HCRide: Harmonizing Passenger Fairness and Driver Preference for Human-Centered Ride-Hailing

TL;DR

HCRide is a novel human-centered ride-hailing system that balances passenger fairness, driver preference, and system efficiency using a multi-agent reinforcement learning approach, validated on real-world datasets.

Contribution

This work introduces HCRide, a multi-agent reinforcement learning framework that harmonizes passenger fairness and driver preferences without sacrificing efficiency.

Findings

Improves system efficiency by 2.02%

Enhances fairness by 5.39%

Increases driver preference satisfaction by 10.21%

Abstract

Order dispatch systems play a vital role in ride-hailing services, which directly influence operator revenue, driver profit, and passenger experience. Most existing work focuses on improving system efficiency in terms of operator revenue, which may cause a bad experience for both passengers and drivers. Hence, in this work, we aim to design a human-centered ride-hailing system by considering both passenger fairness and driver preference without compromising the overall system efficiency. However, it is nontrivial to achieve this target due to the potential conflicts between passenger fairness and driver preference since optimizing one may sacrifice the other. To address this challenge, we design HCRide, a Human-Centered Ride-hailing system based on a novel multi-agent reinforcement learning algorithm called Harmonization-oriented Actor-Bi-Critic (Habic), which includes three major components (i.e., a multi-agent competition mechanism, a dynamic Actor network, and a Bi-Critic network) to optimize system efficiency and passenger fairness with driver preference consideration. We extensively evaluate our HCRide using two real-world ride-hailing datasets from Shenzhen and New York City. Experimental results show our HCRide effectively improves system efficiency by 2.02%, fairness by 5.39%, and driver preference by 10.21% compared to state-of-the-art baselines.