# Driver Surge Pricing

## TL;DR

This paper develops a theoretical framework for driver surge pricing in ride-hailing services, showing that additive surge mechanisms are more incentive-compatible than traditional multiplicative ones, supported by numerical and real data analysis.

## Contribution

It introduces a dynamic stochastic model for surge pricing, demonstrating the incentive compatibility of additive surge mechanisms over multiplicative ones in ride-hailing.

## Key findings

- Additive surge is more incentive-compatible than multiplicative surge.
- The proposed mechanism closely matches Uber's new additive surge.
- Numerical and real data confirm the practical advantages of additive surge.

## 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 (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.