🤖 machine learning

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

This paper introduces HCRide, a human-centered ride-hailing system that employs a novel multi-agent reinforcement learning algorithm called Harmonization-oriented Actor-Bi-Critic (Habic) to effectively harmonize passenger fairness and driver preferences while simultaneously improving overall system efficiency.

Original authors: Lin Jiang, Yu Yang, Guang Wang

Published 2026-08-06

📖 4 min read☕ Coffee break read

CC BY 4.0

Original authors: Lin Jiang, Yu Yang, Guang Wang

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine a bustling city as a giant, living puzzle where millions of people are trying to get from point A to point B. To solve this puzzle, we have ride-hailing apps like Uber or Lyft, which act as the master puzzle-solvers. Their job is to decide which driver picks up which passenger. For a long time, these puzzle-solvers only cared about one thing: speed. They wanted to get everyone moving as fast as possible to make the most money for the company. But this "speed-first" approach often left some people waiting forever while others got cars instantly, and it sometimes sent drivers to places they hated, far from their homes or usual routes. This is where a branch of computer science called "Human-Centered AI" steps in. It asks a simple but tricky question: Can we build a system that is fast and fair to everyone, while also making sure the people doing the driving (the humans behind the wheel) are happy? It's like trying to run a cafeteria where you want the food to be served quickly, but you also want every student to get a fair share and the cooks to enjoy their work.

The paper you're about to read, titled "HCRide," tackles this exact challenge. The researchers, Lin Jiang, Yu Yang, and Guang Wang, realized that the old way of dispatching rides was too selfish. They wanted to design a new system that balances three things: getting passengers their rides quickly, making sure no passenger waits too much longer than someone else nearby, and respecting where drivers actually want to drive. To do this, they created a smart computer program called HCRide. Think of HCRide as a super-organized traffic manager that doesn't just look at a map, but also listens to the feelings of the drivers and the needs of the passengers.

The core of their invention is a clever algorithm named "Habic" (Harmonization-oriented Actor-Bi-Critic). Imagine a high-stakes game of musical chairs, but instead of chairs, the players are drivers and the music is a stream of ride requests. In the old games, the drivers would just rush blindly. In HCRide's version, there's a special "competition mechanism" where only the drivers who are actually close and capable of taking a specific ride get to compete for it. This keeps the game fair and manageable. Then, there are two "judges" (the Bi-Critic part). One judge checks how happy the passengers are (looking at wait times and fairness), and the other judge checks if the drivers are being sent to places they dislike. The system's "brain" (the Actor) listens to both judges and makes a decision that tries to please everyone. If a driver is sent to a place they hate, the "cost" goes up, and the system learns to avoid that in the future.

The researchers tested their new system using real data from two very different cities: Shenzhen in China and New York City in the US. They ran simulations with over a million ride orders to see how HCRide would perform against the best existing methods. The results were promising. In these simulations, HCRide managed to improve the overall system efficiency by about 2.02%, meaning rides were slightly faster for everyone. More importantly, it made the experience much fairer for passengers, reducing wait-time inequality by about 5.39%. Perhaps most surprisingly, it also respected the drivers' preferences significantly better, improving driver satisfaction by 10.21% compared to the old methods. The paper suggests that by treating drivers and passengers as humans with specific needs rather than just numbers on a screen, we can build a ride-hailing world that works better for everyone.

Technical Summary: HCRide – Harmonizing Passenger Fairness and Driver Preference

Problem Formulation
The paper addresses the limitations of existing ride-hailing order dispatch systems, which predominantly prioritize platform revenue and system efficiency, often at the expense of passenger experience and driver satisfaction. Specifically, current approaches frequently overlook two critical human-centric factors:

Passenger Fairness: Existing fairness models often assume absolute equality in waiting times regardless of location, ignoring the dynamic supply-demand fluctuations across different spatial and temporal contexts.
Driver Preference: Drivers often have distinct preferences for operating in specific regions (e.g., near home or familiar areas), yet dispatch systems frequently assign them to unfamiliar or undesired zones, leading to dissatisfaction and reduced operational efficiency.

