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Northeastern researchers unlock details of Uber’s surge pricing—and suggest ways to avoid it

Uber CEO Travis Kalanick works with fourth graders during Cooking Matters,   a nutrition class taught by 18 Reasons,   a local partner of Share our Strength at Glen Park Elementary School in San Francisco

A research team led by Northeastern assistant professor Christo Wilson cracked open the algorithm that drives Uber’s surge pricing. The team examined ‘surge regions’ within cities and reports findings that reveal tips on how consumers might avoid the inflated prices. Photo by Matthew Moodono/Northeastern University

You’re in Manhattan’s Times Square, run­ning late for dinner at Le Cirque, on East 58th St. You open the Uber app on your smart­phone, hoping a car from the now ubiq­ui­tous ride-​​sharing ser­vice is nearby, only to dis­cover that you’ll have to pay 1.5 times the base rate for the ride.

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New research, led by Christo Wilson, assis­tant pro­fessor in the Col­lege of Com­puter and Infor­ma­tion Sci­ence, unlocks details behind the algo­rithm that drives this surge pricing. Based on data from Man­hattan, Wilson’s team also has a fix:

Wait five min­utes, or walk a few short blocks, and the surge noti­fi­ca­tion may disappear.


Under the Uber hood

On Thursday, at the 2015 Internet Mea­sure­ment Con­fer­ence, in Tokyo, Wilson revealed that and more of Uber’s secrets.

Wilson and his co-​​authors, Le Chen, PhD’16, and asso­ciate pro­fessor Alan Mis­love, had long been trou­bled by Uber’s “lack of trans­parency.” Other sharing mar­ket­places, such as Ebay and AirBnB, openly dis­play their prod­ucts and prices online, enabling cus­tomers to make informed choices. Uber, on the other hand, oper­ates in the dark: It releases no num­bers about how many people are requesting cars or how many dri­vers are avail­able, and prices vary wildly, based on time and place of cus­tomers’ requests.

You have to trust that their system is doing what they say it’s doing, ” says Wilson, whose schol­ar­ship focuses on auditing algo­rithms. “But that’s the ques­tion: Is it doing what they claim?”

Well, yes…and no, the researchers found.

To bal­ance supply and demand during periods of high usage, or “surges, ” Wilson says, Uber uses “an opaque ‘surge pricing’ algo­rithm” that changes fares every five min­utes. And it divides the cities it ser­vices into “dis­crete ‘surge areas.’” The con­flu­ence of ric­o­cheting prices and dis­cretely defined areas leads, the team found, to an unusual—and unfair—scenario: “corner cases, ” says Wilson, “where you can walk across the street and all of a sudden the price changes.”

Times Square, their data show, is one of those corner cases. “For example, ” they write in the paper, “20 per­cent of the time in Times Square, cus­tomers can save 50 per­cent or more by being in an adja­cent surge area.”

What exactly are surge areas? They are man­u­ally demar­cated sec­tions of a par­tic­ular city, each with its own inde­pen­dent price based on inten­sity of demand at a par­tic­ular point in time. The maps of the surge areas look like funky jigsaw puz­zles: uneven pieces with mostly sharp edges locked together. Boston, for instance, has nine surge areas. Man­hattan: 16. London: 19. Still, says Wilson, how Uber divided up the cities is not clear.


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Becoming Uber

To con­duct the research, Wilson and his col­leagues did more than look under Uber’s hood; they essen­tially crawled inside its com­puter systems.

Using servers in a closet on the North­eastern campus, they pro­grammed and ran Uber apps “pre­tending” to be people at 43 dif­ferent GPS loca­tions throughout San Fran­cisco and Man­hattan over a four-​​week period. The researchers chose San Fran­cisco and Man­hattan for sev­eral rea­sons, including their having, respec­tively, the second and third largest number of Uber dri­vers in the U.S. and large dif­fer­ences in access to public transportation.

If you’re a cus­tomer, it can pay to wait or walk.
— Christo Wilson, North­eastern Uni­ver­sity assis­tant professor

The data they col­lected included the surge price—that is, the number by which the base price was mul­ti­plied during surges—and esti­mated wait time for each “ride” as well as the loca­tion of the “request.” Crunching the data, they tracked supply and demand, how those dynamics changed over time and dis­tance, and the way surge prices varied by location.

We did a lot of cor­re­la­tion analysis looking at how many cars were get­ting booked over time and how many cars were avail­able, and you do see high cor­re­la­tion between supply and demand and the surge, ” says Wilson. “So the system is def­i­nitely responding to supply and demand changes.”


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Still, there is room for manip­u­la­tion, on the part of both dri­vers and customers.

The data wasn’t con­clu­sive regarding dri­vers. But Uber driver forums, Wilson reports, con­tained con­ver­sa­tions about “collusion”—drivers in a spe­cific area uniting to go offline to arti­fi­cially reduce supply and thus lead to a price surge. “The dri­vers talk about this, but we don’t have any evi­dence that this actu­ally works, ” says Wilson.

For cus­tomers, how­ever, the find­ings were explicit. “If you’re a cus­tomer, it can pay to wait or walk, ” says Wilson. He notes, how­ever, that you can’t know where to walk to or how far if you don’t have a detailed surge-​​area map in hand—and Uber is unlikely to ever pro­vide one.

How­ever, Wilson and his team will. “Sit­ting on my com­puter is all the research we’re doing in this vein, ” he says. “We have a web­site for it, and even­tu­ally we’ll have a page about the paper that’s acces­sible to the public. We are devel­oping surge maps, and will put all of them there there,  too.”

This article was first published at,  



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