Modeling crowd behavior can help engineers design buildings and other public spaces so as to prevent deaths and injuries during emergencies. But it is hard to design virtual crowds that realistically mimic real ones.

European researchers have now shown that a simple model based on one cognitive factor—vision—can predict pedestrian behavior in various types of crowds. It represents significant progress in a field that has been trying to move away from purely physics-based models.

"There's no clear way to describe the cognitive processes of each individual, but with this vision-based approach, it's actually very simple," says Dirk Helbing, of the Swiss Federal Institute of Technology in Zurich, who carried out the work with Mehdi Moussaïd and Guy Theraulaz, of Université Paul Sabatier in Toulouse, France.

The study, which appears in this week's issue of Proceedings of the National Academy of Sciences, was inspired by previous research that used eye-tracking data to determine how people predict the trajectory of an airborne ball in order to catch it. Numerous other studies have suggested that walking, like catching a ball, is primarily governed by vision. So the researchers hypothesized that using visual factors, mainly line of sight and visibility, would allow them to better model crowd behavior.

The researchers gave virtual crowd members the ability to "see" their surroundings and navigate accordingly. They found that their vision-based model predicted pedestrian behavior surprisingly well for both small and large crowds as long as the physical influence of the crowd as a whole was also considered. They suggest that the model could help avert such crowd disasters as the Love Parade incident that killed 19 concertgoers in Germany last summer, by providing designers with new information about how pedestrians will attempt to move quickly through a specific space.

The model primarily indicates how vision affects pedestrians' direction and speed—two forces that often compete when a person is navigating pedestrian traffic. The researchers predicted pedestrian trajectories using the model and then compared their predictions with data from real-life pedestrian scenarios. They found the trajectories matched up almost exactly.