4 Ways Uber Movement Data Can Be Used
Smart city planners rejoiced last week when Uber announced it would share trip data gathered from vehicles in its ridesharing fleet through its new Uber Movement offering. Besides helping to spot traffic bottlenecks in congested city cores, Uber Movement stands to bring benefits in other areas, too.
Uber last week committed to releasing anonymized vehicle trip data for 2 billion Uber trips through its Uber Movement project. The data currently covers just a fraction of the 450 cities that the ridesharing company operates in. But Uber is expected to release trip data for more cities in the months ahead.
Here are four potential uses of the Uber Movement Data:
Smart City Planning
Uber has already aggregated its trip data into Census Tracts and Traffic Analysis Zones (TAZs), which are data types that transportation planners use to evaluate how traffic flows through cities. That makes traffic optimization the most obvious use case for the Uber Movement data.
Traffic planning may seem like a relatively simple problem to solve. But it’s actually quite complex, combining physical elements of thermodynamics and psychological elements of human behavior. Having a solid corpus of time-series and geospatial data, as Uber intends to provide the public, could be a boon for smart city traffic planners as they seek to design roadways that optimize the flow of traffic.
For example, the data could help traffic planners to answer questions, such as whether a bridge or a tunnel is the solution for optimizing traffic flow, or if the accident-reducing benefit of a traffic circle outweighs the speed benefits of a traffic light in a given situation.
Optimized Food Delivery
The food-delivery business is booming in the United States, where it drives an estimated $30 billion annually, and has the potential to reach $210 billion, according to a recent Morgan Stanley study. Pizza delivery still dominates the fresh-food delivery niche, but urbanites are now getting other types of food delivered to them through outfits like GrubHub and PostMates.
The Uber Movement data could provide a competitive advantage to food delivery companies that can find ways to deliver food faster than their competitors, says Harry Glaser, CEO of San Francisco data visualization startup Periscope Data.
“I’m very hungry and I like pizza,” Glaser says. “If one of these companies can leverage this routing data to get me the pizza 10 minutes sooner, that company is going to win more business over time.”
It may seem odd for a pizza company to employ a data scientist. (They presumably would want to hire good pizza cooks, too). But the notion serves to demonstrate how far data has infiltrated many aspects of our lives.
Public Transportation Planning
Public transportation also stands to benefit from the Uber Movement data. By analyzing how long it takes for Uber vehicles to get from point A to point Z, public transportation planners could not only tweak their schedules to adapt to traffic planners. Because they’re so ubiquitous, Uber vehicles are reasonable proxies for the average commuter vehicle.
Public transportation planners could also use the data to tout the advantages of using public transportation that does not utilize congested roadways. While Ubers and private coaches sit in debilitating traffic, commuters riding light rail and ferry experience no delays.
There are some big gains to be made here. America’s drivers wasted 6.9 billion hours stuck in traffic in 2014, or about 42 hours per year per commuter, according to a study by the Texas A&M Transportation Institute (TTI) and INRIX. It’s no wonder that ridership on public transportation hit a 58-year high in 2014, when Americans took 10.8 billion trips using public transportation, according to the According to the American Public Transportation Association, Americans.
Retail Store Placement
Retailers are already savvy users of geospatial data, which they gather directly from customers who use their apps, or via third-parties that have deals with cellular phone companies. But the new Uber Movement data can provide another data layer to add to their analysis.
The data could alert retailers to potential store locations that might have been under-appreciated before, Glaser says. “Where it gets interesting is in store places, especially if there are traditionally poorly seen areas that we would traditionally think are bad, that actually get a fair amount of traffic,” he says. “I’m going to put a store here because lots of people are going by, and there’s plenty of parking. Even if it’s not traditionally considered a high-traffic downtown spot, that can be pretty cool.”
What are some other uses of the Uber Movement data? We may have to wait for Uber Movement data to be released to the public to find out. But Glaser is convinced there are likely some other informational gems in there that are just waiting to get unearthed.
“People think of data science as answering questions all day long, and data scientists certainly do a lot of that,” he says. “But some of the best data work comes in the free time…where you’re poking around the data set and trying to figure out what might be interesting here. I think people will want to load it up into their data visualizer of choice and just start to play with it, and see what shakes out. And I think we’ll all be surprised by what the trends are.”