Pareto-efficient solutions and regulations of congested ride-sourcing markets with heterogeneous demand and supply

Ride-sourcing services have experienced dramatic growth over the past decade but aroused debates on whether and how the government should regulate the ride-sourcing platform. To tackle this critical issue, this paper investigates the regulatory outcomes of various representative government regulations, including price-cap regulation, vehicle fleet size control, wage (per-order) regulation, income (hourly earnings) regulation, car utilization rate regulation, commission charge regulation, etc. In particular, we try to answer two key questions: (1) whether a regulation leads to a Pareto-efficient outcome; (2) what are the impacts of the regulations on the platform's decisions and the resulting realized passenger demand and driver supply. By conducting theoretical and numerical studies, we offer some interesting and useful managerial insights for the government in designing appropriate regulations. Notably, some prevailing regulations, such as fleet size control and wage regulation, fail to achieve a Pareto-efficient outcome, while the maximum commission regulation and minimum service level (demand) regulation can achieve Pareto-efficient outcomes in markets with homogeneous drivers and mild traffic congestion. In addition, drivers’ heterogeneity and traffic congestion substantially affect the regulatory outcomes of various regulations. For example, in markets with homogeneous drivers, the income regulation does not take effect, while in markets with heterogeneous drivers, the income regulation does influence the platform's decisions but still cannot achieve a Pareto-efficient outcome. We also show that the government acts quite differently under mild or heavy traffic congestion: it tends to encourage more drivers to participate in the market by a minimum fleet size regulation when traffic congestion is light, but restrain the vehicle fleet size by a maximum fleet size regulation when traffic congestion is severe.