Learning from user's behaviour of some well-known congested traffic networks
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866911653697683456 |
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| author | Cardoso, Isolda Venturato, Lucas Walpen, Jorgelina |
| author_facet | Cardoso, Isolda Venturato, Lucas Walpen, Jorgelina |
| contents | The traffic assignment problem (TAP) aims to predict how traffic flows distribute themselves across a road network, traditionally requiring computationally expensive iterative simulations to reach a user equilibrium (UE) where no driver can unilaterally reduce their travel time. Recent developments in machine learning (ML), particularly Graph Neural Networks (GNNs) and hybrid approaches, aim to solve this faster while maintaining accuracy |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_14804 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning from user's behaviour of some well-known congested traffic networks Cardoso, Isolda Venturato, Lucas Walpen, Jorgelina Optimization and Control Machine Learning 90B20, 68T20, 90C33 The traffic assignment problem (TAP) aims to predict how traffic flows distribute themselves across a road network, traditionally requiring computationally expensive iterative simulations to reach a user equilibrium (UE) where no driver can unilaterally reduce their travel time. Recent developments in machine learning (ML), particularly Graph Neural Networks (GNNs) and hybrid approaches, aim to solve this faster while maintaining accuracy |
| title | Learning from user's behaviour of some well-known congested traffic networks |
| topic | Optimization and Control Machine Learning 90B20, 68T20, 90C33 |
| url | https://arxiv.org/abs/2508.14804 |