How simple rules determine pedestrian behavior and crowd disasters

By Mehdi Moussaïd, Dirk Helbing, Guy Theraulaz. Proceedings of the National Academy of Sciences of the United States of America, 2011

1,153 citations recorded by the source corpus.

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.

Subfields: Building and Construction, Computer Vision and Pattern Recognition, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/34c595a2faabde4ce8838a66049fe09bd4e389a1

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Citations in the corpus

Swarm Robotics: Past, Present, and Future (2021, background): “3072740 Swarm robotics started out as an application of swarm intelligence [3], [4], that is, the computational modeling of collective, self-organizing behavior that has resulted in several successful optimization algorithms [5], [6] now being used in fields ranging from telecommunications [7] to si”
Models Are Stupid, and We Need More of Them (2017, methodology): “Such models, if sufficiently precise, may utilize data for validation and calibration (e.g. Schank 2008; Moussaïd et al. 2011; Hills et al. 2012), but this is not strictly necessary for such models to be useful (Wimsatt 1987; Bedau 1999; Epstein 2008; Gunawardena 2014).”
Deciphering Interactions in Moving Animal Groups (2012, background): “However, recent work has suggested that this is not valid when describing pedestrian interactions in a crowd [38].”
Human mobility: Models and applications (2017, background): “A second modification to the social force model deals with the cognitive capacities and limitations of pedestrians and how they can affect the force terms of the model [245,246].”
Crowd disasters as systemic failures: analysis of the Love Parade disaster (2012, background): “[30] (figure from the Supporting Information).”
Information and Computation (2013, background): “crowd dynamics, information Hij is shared through the environment: acustically, visually (Moussäıd et al., 2011), stigmergically (Doyle and Marsh, 2013), etc.”
Saving Human Lives: What Complexity Science and Information Systems can Contribute (2014, neutral): “self-organized phenomenon characterized by alternating moving and stopping phases, which is visible to the naked eye [27, 28].”