Dynamic Pricing and Matching in Ride-Sharing | Dawn Woodard | WiDS 2018

Dawn Woodard, Senior Data Science Manager of Maps at Uber presents Dynamic Pricing and Matching in Ride-Sharing at the WiDS 2018 Conference held at Stanford University on March 5, 2018.

Ride-sharing 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. 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-sharing, because pricing too low causes pickup ETAs to get very long, which reduces the efficiency of the platform and causes a poor experience for riders and drivers. We review the literature on matching and pricing techniques in ride-sharing. We also discuss how to estimate several key inputs to those algorithms: predictions of demand, supply, and travel time in the road network.