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

2024-03-23 · Matteo Cederle, Luca Vittorio Piron, Marina Ceccon, Federico Chiariotti, Alessandro Fabris, Marco Fabris, Gian Antonio Susto

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).

📄 PDF Abstract BibTeX arXiv:2403.15780

Code (1)

mcederle99/fairmss 공식 구현

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FairnessQ-Learning

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Q-Learning Q-Learning is an off-policy temporal difference control algorithm: $$Q\left(S\_{t}, A\_{t}\right) \leftarrow Q\left(S\_{t}, A\_{t}\right) + \alpha\left[R_{t+1} +…

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

2025-04-03 · Matteo Cederle, Marco Fabris, Gian Antonio Susto

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 …

Fairness

Operational AI Deployment Assurance: Governance-State Orchestration Under Threshold-Sensitive Deployment Conditions -- A Governance Framework for High-Stakes AI Systems

2026-05-27 · Khalid Adnan Alsayed arxiv

AI governance frameworks increasingly emphasize fairness, transparency, accountability, and lifecycle risk management in high-stakes domains. However, many current approaches remain observational, relying on static metri…

Utility-Aware DRL-Based TXOP Adaptation for NR-U and Wi-Fi Coexistence Networks

2026-05-01 · Po-Heng Chou, Yi-Fang Yu, Shou-Yu Chen, Chiapin Wang arxiv

The coexistence of NR-U and Wi-Fi in the unlicensed spectrum introduces a challenging resource management problem, where heterogeneous channel access mechanisms can lead to unbalanced spectrum utilization and severe Wi-F…

Reinforcement Learning

Fairness in Reinforcement Learning

2019-07-24 · Paul Weng

Decision support systems (e.g., for ecological conservation) and autonomous systems (e.g., adaptive controllers in smart cities) start to be deployed in real applications. Although their operations often impact many user…

BIG-bench Machine Learning Deep Reinforcement Learning Fairness reinforcement-learning+2

Cooperative Deep Reinforcement Learning for Fair RIS Allocation

2026-03-26 · Martin Mark Zan, Stefan Schwarz arxiv

The deployment of reconfigurable intelligent surfaces (RISs) introduces new challenges for resource allocation in multi-cell wireless networks, particularly when user loads are uneven across base stations. In this work, …

Multi-agent Reinforcement Learning