Driver Positioning and Incentive Budgeting with an Escrow Mechanism for Ride-Sharing Platforms

Abstract

Drivers on the Lyft ride-share platform do not always know where the areas of supply shortage are in real time. This lack of information hurts both riders trying to find a ride and drivers trying to determine how to maximize their earnings opportunities. Lyft’s Personal Power Zone (PPZ) product helps the company to maintain high levels of service on the platform by influencing the spatial distribution of drivers in real time via monetary incentives that encourage them to reposition their vehicles. The underlying system that powers the product has two main components: (1) a novel “escrow mechanism” that tracks available incentive budgets tied to locations within a city in real time, and (2) an algorithm that solves the stochastic driver-positioning problem to maximize short-run revenue from riders’ fares. The optimization problem is a multiagent dynamic program that is too complicated to solve optimally for our large-scale application. Our approach is to decompose it into two subproblems. The first determines the set of drivers to incentivize and where to incentivize them to position themselves. The second determines how to fund each incentive using the escrow budget. By formulating it as two convex programs, we are able to use commercial solvers that find the optimal solution in a matter of seconds. Rolled out to all 320 cities in which Lyft operates in a little more than a year, the system now generates millions of bonuses that incentivize hundreds of thousands of active drivers to optimally position themselves in anticipation of ride requests every week. Together, the PPZ product and its underlying algorithms represent a paradigm shift in how Lyft drivers drive and generate earnings on the platform. Its direct business impact has been a 0.5% increase in incremental bookings, amounting to tens of millions of dollars per year. In addition, the product has brought about significant improvements to the driver and rider experience on the platform. These include statistically significant reductions in pick-up times and ride cancellations. Finally, internal surveys reveal that the vast majority of drivers prefer PPZs over the legacy system.

A Unified Representation Framework for Rideshare Marketplace Equilibrium and Efficiency

“…The supply dual 𝑤 𝑖,𝑡 represents the marginal effect on 𝜌 of adding one driver to the (𝑖, 𝑡) position. The 𝑤 variables can offer guiding analytics for supply positioning strategies (e.g., [15,18,20]) because they indicate where drivers are needed the most and where we may reposition drivers from. The right-hand plot of Figure 1 shows visualization of the supply duals distribution.…”

“…There are extensive works in the ridesharing literature on marketplace optimization to improve a rideshare platform's performance, typically in market equilibrium and efficiency. Some recent works cover algorithms for dispatch [9,12,24,25,29,34], repositioning [5,7,15,18,20], pricing [2,8,14,19,30], and incentives [23,28]. There have also been works focusing on investigations and analyses of the driving factors for rideshare market efficiency [1,11,13,17].…”