# Long-term Fairness in Ride-Hailing Platform

arXiv: https://arxiv.org/abs/2407.17839 · HTML: https://mlsanity.com/paper/2407.17839/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 25 Jul 2024.

## Builds on (most cited first, 9 of 9)

- [Neural Approximate Dynamic Programming for On-Demand Ride-Pooling](https://mlsanity.com/paper/1911.08842/) (2019)
- [Exploring Algorithmic Fairness in Robust Graph Covering Problems](https://mlsanity.com/paper/2006.06865/) (2020)
- [Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms During High-Demand Hours](https://mlsanity.com/paper/1912.08388/) (2019)
- [Group-level Fairness Maximization in Online Bipartite Matching](https://mlsanity.com/paper/2011.13908/) (2020)
- [Data-Driven Methods for Balancing Fairness and Efficiency in Ride-Pooling](https://mlsanity.com/paper/2110.03524/) (2021)
- [Efficient Ridesharing Dispatch Using Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2006.10897/) (2020)
- [Trading the System Efficiency for the Income Equality of Drivers in Rideshare](https://mlsanity.com/paper/2012.06850/) (2020)
- [Using Simple Incentives to Improve Two-Sided Fairness in Ridesharing Systems](https://mlsanity.com/paper/2303.14332/) (2023)
- [Promoting Two-sided Fairness in Dynamic Vehicle Routing Problem](https://mlsanity.com/paper/2405.19184/) (2024)

## Abstract (arXiv)

Matching in two-sided markets such as ride-hailing has recently received significant attention. However, existing studies on ride-hailing mainly focus on optimising efficiency, and fairness issues in ride-hailing have been neglected. Fairness issues in ride-hailing, including significant earning differences between drivers and variance of passenger waiting times among different locations, have potential impacts on economic and ethical aspects. The recent studies that focus on fairness in ride-hailing exploit traditional optimisation methods and the Markov Decision Process to balance efficiency and fairness. However, there are several issues in these existing studies, such as myopic short-term decision-making from traditional optimisation and instability of fairness in a comparably longer horizon from both traditional optimisation and Markov Decision Process-based methods. To address these issues, we propose a dynamic Markov Decision Process model to alleviate fairness issues currently faced by ride-hailing, and seek a balance between efficiency and fairness, with two distinct characteristics: (i) a prediction module to predict the number of requests that will be raised in the future from different locations to allow the proposed method to consider long-term fairness based on the whole timeline instead of consider fairness only based on historical and current data patterns; (ii) a customised scalarisation function for multi-objective multi-agent Q Learning that aims to balance efficiency and fairness. Extensive experiments on a publicly available real-world dataset demonstrate that our proposed method outperforms existing state-of-the-art methods.