A Fairness-Oriented Reinforcement Learning Approach for the Operation and Control of Shared Micromobility Services
<|reference_start|>A Fairness-Oriented Reinforcement Learning Approach for the Operation and Control of Shared Micromobility Services: 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 80% 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.<|reference_end|>
@article{cederle2024a,
    title={A Fairness-Oriented Reinforcement Learning Approach for the Operation
  and Control of Shared Micromobility Services},
    author={Matteo Cederle, Luca Vittorio Piron, Marina Ceccon, Federico
  Chiariotti, Alessandro Fabris, Marco Fabris, and Gian Antonio Susto},
    journal={arXiv preprint arXiv:2403.15780},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.15780},
    primaryClass={eess.SY cs.CY cs.LG cs.SY}
}