Economic modeling and analysis of the ride-sourcing market considering labor supply

Mobile technology development has led to the upsurge of app-based ride-sourcing platforms on the market. This paper analyzes the pricing strategies of ride-sourcing platforms. Economic modeling is used to propose an aggregate model, which includes the demand, labor supply, and the matching between demand and supply. The interactive relationship between labor supply and drivers' revenue is explicitly considered. The market is ultimately modeled as a system of nonlinear equations. We prove the existence and stability of market equilibrium and propose an approximate iterative algorithm to solve for the equilibrium. The algorithm is verified through a numerical experiment. A partial-derivative-based sensitivity analysis is conducted to quantitatively evaluate the impacts of platform pricing strategies on ride-sourcing market performance, based on which we examine desirable price perturbations from the perspectives of customers, drivers, and the platform, respectively. The triple-win Pareto improvement condition is also numerically analyzed.