Optimizing the Outcomes of Ride-Hailing Platforms: Unresolved Challenges and New Insights

Abstract

This dissertation focuses on ride-hailing platforms such as Didi, Grab, Lyft, and Uber, innovative on-demand online services that quickly match riders with drivers. Specifically, it investigates how platforms optimize their outcomes under varying internal policies and environmental factors while considering stakeholder behavior. The research is divided into two studies. Chapter 2 reviews 89 publications published in operations and supply chain management journals about challenges that ride-hailing platforms face in day-to-day operations. An organizational framework is presented to synthesize the central research questions addressed in the existing literature, with these central research questions generally focusing on the impact that interactions among stakeholders and environmental factors have on ride-hailing platform outcomes, rider experience, and driver welfare. The organizational framework classifies the 89 publications into five main themes and identifies future research directions. One important research direction is the need for more research into mechanisms (e.g., information sharing) that encourage desired driver behavior, particularly their relocation decisions across zones to balance supply and demand. Chapter 3 investigates how sharing information about the proportions of ride requests, drivers, and average ride distances between zones affects (i) regret-averse drivers’ decisions to relocate and (ii) matching efficiency and the platform’s profitability. Of particular interest is whether information sharing can substitute for monetary compensation, the extent of this effect, and the conditions under which different information is most effective. Using a two-period Stackelberg game, we compare various information-sharing strategies against a baseline model that only shares surge multipliers, with the objective of either maximizing the platform’s profit or supply-demand matching efficiency. Our findings affirm that surge pricing is beneficial when there is an imbalance between supply (i.e., idle drivers) and demand (i.e., ride requests). Importantly, information can generally serve as a substitute for financial incentives, with its effectiveness depending on the degree of imbalance between idle drivers and ride requests and shared information. Higher relocation costs, moreover, can further amplify the benefit of sharing more information.