Regulating Spatial Fairness in a Tripartite Micromobility Sharing System via Reinforcement Learning
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866908435972358144 |
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| author | Cederle, Matteo Fabris, Marco Susto, Gian Antonio |
| author_facet | Cederle, Matteo Fabris, Marco Susto, Gian Antonio |
| contents | In the growing field of Shared Micromobility Systems, which holds great potential for shaping urban transportation, fairness-oriented approaches remain largely unexplored. This work addresses such a gap by investigating the balance between performance optimization and algorithmic fairness in Shared Micromobility Services using Reinforcement Learning. Our methodology achieves equitable outcomes, measured by the Gini index, across central, peripheral, and remote station categories. By strategically rebalancing vehicle distribution, it maximizes operator performance while upholding fairness principles. The efficacy of our approach is validated through a case study using synthetic data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_02597 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Regulating Spatial Fairness in a Tripartite Micromobility Sharing System via Reinforcement Learning Cederle, Matteo Fabris, Marco Susto, Gian Antonio Systems and Control In the growing field of Shared Micromobility Systems, which holds great potential for shaping urban transportation, fairness-oriented approaches remain largely unexplored. This work addresses such a gap by investigating the balance between performance optimization and algorithmic fairness in Shared Micromobility Services using Reinforcement Learning. Our methodology achieves equitable outcomes, measured by the Gini index, across central, peripheral, and remote station categories. By strategically rebalancing vehicle distribution, it maximizes operator performance while upholding fairness principles. The efficacy of our approach is validated through a case study using synthetic data. |
| title | Regulating Spatial Fairness in a Tripartite Micromobility Sharing System via Reinforcement Learning |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2504.02597 |