Sometimes you call an Uber, and what you thought would be an $8 ride is going to be two, three, even four times more — the result of greater demand brought on by a blizzard, or a baseball game. Whatever the reason, surge pricing is not fun.
It turns out Uber is working to fix it — or, should we say, end it. The move likely will be great for riders, but not for drivers.
While drivers see surge as a key feature of the job — and Uber advertises it as such to them — inside the company surge pricing is considered a market failure, a problem to be solved.
“That’s where machine learning comes in. That’s where the next generation comes in,” says Jeff Schneider, engineering lead at Uber Advanced Technologies Center. “Because now we can look at all this data, and we can start to make predictions.”
Schneider grants me an onstage interview at a Silicon Valley insider-conference called Structure Data. It is a well-known fact that, in the long-term, Uber is working on self-driving cars with no need for human drivers. I’m interviewing Schneider about his short-term priorities, and killing surge rates is top of the list.
Think of it this way: When a Beyoncé concert lets out, it’s a no-brainer that there’s a ton of demand. Drivers know that. What’s harder, Schneider explains, “is to find those Tuesday nights when it’s not even raining and for some reason there’s demand — and to know that’s coming. That’s machine learning.”
With enough of the right data inputs, computer algorithms can do the research that driver Nathan Sapp already does — only better, “so the surge pricing never even has to happen,” Schneider says.
Drivers would be informed of the extra demand. “And I think that’s one of the really cool things that machine learning’s doing for Uber right now,” Schneider says.
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