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

2024-03-23

arXiv.org

AI Summary

Balancing operational efficiency and spatial equity remains challenging in shared micromobility systems. Method: This paper proposes a Q-learning–based reinforcement learning framework that, for the first time, incorporates the Gini coefficient into the reward function to quantify inter-zonal vehicle accessibility disparity—enabling joint optimization of efficiency and fairness. The method integrates dynamic vehicle rebalancing modeling with synthetic urban grid simulation, explicitly constraining spatial inequality while preserving operator profitability. Results: Experiments demonstrate an 85% reduction in spatial inequity and substantial improvement in vehicle accessibility in underserved areas; this is achieved with only a 30% increase in operational cost, yielding a Pareto improvement. This work bridges a critical gap in fairness-aware reinforcement learning for intelligent mobility, establishing a quantifiable, model-based paradigm and empirical benchmark for algorithmic fairness in transportation systems.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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 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).

Problem

• Balancing Efficiency and Equity
• Intelligent Learning Methods
• Shared Micro-mobility Services

Innovation

• Q-learning
• Fairness-enhanced Reinforcement Learning
• Shared Micro-mobility Service Management