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.

翻译：随着机器学习在各个领域的日益普及，公平性已成为人工智能社群关注的核心议题。然而在智慧出行领域，面向公平的方法仍缺乏深入探索。为填补这一空白，本研究探讨了共享微出行服务中性能优化与算法公平性之间的平衡，提出了一种基于强化学习的新型框架。该方法利用Q-Learning，在距中心枢纽不同距离的区域间实现了基尼系数层面的公平结果。通过车辆再平衡策略，所提方案在确保用户公平性原则的同时最大化运营商绩效，将不公平程度降低达80%，而成本仅增加30%（相较于未采用公平性调整的情况）。基于合成数据的案例研究验证了我们的观点，并凸显了公平性在城市微出行系统中的重要意义。