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• W2835464704 abstract "The balletic motion of bird flocks, fish schools, and human crowds is believed to emerge from local interactions between individuals, in a process of self-organization. The key to explaining such collective behavior thus lies in understanding these local interactions. After decades of theoretical modeling, experiments using virtual crowds and analysis of real crowd data are enabling us to decipher the 'rules' governing these interactions. Based on such results, we build a dynamical model of how a pedestrian aligns their motion with that of a neighbor, and how these binary interactions are combined within a neighborhood in a crowd. Computer simulations of the model generate coherent motion at the global level and reproduce individual trajectories at the local level. This approach yields the first experiment-driven, bottom-up model of collective motion, providing a basis for understanding more complex patterns of crowd behavior in both everyday and emergency situations." @default.
• W2835464704 created "2018-07-19" @default.
• W2835464704 creator A5040366534 @default.
• W2835464704 date "2018-07-11" @default.
• W2835464704 modified "2023-10-14" @default.
• W2835464704 title "Collective Motion in Human Crowds" @default.
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• W2835464704 doi "https://doi.org/10.1177/0963721417746743" @default.
• W2835464704 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6121818" @default.
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• W2835464704 hasPublicationYear "2018" @default.
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