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

Banerjee, Siddhartha; Riquelme, Carlos; Johari, Ramesh

doi:10.2139/ssrn.2568258

Cited by 189 publications

(184 citation statements)

References 13 publications

Supporting

Mentioning

178

Contrasting

Unclassified

Order By: Relevance

“…This gives N I = N − λ/u = 2041. Cities with limited supply of drivers (as in US suburbs and in cities like Singapore) require a distinct model [18], [27].…”

Section: Matching Passengers and Drivers: M/g/n Queuementioning

confidence: 99%

“…A recurrent concern is to evaluate decisions that maximize platform profit, with particular attention to static vs. dynamic pricing. A queuing model is proposed in [18] to study the profit maximizing prices of ride-hailing platforms. It shows that the throughput and profit under dynamic pricing strategy can not exceed that under the optimal static pricing strategy that is agnostic to stochastic dynamics of demands.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Regulating TNCs: Should Uber and Lyft set their own rules?

Tavafoghi

Poolla

et al. 2019

Transportation Research Part B: Methodological

106

View full text Add to dashboard Cite

We evaluate the impact of three proposed regulations of transportation network companies (TNCs) like Uber, Lyft and Didi: (1) a minimum wage for drivers, (2) a cap on the number of drivers or vehicles, and (3) a per-trip congestion tax. The impact is assessed using a queuing theoretic equilibrium model which incorporates the stochastic dynamics of the app-based ride-hailing matching platform, the ride prices and driver wages established by the platform, and the incentives of passengers and drivers. We show that a floor placed under driver earnings pushes the ride-hailing platform to hire more drivers and offer more rides, at the same time that passengers enjoy faster rides and lower total cost, while platform rents are reduced. Contrary to standard economic theory, enforcing a minimum wage for drivers benefits both drivers and passengers, and promotes the efficiency of the entire system. This surprising outcome holds for almost all model parameters, and it occurs because the wage floors curbs TNC labor market power. In contrast to a wage floor, imposing a cap on the number of vehicles hurts drivers, because the platform reaps all the benefits of limiting supply. The congestion tax has the expected impact: fares increase, wages and platform revenue decrease. We also construct variants of the model to briefly discuss platform subsidy, platform competition, and autonomous vehicles.

show abstract

“…This gives N I = N − λ/u = 2041. Cities with limited supply of drivers (as in US suburbs and in cities like Singapore) require a distinct model [18], [27].…”

Section: Matching Passengers and Drivers: M/g/n Queuementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Regulating TNCs: Should Uber and Lyft set their own rules?

Tavafoghi

Poolla

et al. 2019

Transportation Research Part B: Methodological

106

View full text Add to dashboard Cite

show abstract

“…Celik and Maglaras (2008), Ata and Olsen (2009), and Kim and Randhawa (2015) each define a model with a platform that serves stochastically arriving price sensitive consumers, but with fixed capacity. Banerjee, Riquelme, and Johari (2015) considers the value of dynamic pricing in a model with random arrivals of consumers and providers. Providers join based on their expectation of long run earnings (as in our model) and are paid a fixed commission of the consumer price (whereas we consider other wage structures).…”

