A Fairness-Oriented Reinforcement Learning Approach for the Operation and Control of Shared Micromobility Services
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866915671211769856 |
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| author | Cederle, Matteo Piron, Luca Vittorio Ceccon, Marina Chiariotti, Federico Fabris, Alessandro Fabris, Marco Susto, Gian Antonio |
| author_facet | Cederle, Matteo Piron, Luca Vittorio Ceccon, Marina Chiariotti, Federico Fabris, Alessandro Fabris, Marco Susto, Gian Antonio |
| contents | As Machine Learning grows in popularity across various fields, equity has become a key focus for the AI community. However, fairness-oriented approaches are still underexplored in smart mobility. Addressing this gap, our study investigates the balance between performance optimization and algorithmic fairness in shared micromobility services providing a novel framework based on Reinforcement Learning. Exploiting Q-learning, the proposed methodology achieves equitable outcomes in terms of the Gini index across different areas characterized by their distance from central hubs. Through vehicle rebalancing, the provided scheme maximizes operator performance while ensuring fairness principles for users, reducing iniquity by up to 85% while only increasing costs by 30% (w.r.t. applying no equity adjustment). A case study with synthetic data validates our insights and highlights the importance of fairness in urban micromobility (source code: https://github.com/mcederle99/FairMSS.git). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_15780 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Fairness-Oriented Reinforcement Learning Approach for the Operation and Control of Shared Micromobility Services Cederle, Matteo Piron, Luca Vittorio Ceccon, Marina Chiariotti, Federico Fabris, Alessandro Fabris, Marco Susto, Gian Antonio Systems and Control Computers and Society Machine Learning As Machine Learning grows in popularity across various fields, equity has become a key focus for the AI community. However, fairness-oriented approaches are still underexplored in smart mobility. Addressing this gap, our study investigates the balance between performance optimization and algorithmic fairness in shared micromobility services providing a novel framework based on Reinforcement Learning. Exploiting Q-learning, the proposed methodology achieves equitable outcomes in terms of the Gini index across different areas characterized by their distance from central hubs. Through vehicle rebalancing, the provided scheme maximizes operator performance while ensuring fairness principles for users, reducing iniquity by up to 85% while only increasing costs by 30% (w.r.t. applying no equity adjustment). A case study with synthetic data validates our insights and highlights the importance of fairness in urban micromobility (source code: https://github.com/mcederle99/FairMSS.git). |
| title | A Fairness-Oriented Reinforcement Learning Approach for the Operation and Control of Shared Micromobility Services |
| topic | Systems and Control Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2403.15780 |