuracy of density reconstruction despite constraints in available data. For the sake of consistency, we will prove that, if only using data from dynamical systems, the approximate density predicted by our learned-based model converges to a well-known macroscopic traffic flow model when the number of vehicles approaches infinity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_11336 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Traffic Flow Reconstruction from Limited Collected Data Baloul, Nail Hayat, Amaury Liard, Thibault Lissy, Pierre Dynamical Systems Machine Learning We propose an efficient method for reconstructing traffic density with low penetration rate of probe vehicles. Specifically, we rely on measuring only the initial and final positions of a small number of cars which are generated using microscopic dynamical systems. We then implement a machine learning algorithm from scratch to reconstruct the approximate traffic density. This approach leverages learning techniques to improve the accuracy of density reconstruction despite constraints in available data. For the sake of consistency, we will prove that, if only using data from dynamical systems, the approximate density predicted by our learned-based model converges to a well-known macroscopic traffic flow model when the number of vehicles approaches infinity. |
| title | Traffic Flow Reconstruction from Limited Collected Data |
| topic | Dynamical Systems Machine Learning |
| url | https://arxiv.org/abs/2602.11336 |