Driver Surge Pricing

Abstract

Ride-hailing marketplaces like Uber and Lyft use dynamic pricing, often called surge, to balance the supply of available drivers with the demand for rides. We study driver-side payment mechanisms for such marketplaces, presenting the theoretical foundation that has informed the design of Uber’s new additive driver surge mechanism. We present a dynamic stochastic model to capture the impact of surge pricing on driver earnings and their strategies to maximize such earnings. In this setting, some time periods (surge) are more valuable than others (nonsurge), and therefore trips of different time lengths vary in the induced driver opportunity cost. First, we show that multiplicative surge, historically the standard on ride-hailing platforms, is not incentive compatible in a dynamic setting. We then propose a structured, incentive-compatible pricing mechanism. This closed-form mechanism has a simple form and is well approximated by Uber’s new additive surge mechanism. Finally, through both numerical analysis and real data from a ride-hailing marketplace, we show that additive surge is more incentive compatible in practice than is multiplicative surge. This paper was accepted by David Simchi-Levi, revenue management and market analytics.

Real-Time Spatial–Intertemporal Pricing and Relocation in a Ride-Hailing Network: Near-Optimal Policies and the Value of Dynamic Pricing

“…Our paper is motivated by the ride-hailing systems and is thus closely connected to a growing body of literature on online platforms which provide different kinds of services (e.g., Uber for urban mobility, Upwork for professional freelancer services, Deliveroo for take-away food delivery) to customers using a pool of independent service agents. Some of the early papers that study these platforms focus on the impact of self-scheduling service agents on platform's operational decisions such as pricing (e.g, prices for customers and wages to service agents) and the resulting welfare implications via either analytical models (Cachon et al 2017, Castillo et al 2017, Taylor 2018, Chen and Hu 2019, Fang et al 2019, Bai et al 2019, Yan et al 2019, Garg and Nazerzadeh 2021, Guda and Subramanian 2019 or empirical approach (Ata et al 2019). However, since many papers in this stream of literature focus on strategic market design questions, they tend to abstract away from some features which are unique to ride-hailing systems such as the fact that the number of available drivers across different regions at any given time of the day are not always well-distributed compared to demands.…”

Fly with social platform? Two-sided platform cooperation and pricing strategy considering same-side crowding out

“…This indicated that the authentication services provided by the platform will result in a situation where the benefits are less than the costs. Garg and Nazerzadeh [15] studied the impact of such pricing on drivers' earnings and their strategies based on the current dynamic pricing mechanism in the online taxi market, and found that additive surges can have a wider range of incentive effects in practice. Devalve and Pekec [11] argued that consumers may derive negative utility from advertisements that media platforms are not fully aware of.…”

Optimizing the economic and environmental benefits of ride‐hailing and pooling

“…Profit‐maximizing strategies for monopolistic platforms that match rider demand and driver supply can improve consumer surplus and social welfare, depending on competition, prices, and the number of customers (Zhong et al., 2019). The efficiency of matching can be improved through various operational levers, such as trips distance limits (Feng et al., 2021) and surge prices that directly affect drivers’ behavior and strategies to maximize earnings (Garg & Nazerzadeh, 2021; Henao & Marshall, 2019a; H. Sun et al., 2019). Besides pricing, the capacity of a ride‐hailing platform can be managed by blending full‐time drivers and independent contractors (Chakravarty, 2021).…”