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Drivers on the Lyft rideshare platform do not always know where the areas ofsupply shortage are in real time. This lack of information hurts both riderstrying to find a ride and drivers trying to determine how to maximize theirearnings opportunity. Lyft's Personal Power Zone (PPZ) product helps thecompany to maintain high levels of service on the platform by influencing thespatial distribution of drivers in real time via monetary incentives thatencourage them to reposition their vehicles. The underlying system that powersthe product has two main components: (1) a novel 'escrow mechanism' that tracksavailable incentive budgets tied to locations within a city in real time, and(2) an algorithm that solves the stochastic driver positioning problem tomaximize short-run revenue from riders' fares. The optimization problem is amultiagent dynamic program that is too complicated to solve optimally for ourlarge-scale application. Our approach is to decompose it into two subproblems.The first determines the set of drivers to incentivize and where to incentivizethem to position themselves. The second determines how to fund each incentiveusing the escrow budget. By formulating it as two convex programs, we are ableto use commercial solvers that find the optimal solution in a matter ofseconds. Rolled out to all 320 cities in which Lyft's operates in a little overa year, the system now generates millions of bonuses that incentivize hundredsof thousands of active drivers to optimally position themselves in anticipationof ride requests every week. Together, the PPZ product and its underlyingalgorithms represent a paradigm shift in how Lyft drivers drive and generateearnings on the platform. Its direct business impact has been a 0.5% increasein incremental bookings, amounting to tens of millions of dollars per year.

Driver Positioning and Incentive Budgeting with an Escrow Mechanism for Ridesharing Platforms