Model Predicts Mob Behavior
In this screenshot, the crowd flees a burning car, toward an evacuation point on the right-hand-side of the screen. Narrow streets form a bottleneck that constrains the densely-packed crowd.
Credit: Paul Torrens, Arizona State University

Scientists who want to see how a crowd behaves in an emergency can’t exactly shout “Fire!” on a city street and watch everyone panic and run. But a newly developed computer model can.

The 3-D model starts with patterns of human behavior and movement and uses them to simulate the behavior of a crowd in mob situations and pedestrian habits under certain building configurations, resulting in a virtual crowd video.

“Crowds are vital to the lifeblood of our cities,” said the model’s creator, Paul Torrens of Arizona State University. But, he adds, it is impractical “to establish live experiments with hundreds or thousands of people along busy streetscapes.”

Torrens’s model uses what he calls an “agent-based methodology.” He can put individual people, or “agents,” each with different characteristics of age, sex, size and health, into the model and have them process information about the world around them. Their unique characteristic combinations make the agents interpret that information and react in different ways.

“It’s the same way we process information in the real world,” Torrens said.

Though you’re not aware of it, when you walk down a crowded street, your brain is constantly monitoring your surroundings and planning the path you will take to your destination while simultaneously monitoring for obstacles.

A prototype that Torrens has developed models the evacuation of a crowded area during a fire when there is only one point of escape, but he has used his method to develop a primitive model of a situation in which a disease spreads through casual contact, and he is attempting to create scenarios in which agitated crowds turn into unruly mobs.

The model could also be used in planning cities to optimize walking space for pedestrians and alleviate congestion.