Inferring urban travel patterns from cellphone data

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.
 
“In the U.S., every metropolitan area has an MPO, which is a metropolitan planning organization, 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,” says Shan Jiang, a postdoc in the Human Mobility and Networks Lab in MIT’s Department of Civil and Environmental Engineering and first author on the new paper. “So our method and model could be the next generation of tools for the planners to plan for the next generation of infrastructure.”
 
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.
 
“The great advantage of our framework is that it learns mobility features from a large number of users, without having to ask them directly about their mobility choices,” says Marta González, an associate professor of civil and environmental engineering (CEE) at MIT and senior author on the paper. “Based on that, we create individual models to estimate complete daily trajectories of the vast majority of mobile-phone users. Likely, in time, we will see that this brings the comparative advantage of making urban transportation planning faster and smarter and even allows directly communicating recommendations to device users.”

---