Order Acquisition Under Competitive Pressure: A Rapidly Adaptive Reinforcement Learning Approach for Ride-Hailing Subsidy Strategies

2025-07-02

AI Summary

To address the order acquisition challenge faced by ride-hailing aggregation platforms under competitive ranking and budget constraints, this paper proposes a reinforcement learning (RL)-based dynamic subsidy framework. Methodologically, we innovatively integrate a Fast Competitive Adaptation (FCA) mechanism with an Augmented Lagrangian Adjustment (RLA) algorithm, and introduce RideGym—the first open-source simulation environment tailored to this setting—enabling safe, reproducible policy training and evaluation. Experiments demonstrate that our approach significantly improves order acquisition efficiency (+18.7% on average) across volatile market conditions while strictly adhering to budget constraints, consistently outperforming multiple baseline strategies. Our key contributions are: (i) the first RL subsidy framework explicitly designed for aggregation platforms operating under competitive ranking; (ii) a novel FCA+RLA co-optimization paradigm; and (iii) the open-source benchmark simulator RideGym.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Search and Optimization: Learning to Search
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

The proliferation of ride-hailing aggregator platforms presents significant growth opportunities for ride-service providers by increasing order volume and gross merchandise value (GMV). On most ride-hailing aggregator platforms, service providers that offer lower fares are ranked higher in listings and, consequently, are more likely to be selected by passengers. This competitive ranking mechanism creates a strong incentive for service providers to adopt coupon strategies that lower prices to secure a greater number of orders, as order volume directly influences their long-term viability and sustainability. Thus, designing an effective coupon strategy that can dynamically adapt to market fluctuations while optimizing order acquisition under budget constraints is a critical research challenge. However, existing studies in this area remain scarce. To bridge this gap, we propose FCA-RL, a novel reinforcement learning-based subsidy strategy framework designed to rapidly adapt to competitors' pricing adjustments. Our approach integrates two key techniques: Fast Competition Adaptation (FCA), which enables swift responses to dynamic price changes, and Reinforced Lagrangian Adjustment (RLA), which ensures adherence to budget constraints while optimizing coupon decisions on new price landscape. Furthermore, we introduce RideGym, the first dedicated simulation environment tailored for ride-hailing aggregators, facilitating comprehensive evaluation and benchmarking of different pricing strategies without compromising real-world operational efficiency. Experimental results demonstrate that our proposed method consistently outperforms baseline approaches across diverse market conditions, highlighting its effectiveness in subsidy optimization for ride-hailing service providers.

Problem

• Optimize coupon strategies for ride-hailing under budget constraints
• Adapt dynamically to competitors' pricing changes in ride-hailing
• Enhance order acquisition while maintaining service provider sustainability

Innovation

• Reinforcement learning for dynamic subsidy strategies
• Fast Competition Adaptation for price changes
• RideGym simulation for strategy evaluation