The current disruptive trends—autonomous driving, connectivity, the electrification of vehicles, and shared mobility—are altering traditional thinking and changing the way we travel for our daily activities. My PhD thesis is primarily directed towards one typical type of shared mobility ⎯ ride-sourcing service, which has experienced dramatic growth over the past decade and also aroused many interesting research questions. From the perspective of the platform, the question is how to design suitable operating strategies in terms of pricing (collected from passengers), waging (paid to drivers), and matching (between drivers and passengers), to maximize its own profit. From the perspective of the government, the question is how to induce the platform to voluntarily choose a targeted...[ Read more ]

The current disruptive trends—autonomous driving, connectivity, the electrification of vehicles, and shared mobility—are altering traditional thinking and changing the way we travel for our daily activities. My PhD thesis is primarily directed towards one typical type of shared mobility ⎯ ride-sourcing service, which has experienced dramatic growth over the past decade and also aroused many interesting research questions. From the perspective of the platform, the question is how to design suitable operating strategies in terms of pricing (collected from passengers), waging (paid to drivers), and matching (between drivers and passengers), to maximize its own profit. From the perspective of the government, the question is how to induce the platform to voluntarily choose a targeted Pareto-efficient strategy that well balances the interests or benefits of multiple stakeholders, including passengers, drivers and the platform.

These questions are not easy to answer since the ride-sourcing market is a two-sided market with its demand and supply interacting with each other in a complex manner. To address these issues, this thesis establishes several economic models that can well approximate the matching frictions between drivers and passengers and then describe the equilibrium state of ride-sourcing markets. Based on these models, we then seek out the monopoly optimum strategy (in terms of trip fare, wage and/or matching) that maximizes the platform profit. While the first-best social optimum solution (for maximizing the social welfare) is generally unsustainable, we show that the government can induce the platform to voluntarily choose a second-best solution by imposing some appropriate regulations, such as commission cap.

In addition, we establish some models to analyze the ride-sourcing markets with ridepooling services, which pool-match two or more passengers in some rides. Besides, traffic congestion externalities are incorporated into the models to see how the platform and government designs optimal operating strategies in response to the level the traffic congestion. Apart from analyzing effects of operating strategies on stationary equilibrium state, we also discuss how the matching time interval and matching radius affect the efficiency of the ride-sourcing system in the real-time matching process. Our analyses and results offer valuable operations insights for the ride-sourcing platform and policy insights for the government/regulators.