[Paper Review] Fairness-Enhancing Vehicle Rebalancing in the Ride-hailing System

Xiaotong Guo, Hanyong Xu+3 more|arXiv (Cornell University)|Dec 29, 2023

Transportation and Mobility Innovations4 citations

TL;DR

This paper proposes a fairness-enhanced vehicle rebalancing framework for ride-hailing systems that integrates a Socio-Aware Spatial-Temporal Graph Convolutional Network (SA-STGCN) for equitable demand prediction and a fairness-integrated Matching-Integrated Vehicle Rebalancing (MIVR) model for balanced vehicle distribution. The approach reduces prediction bias and improves service equity, achieving a 6.48% reduction in wait time standard deviation and 0.49% decrease in average wait times, demonstrating a win-win outcome for fairness and operational efficiency.

ABSTRACT

The rapid growth of the ride-hailing industry has revolutionized urban transportation worldwide. Despite its benefits, equity concerns arise as underserved communities face limited accessibility to affordable ride-hailing services. A key issue in this context is the vehicle rebalancing problem, where idle vehicles are moved to areas with anticipated demand. Without equitable approaches in demand forecasting and rebalancing strategies, these practices can further deepen existing inequities. In the realm of ride-hailing, three main facets of fairness are recognized: algorithmic fairness, fairness to drivers, and fairness to riders. This paper focuses on enhancing both algorithmic and rider fairness through a novel vehicle rebalancing method. We introduce an approach that combines a Socio-Aware Spatial-Temporal Graph Convolutional Network (SA-STGCN) for refined demand prediction and a fairness-integrated Matching-Integrated Vehicle Rebalancing (MIVR) model for subsequent vehicle rebalancing. Our methodology is designed to reduce prediction discrepancies and ensure equitable service provision across diverse regions. The effectiveness of our system is evaluated using simulations based on real-world ride-hailing data. The results suggest that our proposed method enhances both accuracy and fairness in forecasting ride-hailing demand, ultimately resulting in more equitable vehicle rebalancing in subsequent operations. Specifically, the algorithm developed in this study effectively reduces the standard deviation and average customer wait times by 6.48% and 0.49%, respectively. This achievement signifies a beneficial outcome for ride-hailing platforms, striking a balance between operational efficiency and fairness.

Motivation & Objective

• To address growing equity concerns in ride-hailing systems, where underserved communities face limited access due to biased demand forecasting and rebalancing strategies.
• To reduce algorithmic bias in demand prediction by incorporating socio-economic data and spatial-temporal patterns into the model architecture.
• To enhance rider fairness by minimizing disparities in customer wait times across different geographic regions, especially low-income and historically underserved areas.
• To develop a vehicle rebalancing strategy that balances operational efficiency with equitable service distribution, avoiding feedback loops that disadvantage low-demand zones.
• To demonstrate that fairness in prediction and rebalancing can coexist with improved system-wide performance, achieving a Pareto improvement.

Proposed method

• The SA-STGCN model integrates a socio-enriched adjacency matrix that encodes demographic and economic data (e.g., poverty levels) to improve spatial representation in demand forecasting.
• The model applies bias-reduction regularization to minimize prediction errors across regions, particularly in low-demand, low-income areas.
• A fairness-integrated objective function in the MIVR model incorporates wait time variance and average wait time as optimization targets, with hyperparameters λ and γ to balance fairness and efficiency.
• The MIVR model uses a matching-integrated approach to assign rebalancing movements that prioritize equitable distribution of vehicles across zones.
• The system is evaluated using a simulator trained on real-world ride-hailing data from New York City, with spatial and temporal demand patterns validated against actual taxi trip data.
• Hyperparameter tuning of λ and γ in the objective function enables simultaneous reduction in both average wait time and wait time standard deviation.

(a) NYC Ride-Hailing Demand Distribution (by taxi zones)

Experimental results

Research questions

• RQ1 Can integrating socio-economic data into a spatio-temporal graph neural network improve fairness in ride-hailing demand prediction across diverse urban regions?
• RQ2 To what extent can a fairness-integrated vehicle rebalancing model reduce disparities in customer wait times between high- and low-demand areas?
• RQ3 Does enhancing fairness in demand prediction lead to more equitable vehicle distribution and improved service equity in subsequent rebalancing operations?
• RQ4 Can a Pareto improvement be achieved in ride-hailing systems by simultaneously optimizing for fairness and operational efficiency?
• RQ5 How do hyperparameters in the fairness-aware objective function affect the trade-off between average wait time and wait time variance?

Key findings

• The SA-STGCN model significantly outperforms standard demand prediction models in both accuracy and fairness, particularly in low-income and low-demand regions.
• The fairness-integrated MIVR model reduces the standard deviation of customer wait times by 6.48%, indicating a more equitable distribution of wait times across zones.
• The average customer wait time is reduced by 0.49% through the proposed framework, demonstrating improved system efficiency.
• The joint optimization of fairness and efficiency results in a Pareto improvement, where both fairness and operational performance are enhanced simultaneously.
• The framework effectively breaks the negative feedback loop in which low-demand, underserved areas receive fewer vehicles due to under-prediction and low profitability.
• The results suggest that integrating fairness into demand forecasting and rebalancing can lead to more inclusive mobility outcomes without sacrificing system performance.

(b) NYC Poverty Map (by census tracts)