The authors formalize the order dispatch problem as a Constrained Markov Decision Process (CMDP). The objective is to maximize long-term cumulative rewards (system efficiency and passenger fairness) while ensuring that the cumulative cost (violation of driver preferences) remains below a predetermined threshold.

Methodology: HCRide and Habic
To solve this CMDP, the authors propose HCRide, a human-centered framework powered by a novel multi-agent reinforcement learning (MARL) algorithm called Harmonization-oriented Actor-Bi-Critic (Habic). The system is built on three core components:

Spatio-Temporal Fairness and Preference Modeling:
- Passenger Fairness: Defined at two levels. Intra-region fairness ensures passengers in the same region and time period experience comparable waiting times. Inter-region fairness establishes a dynamic benchmark based on the supply-demand ratio, acknowledging that waiting times should naturally vary based on regional density.
- Driver Preference: Modeled using historical visitation frequencies. Regions are categorized into Positive ( $H^+$ ), Neutral ( $H^0$ ), and Negative ( $H^-$ ) sets. Dispatching a driver to a region in $H^-$ incurs a cost, with the magnitude determined by the distance to the driver's nearest preferred location.
Multi-Agent Competition Mechanism:
To manage the large joint action space, the system transforms the problem into smaller, distributed action spaces. For each order, a limited set of candidate drivers within a specific range competes for the task. This mechanism generates "matching features" (combining driver state and order state) to facilitate micro-level decision-making among heterogeneous drivers with varying preferences.
Harmonization-oriented Actor-Bi-Critic (Habic) Algorithm:
- Dynamic Actor Network: Utilizes the matching features to select the optimal driver. It alternately updates the policy parameters ( $\theta$ ) and the Lagrange multiplier ( $\lambda$ ) to balance the trade-off between reward maximization and constraint satisfaction.
- Bi-Critic Network: Consists of two evaluation networks. The Reward Critic estimates the accumulated reward (efficiency and fairness), while the Cost Critic estimates the accumulated cost (preference violations). Both utilize Generalized Advantage Estimation (GAE) and Proximal Policy Optimization (PPO-Clip) to ensure stable training and convergence.

Key Contributions

Novel Framework: This work presents the first study on human-centered ride-hailing dispatch that simultaneously optimizes for passenger fairness and driver preference without compromising overall system efficiency.
Data-Driven Definitions: The authors introduce formal, spatio-temporal-aware definitions for fairness and driver preference, derived from qualitative studies and quantitative analysis of over one million real-world orders.
Algorithmic Innovation: The development of the Habic algorithm, which integrates a multi-agent competition mechanism with a Bi-Critic architecture to effectively solve the constrained optimization problem in a dynamic environment.

Experimental Results
The HCRide system was evaluated using two real-world datasets: Shenzhen (1.07 million orders) and New York City (214k orders). It was compared against eight baselines, including myopic dispatching, single-agent and multi-agent RL methods, and constrained RL approaches.

Key performance improvements of HCRide over state-of-the-art baselines include:

System Efficiency: Reduced Average Passenger Waiting Time (APWT) by 2.02% (Shenzhen) and 2.02% (NYC) compared to the state-of-the-art baseline Lag-TRPO.
Passenger Fairness: Improved inter-region fairness by 5.29% (Shenzhen) and 5.28% (NYC); improved intra-region fairness by 7.65% (Shenzhen) and 5.39% (NYC).
Driver Preference: Reduced the Preference Violation Rate by 7.77% (Shenzhen) and 10.21% (NYC).

Significance and Claims
The paper claims that HCRide successfully harmonizes conflicting objectives in ride-hailing dispatch. By treating driver preference as a constraint rather than an afterthought, and by defining fairness through a spatio-temporal lens rather than absolute equality, the system achieves a more balanced ecosystem. The authors assert that their approach not only enhances the user experience for both passengers and drivers but also maintains or improves the operational efficiency of the platform, offering a viable path toward more sustainable and human-centric ride-hailing services.

Technical Summary: HCRide – Harmonizing Passenger Fairness and Driver Preference

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