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

TL;DR

This paper introduces a multi-agent reinforcement learning framework for dynamic pricing in high-speed railways, modeling passenger behavior and agent interactions to optimize profits and system efficiency.

Contribution

It presents a novel MARL framework and a versatile simulation environment tailored for railway dynamic pricing, addressing a gap in deep reinforcement learning applications in this sector.

Findings

User preferences significantly impact MARL performance.

Pricing policies influence passenger choices and system utility.

The framework effectively models heterogeneous agent interactions.

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.