A Fairness-Oriented Reinforcement Learning Approach for the Operation and Control of Shared Micromobility Services

Autor:	Cederle, Matteo, Piron, Luca Vittorio, Ceccon, Marina, Chiariotti, Federico, Fabris, Alessandro, Fabris, Marco, Susto, Gian Antonio
Rok vydání:	2024
Předmět:	Electrical Engineering and Systems Science - Systems and Control Computer Science - Computers and Society Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 6 pages, 2 figures, jointly submitted to IEEE L-CSS and ACC 2025
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2403.15780 Zobrazit plný text záznamu View this record from Arxiv