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

2026
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Authors: Fangzhou Shi; Xiaopeng Ke; Xinye Xiong; Kexin Meng; Chang Men; Zhengdan Zhu
Published in: Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track and Demo Track
Publisher: Springer Nature Switzerland

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Abstract

This chapter delves into the challenges faced by ride-service providers (RSPs) in the competitive landscape of ride-hailing aggregators (RHAs). It introduces FCA-RL, a reinforcement learning framework designed to optimize order acquisition through dynamic coupon strategies. The study highlights the importance of adapting to price competition and presents RideGym, a simulation system for evaluating these strategies. Key topics include the formulation of the order acquisition problem as a Markov Decision Process (MDP), the development of the Fast Competition Adaptation (FCA) module for real-time market response, and the Reinforced Lagrangian Adjustment (RLA) module for budget control. The chapter also discusses the evaluation of FCA-RL against baseline methods, demonstrating its superior performance in various competitive scenarios. The conclusion emphasizes the potential of FCA-RL in enhancing order acquisition efficiency and the need for further exploration of passenger response and long-term supply-demand dynamics.

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The online version contains supplementary material available at https://doi.org/10.1007/978-3-032-06129-4_15.

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Title: Order Acquisition Under Competitive Pressure: A Rapidly Adaptive Reinforcement Learning Approach for Ride-Hailing Subsidy Strategies
Authors: Fangzhou Shi
Xiaopeng Ke
Xinye Xiong
Kexin Meng
Chang Men
Zhengdan Zhu
Publisher: Springer Nature Switzerland
Book: Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track and Demo Track
Print ISBN: 978-3-032-06128-7

Electronic ISBN: 978-3-032-06129-4

Copyright Year: 2026
DOI: https://doi.org/10.1007/978-3-032-06129-4_15

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