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

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

URL: http://arxiv.org/abs/2403.15780v2
Date: Tue, 24 Sep 2024 09:24:11 GMT
Title: A Fairness-Oriented Reinforcement Learning Approach for the Operation and Control of Shared Micromobility Services
Authors: Matteo Cederle, Luca Vittorio Piron, Marina Ceccon, Federico Chiariotti, Alessandro Fabris, Marco Fabris, Gian Antonio Susto,
Abstract summary: This study investigates the balance between performance optimization and algorithmic fairness in shared micromobility services. Exploiting Q-Learning, the proposed methodology achieves equitable outcomes in terms of the Gini index across different areas. A case study with synthetic data validates our insights and highlights the importance of fairness in urban micromobility.
Score: 46.1428063182192
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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 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.

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