Dynamic Pricing Mechanisms and Impact Analysis in Ride-Hailing Platforms: A Study on Driving Factors, Typical Models, and Stakeholder Impacts

Abstract

The rapid growth of ride-hailing services such as Didi and Uber has transformed the urban transportation system, and it has become more accessible and flexible. Dynamic pricing or adjusting prices in real-time based on the market situation, is among the key strategies such platforms utilize to maximize operations. This study examines the dynamic pricing mechanisms employed by ride-hailing platforms, identifying the driving forces, typical pricing models, and the effects on platform operators, drivers, and clients. The research places emphasis on the role of big data, the behavior of consumers, market volatility, and competition in informing dynamic pricing approaches. Through the examination of several pricing models, such as supply-demand-based pricing, user-profile-based pricing, social interaction-based pricing, and time-based pricing, the research determines the benefits and risks of dynamic pricing. It reveals that while dynamic pricing can maximize platform efficiency and customer satisfaction, algorithm transparency and price oscillation should be addressed with caution. The research concludes by offering suggestions for improving dynamic pricing strategies, the long-run efficient operation of ride-hailing platforms.