001     1024166
005     20250204113816.0
024 7 _ |a 10.1093/pnasnexus/pgae120
|2 doi
024 7 _ |a 10.34734/FZJ-2024-01997
|2 datacite_doi
024 7 _ |a 38577258
|2 pmid
024 7 _ |a WOS:001196541200001
|2 WOS
037 _ _ |a FZJ-2024-01997
082 _ _ |a 600
100 1 _ |a Cordes, Jakob
|0 P:(DE-Juel1)187329
|b 0
|e Corresponding author
245 _ _ |a Dimensionless numbers reveal distinct regimes in the structure and dynamics of pedestrian crowds
260 _ _ |a Oxford
|c 2024
|b Oxford University Press
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1715081735_31030
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a 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.
536 _ _ |a 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)
|0 G:(DE-HGF)POF4-5111
|c POF4-511
|f POF IV
|x 0
536 _ _ |a DFG project 446168800 - Multi-Agent-Modellierung der Dynamik von dichten Fußgängermengen: Vorhersagen & Verstehen (446168800)
|0 G:(GEPRIS)446168800
|c 446168800
|x 1
588 _ _ |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de
700 1 _ |a Schadschneider, Andreas
|0 0000-0002-2054-7973
|b 1
700 1 _ |a Nicolas, Alexandre
|0 0000-0002-8953-3924
|b 2
773 _ _ |a 10.1093/pnasnexus/pgae120
|g p. pgae120
|0 PERI:(DE-600)3120703-0
|n 4
|p pgae120
|t PNAS nexus
|v 3
|y 2024
|x 2752-6542
856 4 _ |u https://juser.fz-juelich.de/record/1024166/files/Invoice_SOA24LT003220.pdf
856 4 _ |x icon
|u https://juser.fz-juelich.de/record/1024166/files/Invoice_SOA24LT003220.gif?subformat=icon
856 4 _ |x icon-1440
|u https://juser.fz-juelich.de/record/1024166/files/Invoice_SOA24LT003220.jpg?subformat=icon-1440
856 4 _ |x icon-180
|u https://juser.fz-juelich.de/record/1024166/files/Invoice_SOA24LT003220.jpg?subformat=icon-180
856 4 _ |x icon-640
|u https://juser.fz-juelich.de/record/1024166/files/Invoice_SOA24LT003220.jpg?subformat=icon-640
856 4 _ |y OpenAccess
|u https://juser.fz-juelich.de/record/1024166/files/pgae120.pdf
856 4 _ |y OpenAccess
|x icon
|u https://juser.fz-juelich.de/record/1024166/files/pgae120.gif?subformat=icon
856 4 _ |y OpenAccess
|x icon-1440
|u https://juser.fz-juelich.de/record/1024166/files/pgae120.jpg?subformat=icon-1440
856 4 _ |y OpenAccess
|x icon-180
|u https://juser.fz-juelich.de/record/1024166/files/pgae120.jpg?subformat=icon-180
856 4 _ |y OpenAccess
|x icon-640
|u https://juser.fz-juelich.de/record/1024166/files/pgae120.jpg?subformat=icon-640
909 C O |o oai:juser.fz-juelich.de:1024166
|p openaire
|p open_access
|p OpenAPC
|p driver
|p VDB
|p openCost
|p dnbdelivery
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)187329
913 1 _ |a DE-HGF
|b Key Technologies
|l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action
|1 G:(DE-HGF)POF4-510
|0 G:(DE-HGF)POF4-511
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Enabling Computational- & Data-Intensive Science and Engineering
|9 G:(DE-HGF)POF4-5111
|x 0
914 1 _ |y 2024
915 p c |a APC keys set
|0 PC:(DE-HGF)0000
|2 APC
915 p c |a DOAJ Journal
|0 PC:(DE-HGF)0003
|2 APC
915 _ _ |a Creative Commons Attribution-NonCommercial CC BY-NC 4.0
|0 LIC:(DE-HGF)CCBYNC4
|2 HGFVOC
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a Article Processing Charges
|0 StatID:(DE-HGF)0561
|2 StatID
|d 2023-08-31
915 _ _ |a Fees
|0 StatID:(DE-HGF)0700
|2 StatID
|d 2023-08-31
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2025-01-07
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2025-01-07
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0501
|2 StatID
|b DOAJ Seal
|d 2024-10-31T15:07:51Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0500
|2 StatID
|b DOAJ
|d 2024-10-31T15:07:51Z
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b DOAJ : Anonymous peer review
|d 2024-10-31T15:07:51Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2025-01-07
915 _ _ |a WoS
|0 StatID:(DE-HGF)0112
|2 StatID
|b Emerging Sources Citation Index
|d 2025-01-07
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2025-01-07
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)IAS-7-20180321
|k IAS-7
|l Zivile Sicherheitsforschung
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)IAS-7-20180321
980 _ _ |a APC
980 1 _ |a APC
980 1 _ |a FullTexts


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21