网约车平台的动态定价策略

在现实情况下，网约车市场具有随机性，平台静态定价策略无法对系统中司机供给和乘客需求的瞬时不平衡做出反应。从政府和网约车平台双重角度出发，针对平台、司机和乘客三方面，研究了基于社会福利最大化及利润最大化的平台动态定价策略。利用排队论及生灭过程理论描述了司机在网约车平台中的流动过程，并引入乘客和司机心理预期价格曲线，构建了动态定价策略下的社会福利最大化和平台利润最大化模型；应用布劳威尔不动点定理考察模型解的存在性；基于序列二次规划法设计算法求解模型，得出实现网约车系统社会福利最大化和利润最大化的平台定价及司机分成，并进行对比。研究表明，所建立的模型不仅能计算平台的最优定价策略，还能分析基本参数（平均乘坐时间、乘客/司机心理预期价格分布等）的变化对平台定价策略的影响。

Dynamic pricing strategy of ride-hailing platforms

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.