Long-term Fairness in Ride-Hailing Platform

TL;DR

This paper introduces a long-term fairness approach for ride-hailing platforms using a dynamic Markov Decision Process, a prediction module, and multi-objective Q Learning to improve fairness and efficiency over extended periods.

Contribution

It proposes a novel long-term fairness model with a prediction component and a customized scalarisation function, addressing instability and short-term bias in previous methods.

Findings

Outperforms existing methods in fairness and efficiency metrics

Effectively balances long-term fairness and efficiency in ride-hailing

Demonstrates robustness on real-world datasets

Abstract

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.