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 on public transportation. Those models are largely based on surveys of residents’ travel habits.
But conducting surveys and analyzing their results is costly and time consuming: A city might go more than a decade between surveys. And even a broad survey will cover only a tiny fraction of a city’s population.
In the latest issue of the Proceedings of the National Academy of Sciences, researchers from MIT and Ford Motor Company describe a new computational system that uses cellphone location data to infer urban mobility patterns. Applying the system to six weeks of data from residents of the Boston area, the researchers were able to quickly assemble the kind of model of urban mobility patterns that typically takes years to build.
The system holds the promise of not only more accurate and timely data about urban mobility but the ability to quickly determine whether particular attempts to address cities’ transportation needs are working.
To validate their new system, the researchers compared the model it generated to the model currently used by Boston’s MPO. The two models accorded very well.
