Dynamic Pricing in High-Speed Railways Using Multi-Agent Reinforcement Learning

• URL: http://arxiv.org/abs/2501.08234v1
• Date: Tue, 14 Jan 2025 16:19:25 GMT
• Title: Dynamic Pricing in High-Speed Railways Using Multi-Agent Reinforcement Learning
• Authors: Enrique Adrian Villarrubia-Martin, Luis Rodriguez-Benitez, David Muñoz-Valero, Giovanni Montana, Luis Jimenez-Linares,
• Abstract summary: This paper addresses the challenge of designing effective dynamic pricing strategies in the context of competing and cooperating operators. A reinforcement learning framework based on a non-zero-sum Markov game is proposed, incorporating random utility models to capture passenger decision making.
• Score: 4.800138615859937
• License:
• Abstract: This paper addresses a critical challenge in the high-speed passenger railway industry: designing effective dynamic pricing strategies in the context of competing and cooperating operators. To address this, a multi-agent reinforcement learning (MARL) framework based on a non-zero-sum Markov game is proposed, incorporating random utility models to capture passenger decision making. Unlike prior studies in areas such as energy, airlines, and mobile networks, dynamic pricing for railway systems using deep reinforcement learning has received limited attention. A key contribution of this paper is a parametrisable and versatile reinforcement learning simulator designed to model a variety of railway network configurations and demand patterns while enabling realistic, microscopic modelling of user behaviour, called RailPricing-RL. This environment supports the proposed MARL framework, which models heterogeneous agents competing to maximise individual profits while fostering cooperative behaviour to synchronise connecting services. Experimental results validate the framework, demonstrating how user preferences affect MARL performance and how pricing policies influence passenger choices, utility, and overall system dynamics. This study provides a foundation for advancing dynamic pricing strategies in railway systems, aligning profitability with system-wide efficiency, and supporting future research on optimising pricing policies.

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