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The fixed pricing strategy of high-speed railways has, for an extended period, presented a challenge to the enhancement of revenue. This research paper employs the theory of revenue management and applies it to the problem of dynamic pricing for multiple high-speed trains. It presents a solution to the issue by transforming it into a Markov Decision Process (MDP). Considering passenger choice behavior, a reinforcement learning environment for dynamic pricing of multiple trains is established with the objective of maximising the expected revenue. The Double Deep Q Network (DDQN) from deep reinforcement learning is employed to solve this issue. The efficacy of the algorithm is evaluated using the Beijing-Shanghai high-speed railway as a case study. Experimental results demonstrate that, compared to fixed pricing strategies and traditional dynamic pricing algorithms (Particle Swarm Optimization, PSO), the DDQN model increases total revenue by approximately 7.70% and 3.89%, respectively, indicating its practical application value.

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Research on Dynamic Pricing Strategies for Multiple High-Speed Trains Based on Deep Reinforcement Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Jiaxing Wang,
  • Jinyou Zhang,
  • Xinyi Zeng,
  • Jiapu Li,
  • Zhenying Yan

摘要

The fixed pricing strategy of high-speed railways has, for an extended period, presented a challenge to the enhancement of revenue. This research paper employs the theory of revenue management and applies it to the problem of dynamic pricing for multiple high-speed trains. It presents a solution to the issue by transforming it into a Markov Decision Process (MDP). Considering passenger choice behavior, a reinforcement learning environment for dynamic pricing of multiple trains is established with the objective of maximising the expected revenue. The Double Deep Q Network (DDQN) from deep reinforcement learning is employed to solve this issue. The efficacy of the algorithm is evaluated using the Beijing-Shanghai high-speed railway as a case study. Experimental results demonstrate that, compared to fixed pricing strategies and traditional dynamic pricing algorithms (Particle Swarm Optimization, PSO), the DDQN model increases total revenue by approximately 7.70% and 3.89%, respectively, indicating its practical application value.