Dynamic Pricing and Matching in Ride-Hailing Platforms

October 1, 2018 / Global

Abstract

Ride-hailing platforms such as Uber, Lyft and DiDi have achieved explosive growth and reshaped urban transportation. The theory and technologies behind these platforms have become one of the most active research areas in the fields of economics, operations research, computer science, and transportation engineering. In particular, advanced matching and dynamic pricing algorithms — the two key levers in ride-hailing — have received tremendous attention from the research community and are continuously being designed and implemented at industrial scales by ride-hailing platforms. We first review matching and dynamic pricing techniques in ride-hailing, and show that these are critical for providing an experience with low waiting time for both riders and drivers. Then we link the two levers together by studying a pool-matching mechanism called dynamic waiting that varies rider waiting and walking before dispatch, which is inspired by a recent carpooling product Express Pool from Uber. We show using data from Uber that by jointly optimizing dynamic pricing and dynamic waiting, price variability can be mitigated, while increasing capacity utilization, trip throughput, and welfare. We also highlight several key practical challenges and directions of future research from a practitioner’s perspective.