How simple rules determine pedestrian behavior and crowd disasters
Authored by Guy Theraulaz, Mehdi Moussaid, Dirk Helbing
Date Published: 2011
DOI: 10.1073/pnas.1016507108
Sponsors:
French National Research Agency (ANR)
French National Center for Scientific Research (CNRS)
Platforms:
No platforms listed
Model Documentation:
Other Narrative
Mathematical description
Model Code URLs:
Model code not found
Abstract
With the increasing size and frequency of mass events, the study of
crowd disasters and the simulation of pedestrian flows have become
important research areas. However, even successful modeling approaches
such as those inspired by Newtonian force models are still not fully
consistent with empirical observations and are sometimes hard to
calibrate. Here, a cognitive science approach is proposed, which is
based on behavioral heuristics. We suggest that, guided by visual
information, namely the distance of obstructions in candidate lines of
sight, pedestrians apply two simple cognitive procedures to adapt their
walking speeds and directions. Although simpler than previous
approaches, this model predicts individual trajectories and collective
patterns of motion in good quantitative agreement with a large variety
of empirical and experimental data. This model predicts the emergence of
self-organization phenomena, such as the spontaneous formation of
unidirectional lanes or stop-and-go waves. Moreover, the combination of
pedestrian heuristics with body collisions generates crowd turbulence at
extreme densities-a phenomenon that has been observed during recent
crowd disasters. By proposing an integrated treatment of simultaneous
interactions between multiple individuals, our approach overcomes
limitations of current physics-inspired pair interaction models.
Understanding crowd dynamics through cognitive heuristics is therefore
not only crucial for a better preparation of safe mass events. It also
clears the way for a more realistic modeling of collective social
behaviors, in particular of human crowds and biological swarms.
Furthermore, our behavioral heuristics may serve to improve the
navigation of autonomous robots.
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Decision-Making
Mechanisms