Abstract
Increasing attention is being paid to the pricing decisions of ride-hailing platforms. These platforms usually face market demand fluctuation and reflect supply and demand imbalances. Unlike existing studies, we focus on the optimal dynamic pricing of the platforms under imbalance between supply and demand caused by market fluctuation. Dynamic models are constructed based on the state change of supply and demand by using optimal control theory, with the aim of maximizing the platform’s total profit. We obtain the optimal trajectories of price, supply, and demand under three ride demand situations. The effects of some key parameters on pricing decisions, such as coefficient of demand fluctuation, service quality, and fixed commission rate, are examined. We find the optimal dynamic price can improve the match of supply-demand in ride-hailing market and enhance the revenue of platform.
Highlights
As an innovation of the mobile Internet era, ride-hailing platforms (e.g., Uber, Lyft, and DiDi) have played a significant role in improving vehicle use efficiency, increasing transportation service supply, facilitating taxiing, and promoting employment
We consider the characteristics of demand fluctuation into the models, in view of the practices of such platforms as DiDi and Uber that face the ride-hailing demand vary dynamically with time at different periods. e optimal dynamic price trajectory of ride-hailing platforms are investigated, and we find out the key time points that affect the platform optimization pricing under different circumstances, so as to make optimal pricing decisions for different ride time periods be more practical guiding
According to the modeling and the analysis above, we summarize in Table 2, the properties of the optimal dynamic prices in the three scenarios, where ↑ indicates the variable is increasing over time, ⟶ indicates the variable is stable or unchanged over time, and ↓ indicates the variable is decreasing over time
Summary
As an innovation of the mobile Internet era, ride-hailing platforms (e.g., Uber, Lyft, and DiDi) have played a significant role in improving vehicle use efficiency, increasing transportation service supply, facilitating taxiing, and promoting employment. E fluctuation of ride-hailing demand based on specific reasons was related to more people wanting to use the service (e.g., time of day, price, and service quality), and the supply of services usually be influenced by factors such whether there are drivers available, geographic areas, commissions etc. Erefore, we take into account the service quality and the fixed commission rate of the ride-hailing platforms, the opportunity cost when supply exceeds demand, and the booking delay cost when supply fails to meet demand in time. Us, we use optimal control theory to construct models and study the dynamic pricing of ride-hailing platforms with different market demand situations in a finite service time [0, T]. (3) How do the coefficient of demand fluctuation, service quality, and fixed commission rate affect pricing decisions, transaction volumes, and profits under different market demand situations?
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