Section: Literature Reviewmentioning

confidence: 99%

The Role of Surge Pricing on a Service Platform with Self-Scheduling Capacity

2016

View full text Add to dashboard Cite

Recent platforms, like Uber and Lyft, offer service to consumers via "self-scheduling" providers who decide for themselves how often to work. These platforms may charge consumers prices and pay providers wages that both adjust based on prevailing demand conditions. For example, Uber uses a "surge pricing" policy, which pays providers a fixed commission of its dynamic price. With a stylized model that yields analytical and numerical results, we study several pricing schemes that could be implemented on a service platform, including surge pricing. We find that the optimal contract substantially increases the platform's profit relative to contracts that have a fixed price or fixed wage (or both), and although surge pricing is not optimal, it generally achieves nearly the optimal profit. Despite its merits for the platform, surge pricing has been criticized because of concerns for the welfare of providers and consumers. In our model, as labor becomes more expensive, providers and consumers are better off with surge pricing because providers are better utilized and consumers benefit both from lower prices during normal demand and expanded access to service during peak demand. We conclude, in contrast to popular criticism, that all stakeholders can benefit from the use of surge pricing on a platform with self-scheduling capacity. Abstract Recent platforms, like Uber and Lyft, offer service to consumers via "self-scheduling" providers who decide for themselves how often to work. These platforms may charge consumers prices and pay providers wages that both adjust based on prevailing demand conditions. For example, Uber uses a "surge pricing" policy, which pays providers a fixed commission of its dynamic price. We find that the optimal contract substantially increases the platform's profit relative to contracts that have a fixed price or fixed wage (or both) and although surge pricing is not optimal, it generally achieves nearly the optimal profit. Despite its merits for the platform, surge pricing has been criticized in the press and has garnered the attention of regulators due to concerns for the welfare of providers and consumers. However, we find that providers and consumers are generally better off with surge pricing because providers are better utilized and consumers benefit both from lower prices during normal demand and expanded access to service during peak demand. We conclude, in contrast to popular criticism, that all stakeholders can benefit from the use of surge pricing on a platform with self-scheduling capacity.

show abstract

“…A number of papers have studied various peer-to-peer platforms where customers decide to buy or rent the products (Benjaafar et al 2015, Fraiberger and Sundararajan 2015, Jiang and Tian 2016. Recent developments of ride-sharing platforms have also motivated research in the matching mechanism, wage, and price of on-demand services (Banerjee et al 2015, Cachon et al 2017, Hu and Zhou 2016, Tang et al 2016, Taylor 2016. In an EV-sharing system, the high fixed costs of EV ownership are transformed into a usage-based cost of service.…”

Section: Literature Reviewmentioning

confidence: 99%

Service Region Design for Urban Electric Vehicle Sharing Systems

Mak

Rong

et al. 2017

M&SOM

211

View full text Add to dashboard Cite

Emerging collaborative consumption business models have shown promise in terms of both generating business opportunities and enhancing the efficient use of resources. In the transportation domain, car-sharing models are being adopted on a mass scale in major metropolitan areas worldwide. This mode of servicized mobility bridges the resource efficiency of public transit and the flexibility of personal transportation. Beyond the significant potential to reduce car ownership, car sharing shows promise in supporting the adoption of fuel-efficient vehicles, such as electric vehicles (EVs), because of these vehicles' special cost structure with high purchase but low operating costs. Recently, key players in the car-sharing business, such as Autolib', car2go, and DriveNow, have begun to employ EVs in an operations model that accommodates one-way trips. On the one hand (and particularly in free-floating car sharing), the one-way model results in significant improvements in coverage of travel needs and therefore in adoption potential compared with the conventional round-trip-only model (advocated by Zipcar, for example). On the other hand, this model poses tremendous planning and operational challenges. In this work, we study the planning problem faced by service providers in designing a geographical service region in which to operate the service. This decision entails trade-offs between maximizing customer catchment by covering travel needs and controlling fleet operation costs. We develop a mathematical programming model that incorporates details of both customer adoption behavior and fleet management (including EV repositioning and charging) under imbalanced travel patterns. To address inherent planning uncertainty with regard to adoption patterns, we employ a distributionally robust optimization framework that informs robust decisions to overcome possible ambiguity (or lacking) of data. Mathematically, the problem can be approximated by a mixed integer second-order cone program, which is computationally tractable with practical scale data. Applying this approach to the case of car2go's service with real operations data, we address a number of planning questions and suggest that there is potential for the future development of this service.

show abstract

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

Cited by 189 publications

References 13 publications

Regulating TNCs: Should Uber and Lyft set their own rules?

Regulating TNCs: Should Uber and Lyft set their own rules?

The Role of Surge Pricing on a Service Platform with Self-Scheduling Capacity

Service Region Design for Urban Electric Vehicle Sharing Systems

Contact Info

Product

Resources

About