Journal Article FZJ-2024-01997

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Dimensionless numbers reveal distinct regimes in the structure and dynamics of pedestrian crowds

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2024
Oxford University Press Oxford

PNAS nexus 3(4), pgae120 () [10.1093/pnasnexus/pgae120]

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Abstract: In Fluid Mechanics, dimensionless numbers like the Reynolds number help classify flows. We argue that such a classification is also relevant for crowd flows by putting forward the dimensionless Intrusion and Avoidance numbers, which quantify the intrusions into the pedestrians’ personal spaces and the imminency of the collisions that they face, respectively.Using an extensive dataset, we show that these numbers delineate regimes where distinct variables characterize the crowd's arrangement, namely, Euclidean distances at low Avoidance number and times-to-collision at low Intrusion number. On the basis of these findings, a perturbative expansion of the individual pedestrian dynamics is carried out around the non-interacting state, in quite general terms. Simulations confirm that this expansion performs well in its expected regime of applicability.

Classification:

Contributing Institute(s):
  1. Zivile Sicherheitsforschung (IAS-7)
Research Program(s):
  1. 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) (POF4-511)
  2. DFG project 446168800 - Multi-Agent-Modellierung der Dynamik von dichten Fußgängermengen: Vorhersagen & Verstehen (446168800) (446168800)

Appears in the scientific report 2024
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Medline ; Creative Commons Attribution-NonCommercial CC BY-NC 4.0 ; DOAJ ; OpenAccess ; Article Processing Charges ; Clarivate Analytics Master Journal List ; DOAJ Seal ; Emerging Sources Citation Index ; Fees ; SCOPUS ; Web of Science Core Collection
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Open Access

 Datensatz erzeugt am 2024-03-20, letzte Änderung am 2025-02-04


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