Summary: A new study suggests that human crowd behavior is best predicted by a visual neighborhood model, based on the visual fields of each person in the crowd.
Researchers experiments with real and virtual crowds, finding that this model offered a more accurate prediction of crowd dynamics than other mathematical models.
These findings could have significant implications for managing crowd flow and preventing dangerous situations like jams, crushes, or stampedes. A visual model could promise more realistic simulations of crowd dynamics, according to the researchers.
Key Facts:
- The study compared different models of predicting crowd behavior and found the visual neighborhood model, based on the visual fields of individuals, to be the most accurate.
- In dense crowds, near neighbors may block the view of more distant neighbors, impacting the overall crowd movement.
- This new understanding of crowd dynamics can help in preventing dangerous crowd situations and in creating more realistic simulations.
Source: PNAS Nexus
Human crowd dynamics are best predicted by a visual neighborhood model, based on the visual fields of each person in the crowd. Birds flock, fish school, and human crowds, too, move in a collective motion pattern.
Understanding human crowd behavior can be useful for preventing jams, crushes, and stampedes.
Mathematical models of collective motion are typically based on characterizing the local interactions between individuals. One popular approach, called a metric model, is to quantify forces of attraction, repulsion, and velocity alignment for all neighbors within a fixed radius from the focal individual.
Alternatively, in a topological model the focal individual might be influenced by a fixed number of near neighbors, regardless of the distance to the focal individual.
Trenton Wirth and colleagues asked participants to walk in real and virtual crowds of varying densities, then changed the walking direction of some neighbors to see how the participants responded.
The authors found that the data produced was better predicted by the metric model than by the topological model. But the best model was based on the visual motions of the neighbors the focal individual could see.
In dense crowds, near neighbors may partially or completely block the view of more distant neighbors, removing the distant neighbors from the focal pedestrian’s input.
Pursuing a visual model promises more realistic simulations of crowd dynamics, according to the authors.
About this neuroscience research news
Author: Trenton D. Wirth
Source: PNAS Nexus
Contact: Trenton D. Wirth – PNAS Nexus
Image: The image is credited to Neuroscience News
Original Research: Open access.
“Is the neighborhood of interaction in human crowds topological, metric, or visual?” by Trenton D. Wirth et al. PNAS Nexus
Abstract
Is the neighborhood of interaction in human crowds topological, metric, or visual?
Global patterns of collective motion in bird flocks, fish schools, and human crowds are thought to emerge from local interactions within a neighborhood of interaction, the zone in which an individual is influenced by their neighbors.
Both metric and topological neighborhoods have been reported in animal groups, but this question has not been addressed for human crowds.
The answer has important implications for modeling crowd behavior and predicting crowd disasters such as jams, crushes, and stampedes.
In a metric neighborhood, an individual is influenced by all neighbors within a fixed radius, whereas in a topological neighborhood, an individual is influenced by a fixed number of nearest neighbors, regardless of their physical distance.
A recently proposed alternative is a visual neighborhood, in which an individual is influenced by the optical motions of all visible neighbors. We test these hypotheses experimentally by asking participants to walk in real and virtual crowds and manipulating the crowd’s density.
Our results rule out a topological neighborhood, are approximated by a metric neighborhood, but are best explained by a visual neighborhood that has elements of both.
We conclude that the neighborhood of interaction in human crowds follows naturally from the laws of optics and suggest that previously observed “topological” and “metric” interactions might be a consequence of the visual neighborhood.
