Ride-Hailing Profits: The Role of New-Existing BBPD

Ride-hailing platforms can employ Behavior-based Price Discrimination (BBPD) using big data technologies. Although the BBPD strategy enables platforms to provide personalized pricing, research suggests that its profitability is influenced by various market factors. In order to investigate the platform's choice of BBPD strategies for new and existing users under different circumstances, this study develops an equilibrium clearing model and utilizes reinforcement learning algorithms to train the platform's pricing based on historical user behaviors. Through experiments conducted with two or three participants at different stages, this research examines the impact of the pricing strategy on profits. The findings indicate that platforms should only adopt the BBPD strategy in conditions of low-stage and low-oligopoly. This study aims to assist ride-hailing platforms in making informed decisions about the implementation of BBPD strategies for new and existing users, particularly in the context of regular or seasonal promotions.