A physicist at the Indian Institute of Space Science and Technology has developed a model that could explain how ants handle traffic congestion better than humans.

Above: Ants march across a fallen tree in Tanjung Puting National Park, West Kalimantan, Indonesia | Photo by Daniel Murdiyarso for Center for International Forestry Research (CIFOR) | CC BY-NC-ND 2.0

Physicist Apoorva Nagar came up with his model after he read a study that showed ants can maintain a steady flow of traffic, even as the number of ants along a path increases. His model adds to the growing list of physical and mathematical theories that attempt to explain traffic jams and curb their emergence. Here's Joe Palca for NPR:

Nagar says there are three main reasons ants don't jam up. No. 1, ants don't have egos. They don't show off by zooming past people.

"The second thing is, they do not mind a few accidents or collisions," say Nagar. So unless there's a serious pileup, they just keep going.

The third reason, he says, is that ants seem to get more disciplined when paths get crowded, running in straighter lines and varying their speed less. They're less likely to make unexpected moves in this sort of heavy traffic. It's the kind of steady control you see when a computer, rather than a human, is controlling a car. There's less variability unless it's absolutely called for.

While we can't exactly have people bumping into one another on the highway, there is evidence that self-driving and connected cars could go a long way in reducing highway congestion.

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Palca also add that the extent to which marching ants are better than driving humans at avoiding traffic jams is a little unclearin the first place. "Nagar is a physicist, not an ant man," he hedges. "I've talked with ant researchers who say that, for at least some species of ants, one will overtake another on the ant highway. And when the volume of ants is high enough, ants do jam up."

Still, you've gotta hand it to ants on the collective-intelligence front. If their marching skills are anything like their capacity for teamwork, we could probably stand to learn something from them.

Nagar's model relies in large part on the Langevin equation. More details at NPR.