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

Abstract

As ride-sharing becomes an integral part of ride-hailing platforms, understanding its operational and economic implications is crucial. While 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 under one of three objectives---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. The rationale is that because ride-sharing expands service capacity and improves society-wide service access for riders not so sensitive to the discomfort of sharing in particular), a volume-maximizing platform and a social planner tend to induce more ride-sharing than a revenue-centric platform, which 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 forgo shared services completely given any operational objective 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, while 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 born by sharing riders from outweighing the overall gains in the platform's operational efficiency. We calibrate the model and apply our insights to the ride-hailing market in Chicago.

Keywords: Sharing Economy, Ride-Hailing, Ride-Sharing, Queuing Economics