Surge pricing also boosts supply, at least locally. The extra money is shared with drivers, who therefore have an incentive to travel to areas with high demand to help relieve the crush.
A recent analysis published by Uber illustrates how the system is intended to work. Jonathan Hall, head of economic research at Uber, Cory Kendrick, a data scientist at the firm, and Chris Nosko, of the University of Chicago, compared two high-demand cases in New York city to illustrate how surge pricing is intended to work. In March 2015 it kicked in after a sold-out concert by Ariana Grande, a singer, in an arena in the middle of Manhattan. As the show came to an end, the number of people in the area opening the Uber app quadrupled in just a few minutes. Uber's algorithm swiftly applied surge pricing; the average waiting time for a car rose only modestly, while the “completion rate”—the share of requests for rides that are met—never fell below 100%. On New Year's Eve in 2014, in contrast, Uber's surge-pricing algorithm broke down for 26 minutes, leaving New York without surge pricing. The average wait time for a car soared from about two minutes to roughly eight, while the completion rate dropped below25% (see chart).
The comparison may overstate the power of surge pricing. Even without the help of algorithms, cab drivers know to converge on a venue as an event finishes; more Uber drivers than normal were surely in the area at the end of Ms Grande's concert in expectation of the extra business. Yet the possibility of earning a surge fare may also strengthen drivers' incentives to anticipate and respond pre-emptively to high demand. Ironically, the better Uber's surge-pricing algorithm works, the less the company will need to use it, since drivers' pre-emptive responses will tend to eliminate the demand imbalances that make surge pricing necessary in the first place.
There are tantalising hints that Uber hopes to follow this logic to its conclusion. Mr Schneider noted that clever machine-learning tools could process Uber's piles of data and determine when and where demand is likely to outstrip the supply of cars. There would be no need to wait until demand starts to rise, nor for drivers to scan concert schedules. The ability to anticipate demand would be of some use to Uber today: it could tell drivers where they are likely to be needed. But they would presumably not respond as rapidly as they do to the inducement of surge fares. Eventually, however, Uber hopes to replace its human drivers with autonomous vehicles, which could be directed around the city by the company's computers without any pecuniary incentives. (The company still has an incentive to maximise earnings, though, so it might opt to keep surge pricing even if technology made it redundant, at the risk of further public rage.)
Apps and downs
Whether Uber remains a big part of the transport network in future, and whether it retains surge pricing, depends in part on how well local governments manage the transport system as a whole. In districts or cities where travellers have appealing alternatives, in the form of good public transport or private competitors to Uber, users will be more sensitive to price. Surge pricing will therefore not generate a big financial windfall for Uber (or its drivers). But where public transport is thin on the ground, or where Uber has little private competition, it is a different story. In other words, surge pricing is really only as painful as local officials allow it to be.