Urban commuting pretty much sucks, no matter where you go: No matter the promise of high-speed trains, light rail and efficient bus routes, public transit always ends up feeling like the crammed-in people-mover tubes from Futurama.
That's partially because cities and counties don't have anything but the barest sense of what routes are in high demand and how outside events affect traffic and they're usually collecting data manually (from those guys you may see on the subway with a clipboard and a clicker, or handing out rider surveys). When it comes to improving transit, they're trying to hit a pinata while blindfolded.
Enter Urban Engines, an analytics startup founded by a two ex-Googlers, a Stanford graduate and a PhD, that plans to use the data users already generate every day to deliver a better, deeper view into cities and how people move around in them. Urban Engines calls its purview the "Internet of Moving Things," and it's all about taking an algorithmic approach to public transit.
The company's roots trace back to when CEO and co-founder Shiva Shivakumar was still at Google from 2001-2010, working on usability and figuring out how people actually move through websites. Over time, observing online user behaviors gave way to wanting to know more about how people navigate the physical world, too, something made easier in an age where location-based data is more readily available in larger quantities than ever before.
Shivakumar made a trip to Stanford University to recruit a founding team: Balaji Prabhakar, a professor of Electrical Engineering and Computer Science; ex-Google systems engineer Giao Nguyen; and Deepak Merugu, who has Bachelor's degree in Electrical Engineering from IIT Bombay and Master's and Ph.D. degrees in Electrical Engineering at Stanford. Urban Engines officially opening for business in May 2014.
The company's mission: to help "navigate the real world as efficiently as the digital one." It's venture backed and funded by firms and investors like Google Ventures, Andreessen-Horowitz, Ram Shiram and Google Chairman Eric Schmidt.
So, back to that data collection. Most of what is gathered by transit agencies is slow, making it hard to get anything resembling a current picture of how people are using local transit systems. Moreover, since each agency tends to keep its findings to itself, it's hard to see how, say, a traffic jam downtown might affect subway usage uptown.
"Train guys know what trains do, bus guys know what buses do," said Prabhakar.
Urban Engines is a little different: By constructing a map based on data provided by transit agencies, it can come up with a working model of the city that gives real-time visibility into what's going on. What's more, it doesn't require any special effort on the part of local government. The company relies on, as a major resource, the data generated by tap-in/tap-out NFC payment cards like the ones used on the Washington DC metro, where Urban Engines is presently engaged (along with Shanghai and San Paolo).
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