Abstract: In reality, the ride-hailing market is characterized by randomness, and the static pricing strategy of the platform cannot adjust to instantaneous imbalances in the system between available driver supply and passenger demand. To this end, from the dual perspective of the government and ride-hailing platforms, the platform dynamic pricing strategy based on social welfare and profit maximization is studied in terms of the platform, drivers, and passengers. The queuing theory and the birth-death process are used to describe the flow process of drivers in ride-hailing platforms, and the social welfare maximization and platform profit maximization model under dynamic pricing is constructed by introducing the psychological expected price curve of passengers and drivers. The Brouwer fixed-point theorem is used to examine the existence of the model solution. Based on the sequential quadratic programming method, the algorithm is used to solve the model, and the platform pricing and driver profit-sharing to maximize social welfare and platform profit of the ride-hailing system are obtained and compared. Research shows that the established model not only calculates the optimal pricing strategy of the platform but also analyzes the impact of changes in basic parameters (average ride time, expected passenger/driver psychological price distribution, etc.) on the pricing strategy of the platforms.

Key words: transportation economy, dynamic pricing, queuing theory, ride-hailing, social welfare