# Driver Surge Pricing

arXiv: https://arxiv.org/abs/1905.07544 · HTML: https://mlsanity.com/paper/1905.07544/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2019.

## Built on by (most cited first, 4 of 4)

- [Policy Evaluation for Temporal and/or Spatial Dependent Experiments](https://mlsanity.com/paper/2202.10887/) (2022)
- [Minimax Optimal Estimation of Stability Under Distribution Shift](https://mlsanity.com/paper/2212.06338/) (2022)
- [Making the most of your day: online learning for optimal allocation of time](https://mlsanity.com/paper/2102.08087/) (2021)
- [Data-Driven Simulation of Ride-Hailing Services using Imitation and Reinforcement Learning](https://mlsanity.com/paper/2104.02661/) (2021)

## Abstract (arXiv)

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 (non-surge), and so 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.