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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2408.00251 |
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| _version_ | 1866917738534928384 |
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| author | Angah, Ohay Enouen, James Xuegang Ban Liu, Yan |
| author_facet | Angah, Ohay Enouen, James Xuegang Ban Liu, Yan |
| contents | This study aims to discover the governing mathematical expressions of car-following dynamics from trajectory data directly using deep learning techniques. We propose an expression exploration framework based on deep symbolic regression (DSR) integrated with a variable intersection selection (VIS) method to find variable combinations that encourage interpretable and parsimonious mathematical expressions. In the exploration learning process, two penalty terms are added to improve the reward function: (i) a complexity penalty to regulate the complexity of the explored expressions to be parsimonious, and (ii) a variable interaction penalty to encourage the expression exploration to focus on variable combinations that can best describe the data. We show the performance of the proposed method to learn several car-following dynamics models and discuss its limitations and future research directions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_00251 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Discovering Car-following Dynamics from Trajectory Data through Deep Learning Angah, Ohay Enouen, James Xuegang Ban Liu, Yan Machine Learning This study aims to discover the governing mathematical expressions of car-following dynamics from trajectory data directly using deep learning techniques. We propose an expression exploration framework based on deep symbolic regression (DSR) integrated with a variable intersection selection (VIS) method to find variable combinations that encourage interpretable and parsimonious mathematical expressions. In the exploration learning process, two penalty terms are added to improve the reward function: (i) a complexity penalty to regulate the complexity of the explored expressions to be parsimonious, and (ii) a variable interaction penalty to encourage the expression exploration to focus on variable combinations that can best describe the data. We show the performance of the proposed method to learn several car-following dynamics models and discuss its limitations and future research directions. |
| title | Discovering Car-following Dynamics from Trajectory Data through Deep Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2408.00251 |