A Fairness-Oriented Reinforcement Learning Approach for the Operation and Control of Shared Micromobility Services
Abstract
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).
Keywords
Cite
@article{arxiv.2403.15780,
title = {A Fairness-Oriented Reinforcement Learning Approach for the Operation and Control of Shared Micromobility Services},
author = {Matteo Cederle and Luca Vittorio Piron and Marina Ceccon and Federico Chiariotti and Alessandro Fabris and Marco Fabris and Gian Antonio Susto},
journal= {arXiv preprint arXiv:2403.15780},
year = {2025}
}
Comments
6 pages, 3 figures, accepted at the 2025 American Control Conference (ACC) on January 17th, 2025