# Regulating Spatial Fairness in a Tripartite Micromobility Sharing System via Reinforcement Learning

arXiv: https://arxiv.org/abs/2504.02597 · HTML: https://mlsanity.com/paper/2504.02597/

## Key facts

- Cited by 1 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 0 of its 10 references that are in the map.
- First posted on arXiv 3 Apr 2025.

## Placed in

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

## Built on by (most cited first, 1 of 1)

- [A Fairness-Oriented Multi-Objective Reinforcement Learning approach for Autonomous Intersection Management](https://mlsanity.com/paper/2507.09311/) (2025)

## Abstract (arXiv)

In the growing field of Shared Micromobility Systems, which holds great potential for shaping urban transportation, fairness-oriented approaches remain largely unexplored. This work addresses such a gap by investigating the balance between performance optimization and algorithmic fairness in Shared Micromobility Services using Reinforcement Learning. Our methodology achieves equitable outcomes, measured by the Gini index, across central, peripheral, and remote station categories. By strategically rebalancing vehicle distribution, it maximizes operator performance while upholding fairness principles. The efficacy of our approach is validated through a case study using synthetic data.