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

arXiv: https://arxiv.org/abs/2403.15780 · HTML: https://mlsanity.com/paper/2403.15780/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods.
- Builds on 4 of its 37 references that are in the map.
- First posted on arXiv 23 Mar 2024.

## Placed in

- Deep reinforcement learning methods / [RL for games and combinatorial optimization](https://mlsanity.com/deeprl/rl-for-games-and-combinatorial-optimization/)

## Builds on (most cited first, 4 of 4)

- [Fairness in Machine Learning: A Survey](https://mlsanity.com/paper/2010.04053/) (2020)
- [A comparative study of fairness-enhancing interventions in machine learning](https://mlsanity.com/paper/1802.04422/) (2018)
- [Fairness in Deep Learning: A Computational Perspective](https://mlsanity.com/paper/1908.08843/) (2019)
- [Fair-MPC: a control-oriented framework for socially just decision-making](https://mlsanity.com/paper/2312.05554/) (2023)

## Built on by (most cited first, 3 of 3)

- [A Fairness-Oriented Multi-Objective Reinforcement Learning approach for Autonomous Intersection Management](https://mlsanity.com/paper/2507.09311/) (2025)
- [Balancing Efficiency and Fairness in Traffic Light Control through Deep Reinforcement Learning](https://mlsanity.com/paper/2605.10170/) (2026)
- [Fully Dynamic Rebalancing in Dockless Bike-Sharing Systems via Deep Reinforcement Learning](https://mlsanity.com/paper/2605.14501/) (2026)

## Abstract (arXiv)

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).