Profit-Aware Graph Framework for Cross-Platform Ride-Sharing: Analyzing Allocation Mechanisms and Efficiency Gains

2025-08-26

AI Summary

Platform fragmentation in ride-hailing ecosystems restricts ride-pooling matching to intra-platform operations, hindering system-wide efficiency gains. Method: We propose a profit-aware graph optimization framework that formulates cross-platform ride-pooling as a profit-constrained graph matching problem. Integrating graph-theoretic modeling with network optimization, we comparatively analyze three profit-allocation mechanisms—Shapley value, equal-profit sharing, and market-share-based allocation. Contribution/Results: Our analysis first uncovers the scale economies induced by demand growth and their diminishing marginal returns. Large-scale simulations demonstrate that the Shapley-value mechanism dominates across six key metrics: matching rate, passenger waiting time, vehicle empty-trip rate, platform revenue, fairness, and system stability. Moreover, both system efficiency and passenger service quality improve monotonically with increasing demand, empirically validating substantial gains from cross-platform collaboration.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Combinatorial Optimization
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Economics, Online Markets and Human Computation: The sharing economy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Ride-hailing platforms (e.g., Uber, Lyft) have transformed urban mobility by enabling ride-sharing, which holds considerable promise for reducing both travel costs and total vehicle miles traveled (VMT). However, the fragmentation of these platforms impedes system-wide efficiency by restricting ride-matching to intra-platform requests. Cross-platform collaboration could unlock substantial efficiency gains, but its realization hinges on fair and sustainable profit allocation mechanisms that can align the incentives of competing platforms. This study introduces a graph-theoretic framework that embeds profit-aware constraints into network optimization, facilitating equitable and efficient cross-platform ride-sharing. Within this framework, we evaluate three allocation schemes -- equal-profit-based, market-share-based, and Shapley-value-based -- through large-scale simulations. Results show that the Shapley-value-based mechanism consistently outperforms the alternatives across six key metrics. Notably, system efficiency and rider service quality improve with increasing demand, reflecting clear economies of scale. The observed economies of scale, along with their diminishing returns, can be understood with the structural evolution of rider-request graphs, where super-linear edge growth expands feasible matches and sub-linear degree scaling limits per-rider connectivity.

Problem

• Addressing cross-platform ride-sharing efficiency and fairness
• Evaluating profit allocation mechanisms for competing platforms
• Analyzing scalability and connectivity in rider-request graphs

Innovation

• Graph-theoretic framework with profit-aware constraints
• Evaluated three allocation schemes via simulations
• Shapley-value-based mechanism outperforms other methods