Pricing in Ride-Share Platforms: A Queueing-Theoretic Approach

54 Pages Posted: 24 Feb 2015 Last revised: 18 Jan 2016

Siddhartha Banerjee

Cornell University - School of Operations Research and Information Engineering

Carlos Riquelme

Stanford University - Institute for Computational and Mathematical Engineering

Ramesh Johari

Stanford University

Date Written: February 10, 2015

Abstract

We study optimal pricing strategies for ride-sharing platforms, such as Lyft, Sidecar, and Uber. Analysis of pricing in such settings is complex: On one hand these platforms are two-sided -- this requires economic models that capture the incentives of both drivers and passengers. On the other hand, these platforms support high temporal-resolution for data collection and pricing -- this requires stochastic models that capture the dynamics of drivers and passengers in the system.

In this paper we build a queueing-theoretic economic model to study optimal platform pricing. In particular, we focus our attention on the value of dynamic pricing: where prices can react to instantaneous imbalances between available supply and incoming demand. We find two main results: We first show that performance (throughput and revenue) under any dynamic pricing strategy cannot exceed that under the optimal static pricing policy (i.e., one which is agnostic of stochastic fluctuations in the system load). This result belies the prevalence of dynamic pricing in practice. Our second result explains the apparent paradox: we show that dynamic pricing is much more robust to fluctuations in system parameters compared to static pricing. Thus dynamic pricing does not necessarily yield higher performance than static pricing -- however, it lets platforms realize the benefits of optimal static pricing, even with imperfect knowledge of system parameters.

Keywords: ride-sharing, dynamic pricing, matching markets, queueing networks

Suggested Citation

Banerjee, Siddhartha and Riquelme, Carlos and Johari, Ramesh, Pricing in Ride-Share Platforms: A Queueing-Theoretic Approach (February 10, 2015). Available at SSRN: https://ssrn.com/abstract=2568258 or http://dx.doi.org/10.2139/ssrn.2568258

Siddhartha Banerjee (Contact Author)

Cornell University - School of Operations Research and Information Engineering ( email )

237 Rhodes Hall
Ithaca, NY 14853
United States

Carlos Riquelme

Stanford University - Institute for Computational and Mathematical Engineering ( email )

Huang Building, 475 Via Ortega
Suite 060 (Bottom level)
Stanford, CA 94305-4042
United States

Ramesh Johari

Stanford University ( email )

473 Via Ortega
Stanford, CA 94305-9025
United States

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