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.

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