Paper Info
Title
Pricing in Ride-Sharing Platforms: A Queueing-Theoretic Approach
Abstract
We study optimal pricing strategies for ride-sharing platforms, using a queueing-theoretic economic model. 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 very high temporal-resolution for data collection and pricing - this requires stochastic models that capture the dynamics of drivers and passengers in the system. 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 profit under any dynamic pricing strategy cannot exceed profit 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. Moreover, these results hold even if the monopolist maximizes welfare or throughput. 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.
Year
DOI
Venue
2015
10.1145/2764468.2764527
Evolutionary Computation
Field
DocType
Citations 
Mathematical optimization,Rational pricing,Dynamic pricing,Computer science,Investment theory,Microeconomics,Consumption-based capital asset pricing model,Average cost pricing,Variable pricing,Pricing strategies,Arbitrage pricing theory
Conference
24
PageRank 
References 
Authors
1.35
2
3
Name
Order
Citations
PageRank
Siddhartha Banerjee118522.85
Ramesh Johari21484116.25
Carlos Riquelme3353.29