Smartphones have changed the way we drive, both by adding new distractions and by helping us get where we're going with GPS-assisted directions and real-time information on traffic jams.

But what if smartphones could help eliminate some traffic jams, instead of just warning us when they exist? That's the goal of a study using cell phone records and GPS data to track drivers' movements and identify the sources of traffic.

The Boston Globe described the study today, noting that MIT and UC-Berkeley analyzed the cell phone records of 680,000 Boston-area commuters through call logs, "which identify the towers used to transmit calls," allowing "the researchers to trace each individual’s commute, anonymously, from origin to destination." This helped produce "one of the most detailed maps of urban traffic patterns ever constructed."

The study was published in December in the journal Scientific Reports, and described in announcements by MIT and Berkeley. Such cell phone tracking helps bring traffic analysis patterns into the modern age, with statistics being constantly updated instead of becoming constantly outdated.

"This is the first large-scale traffic study to track travel using anonymous cellphone data rather than survey data or information obtained from U.S. Census Bureau travel diaries," Berkeley's announcement said. Studies chronicled traffic both in Boston and San Francisco.

In Boston, it turned out traffic jams are caused by just a few drivers in the grand scheme of things, the Globe noted. "What they found, perhaps surprisingly, is that during rush hour, 98 percent of roads in the Boston area were in fact below traffic capacity, while just 2 percent of roads had more cars on them than they could handle," the Globe wrote. "The backups on these roads ripple outward, causing traffic to snarl across the Hub."

Moreover, "By tracking the cell records, they found that it’s just a small number of drivers from a small number of neighborhoods who are responsible for tying up the key roads. Specifically, they identified 15 census tracts (out of the 750 in Greater Boston) located in Everett, Marlborough, Lawrence, Lowell, and Waltham as the heart of the problem, because drivers from those areas make particularly intensive use of the problematic roads in the system."

This data doesn't offer any overnight solution to traffic congestion, but it may help city planners and public transportation officials better target their resources. Focusing on problem neighborhoods might be the key.

As MIT described, the adoption of alternatives like public transportation, carpooling, flex time, and working from home can be effective in reducing traffic if undertaken by a small number of people in certain problem areas.

The study demonstrated that "canceling or delaying the trips of 1 percent of all drivers across a road network would reduce delays caused by congestion by only about 3 percent," MIT wrote. " But canceling the trips of 1 percent of drivers from carefully selected neighborhoods would reduce the extra travel time for all other drivers in a metropolitan area by as much as 18 percent."

The effectiveness of this "selective strategy" is attributed in the study to the facts that "only [a] few road segments are congested" and that these road segments are clogged by people originating largely from only a few areas. Even though data was anonymous, researchers were able to infer drivers' home neighborhoods "from the regularity of the route traveled and from the locations of cell towers that handled calls made between 9 p.m. and 6 a.m," UC-Berkeley said.

Boston and San Francisco have "radically different commute patterns," UC-Berkeley noted. "In Boston, the freeways spread radially outward to the suburbs, with concentric rings of freeways intersecting them like a spider web. In San Francisco, the freeways encircle San Francisco Bay and are connected by six bridges."

Yet the key takeaway, that changing the habits of a very small group of drivers in certain areas is the most effective method of reducing traffic, remained the same in both Boston and San Francisco.

Since only three types of data—population density, topological information about road networks, and cell phone data—are needed, the same study could be repeated in almost any urban area.