Основні поняття
Balancing performance optimization and fairness is crucial in shared micromobility services.
Анотація
The article discusses the importance of fairness in the operation and control of shared micromobility services. It introduces a pioneering investigation into balancing performance optimization and algorithmic fairness using Q-Learning in Reinforcement Learning. The study focuses on equitable outcomes across different station categories, aiming to maximize operator performance while upholding fairness principles for users. The methodology is validated through a case study or simulation based on synthetic data, emphasizing the critical role of fairness considerations in shaping control strategies for shared micromobility services.
Structure:
- Introduction to Micromobility Systems
- Challenges and Impact of Micromobility Growth
- Equity Issues in Micromobility Services
- Fairness Problems Similar to Machine Learning Bias
- Spatial Fairness Trade-offs in MSS Operation
- Reinforcement Learning Approach Overview
- Numerical Simulations and Case Study Analysis
Статистика
"Notably, our methodology stands out for its ability to achieve equitable outcomes, as measured by the Gini index, across different station categories—central, peripheral, and remote."
"Through Monte Carlo simulations, we reveal the presence of an inherent trade-off between the MSS performance and the associated fairness level obtained by applying a parametric family of RL-based strategies."
Цитати
"Optimizing satisfaction of expected demand implies concentrating vehicles in popular areas with denser populations."
"A fair allocation would require equally distributing vehicles across all areas, with lower performance for the same cost."