Dynamic pricing and matching in ride‐hailing platforms

TLDR

Ride‑hailing platforms such as Uber, Lyft, and DiDi have grown explosively, reshaping urban transportation and becoming a hot research topic across economics, operations research, computer science, and transportation engineering, with advanced matching and dynamic pricing algorithms receiving intense attention. The authors review matching and dynamic‑pricing techniques, arguing that they are essential for delivering low rider and driver wait times. They introduce a pool‑matching mechanism called dynamic waiting that adjusts rider waiting and walking before dispatch, inspired by Uber’s Express Pool product. Using Uber data, they show that jointly optimizing dynamic pricing and dynamic waiting reduces price variability while boosting capacity utilization, trip throughput, and welfare, and they outline key practical challenges and future research directions.

Abstract

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 topics in the fields of economics, operations research, computer science, and transportation engineering. In particular, advanced matching and dynamic pricing (DP) 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 provide a review of matching and DP techniques in ride‐hailing, and show that they 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 (DW) 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 DP and DW, 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.