By collecting the anonymous cellphone location data from nearly two million Bostonians, MIT and Ford were able to produce near-instant urban mobility patterns that typically cost millions of dollars and take years to build.
The big data experiment holds the promise of more accurate and timely data about urban mobility patterns that can be used to quickly determine whether particular attempts to address local transportation needs are working.
In making decisions about infrastructure development and resource allocation, city planners rely on models of how people move through their cities -- on foot, in cars and by public transportation. Those models are largely based on socio-demographic information from costly, time-consuming manual surveys, which are in small sample sizes and infrequently updated. Cities might go more than a decade between surveys.
"In the U.S., every metropolitan area has a...metropolitan planning organization [MPO], and their main job is to use travel surveys to derive the travel demand model, which is their baseline for predicting and forecasting travel demand to build infrastructure," said Shan Jiang, a postdoctoral student in MIT's Human Mobility and Networks Lab. "So our method and model could be the next generation of tools for the planners to plan for the next generation of infrastructure."
The paper, titled TimeGeo: modeling urban mobility without travel surveys, describes how the researchers used call detailed records (CDRs) managed by mobile phone service providers. The CDRs, which are used for billing purposes, contain data in the form of geolocated traces of users across the globe.
Snapshot of the urban mobility simulation by the TimeGeo model at 9am, bird's-eye view of Boston. For visualization purposes, only 1% of the 3.5 million individuals living in the Boston Metro are displayed in the video.
The researchers collected a CDR data set of 1.92 million anonymous mobile phone users for a period of six weeks in the Greater Boston area. To have a control experiment, they also examined a donated set of self-collected mobile phone traces of a graduate student in the same region over a course of 14 months, recorded by a smartphone application.
By applying a big data algorithm the CDR data, the researchers were able to quickly assemble the kind of model of urban mobility patterns that typically takes years to build.
The Boston MPO's practices are fairly typical of a major cities. Boston conducted one urban mobility survey in 1994 and another in 2010. Its current mobility model, however, still uses data from 1994 because it's taken the intervening six years simply to sort through all the information collected in 2010. Only now has the work of organizing that newer data into a predictive model begun, the researchers explained.
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