Matching and Dynamic Pricing in Ride-Hailing Platforms


May 1, 2018


Dawn Woodard


Uber Maps


Ride-hailing platforms like Uber, Lyft, Didi Chuxing, and Ola are transforming urban mobility by connecting riders with drivers via the sharing economy. These platforms have achieved explosive growth, in part by dramatically improving the efficiency of matching, and by calibrating the balance of supply and demand through dynamic pricing. We survey methods for matching and dynamic pricing in ride-hailing, and discuss machine learning and statistical approaches used to predict key inputs into those algorithms: demand, supply, and travel time in the road network.

The dynamic adjustment of prices ensures a reliable service for riders, and incentivizes drivers to provide rides at peak times and locations. Dynamic pricing is particularly important for ride-hailing, because pricing too low causes pickup ETAs to get very long, which reduces the efficiency of the platform and provides a poor experience for riders and drivers. Pricing and matching are intimately connected; we show that flexing wait time for riders during high-demand time periods can reduce the price variability caused by dynamic pricing.


Dawn Woodard

Dr. Dawn Woodard leads data science for Uber Maps, which is the mapping platform used in Uber’s rider app, driver app, and decision systems (such as pricing and dispatch). The team’s technologies include road map and points of interest definition, map search, route optimization, travel time prediction, and navigation. Dr. Woodard received her PhD in statistics from Duke University, after which she became a faculty member in the School of Operations Research and Information Engineering at Cornell. There she developed forecasting methods for ambulance decision support systems, in collaboration with several ambulance organizations. After receiving tenure at Cornell, she joined Microsoft Research for her sabbatical, where she created travel time prediction methods for use in Bing Maps. She then transitioned to a role at Uber, building and leading their Marketplace Optimization Data Science organization. The team creates Uber’s marketplace-related technologies, such as dispatch, pricing, and incentives. It is now one of the premier data science teams at Uber and includes specialists in operations research, economics, statistics, and machine learning. She recently transitioned to her current role in Maps.