Shared or Solo? Platform Pricing and Rider Choices in Ride-Hailing

As ride-sharing becomes an integral part of ride-hailing platforms, understanding its operational and economic implications is crucial. Although shared rides can improve system efficiency and reduce congestion, they also introduce trade-offs, such as longer wait times and rider discomfort. In this paper, we develop a game-theoretic queueing model to examine how a platform operating with one of three objectives, i.e., volume, revenue, or social welfare maximization, sets prices for solo and shared rides, while self-interested riders decide whether to request a ride and, if so, whether to share. Our analysis uncovers a counterintuitive pricing pattern in stark contrast to the standard pricing theory: a platform that aims to maximize volume or social welfare may charge higher prices for both solo and shared rides than when it pursues revenue maximization. Because ride-sharing expands service capacity and improves society-wide service access (particularly for riders not so sensitive to the discomfort of sharing), a volume-maximizing platform and a social planner tend to induce more ride-sharing than a revenue-centric platform. Increased ride-sharing, in turn, further relieves platform congestion, decreases riders’ average wait times, and boosts their willingness to pay for both shared and solo services. That said, a platform may shut down shared services in small markets with low arrival rates of riders, as the extended rider-pairing process can make the entire system less efficient than one with only solo services. Finally, although ride-sharing always (weakly) enhances a specific targeted performance metric desired by the platform, its effect on rider welfare is more nuanced. Under volume or revenue maximization, ride-sharing expands service access and benefits riders overall. However, under social welfare maximization, it may reduce total rider surplus, as the platform restricts service access to prevent excessive discomfort borne by sharing riders from outweighing the overall gains in the platform’s operational efficiency. We calibrate the model and illustrate our insights using the Chicago ride-hailing data. This paper was accepted by Karan Girotra, operations management. Funding: This research was supported by the Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2021-04295]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.04324 .