Dawn Woodard (Uber) - Dynamic Pricing and Matching for Ride-Hailing
in
Workshop: Real-world Sequential Decision Making: Reinforcement Learning and Beyond
Abstract
Ride-hailing platforms like Uber, Lyft, Didi Chuxing, and Ola have achieved explosive growth, in part by improving the efficiency of matching between riders and drivers, and by calibrating the balance of supply and demand through dynamic pricing. We survey methods for dynamic pricing and matching in ride-hailing, and show that these are critical for providing an experience with low waiting time for both riders and drivers. We also discuss approaches used to predict key inputs into those algorithms: demand, supply, and travel time in the road network. 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.