Metro's 29 hour shutdown led to an Uber boom, with a 70 percent increase over the previous week in people signing up for the app as the shutdown drew near. While many worried about sky-high fares, they rarely materialized due to a combination of ride-splitting (the company expanded its UberPool ride-sharing platform to cover the entire region, not just DC), more drivers on the road, and many people in the area just staying home. Uber’s policy of “surge pricing” is, at this point, as ubiquitous as the company itself. The car-hailing service increases its rates when demand for rides outpaces the number of available drivers. In theory, this surge in pricing should encourage more Uber drivers to get on the road bringing prices closer to the standard rate.
New data show in practice where, when, and how much price surging occurs throughout the District. Select a neighborhood on the map below to show the average rate for an UberX any hour across the week within the selected neighborhood from February 3 to March 2.
Nearly all of the District experiences a price surge during the morning commute. Surging is most common and highest downtown, while least common along the outer ring of the city. As Wonkblog reports, neighborhoods with more people of color have fewer periods of surge pricing, but also wait longer for a driver to arrive. While weekdays at 8am is a particular painpont across the entire city, weekend surging is more variable by neighborhood. Areas with a well known nightlife, like Logan Circle and Shaw, see a price surge around 2am Sunday as the bars empty. Quieter neighborhoods like Eastern Market see a similar surge, but hours earlier before midnight. Upper northwest has an Uber surge when most people start their evening, around 6pm. For many parts of the city, Uber riders consider the late ride home part of the bar tab.
Technical notes: Data shown are from the Uber API. The data was collected by Jennifer A. Stark and Nick Diakopoulos over a month long period from 276 DC locations every three minutes. You can read more about the data, and their findings, here. You can find complete code for this post on my github page.