Smartphones to accelerate urban planning

August 30, 2016 // By Julien Happich
By harnessing the anonymous locations of the smartphones that nearly everyone carries nowadays Ford Motor Company and the Massachusetts Institute of Technology (MIT) believe they can plan a better future, at least for urban areas, with near realtime analytics.

In the Proceedings of the National Academy of Sciences — the world's most cited general scientific journal — Ford and MIT demonstrated today [Aug. 29, 2016] how a mere six weeks of historical cell phone location data could be nearly instantly analyzed to provide optimal plans for infrastructure development and resource allocation that city planners might take years to sift out.

"The great advantage of our framework is that it learns mobility features from a large number of mobile phone users, without having to ask them directly about their mobility choices. Based on that we create individual models to estimate complete daily trajectories of the vast majority of mobile phone users," professor Marta Gonzalez at MIT told EE Times in an exclusive interview in advance of the announcement today. "Likely, in time, we will see that this brings the comparative advantage of making urban transportation planning faster and smarter, and even allowing to communicate the recommendations directly to the devise users."

By giving EE Times advance notice of its breakthrough analytics, Ford and MIT were probably expecting an article crammed with buzzwords like Big Data, Crowdsourcing and Disruptive Technologies. The significance of their feat, however, is more important than stringing together tech-talk to describe it. City planners today get paid six-figure salaries to provide this caliber of accurate commuter surveys (which they usually farm out to consultancies which charge them seven-figure prices). By feeding Ford and MIT's algorithms the realtime anonymous data already available from cellphone carriers, the years- to even decade-long urban planning cycles are over.

Ford and MIT use smartphone "where-about" tracking data to plan urban societies at the scale of cities and their surrounding regions in hours instead of months or years. (Source: MIT)