001     818075
005     20210129224101.0
024 7 _ |a 10.17815/CD.2016.4
|2 doi
024 7 _ |a 2128/12344
|2 Handle
024 7 _ |a altmetric:12229241
|2 altmetric
037 _ _ |a FZJ-2016-04609
082 _ _ |a 380
100 1 _ |a Ezaki, Takahiro
|0 P:(DE-HGF)0
|b 0
245 _ _ |a Inflow Process of Pedestrians to a Confined Space
260 _ _ |a Köln
|c 2016
|b Institut für Theoretische Physik
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 1474453812_32723
|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 To better design safe and comfortable urban spaces, understanding the nature of human crowd movement is important. However, precise interactions among pedestrians are difficult to measure in the presence of their complex decision-making processes and many related factors. While extensive studies on pedestrian flow through bottlenecks and corridors have been conducted, the dominant mode of interaction in these scenarios may not be relevant in different scenarios. Here, we attempt to decipher the factors that affect human reactions to other individuals from a different perspective. We conducted experiments employing the inflow process in which pedestrians successively enter a confined area (like an elevator) and look for a temporary position. In this process, pedestrians have a wider range of options regarding their motion than in the classical scenarios; therefore, other factors might become relevant. The preference of location is visualized by pedestrian density profiles obtained from recorded pedestrian trajectories. Non-trivial patterns of space acquisition, e.g., an apparent preference for positions near corners, were observed. This indicates the relevance of psychological and anticipative factors beyond the private sphere, which have not been deeply discussed so far in the literature on pedestrian dynamics. From the results, four major factors, which we call flow avoidance, distance cost, angle cost, and boundary preference, were suggested. We confirmed that a description of decision-making based on these factors can give a rise to realistic preference patterns, using a simple mathematical model. Our findings provide new perspectives and a baseline for considering the optimization of design and safety in crowded public areas and public transport carriers.
536 _ _ |a 511 - Computational Science and Mathematical Methods (POF3-511)
|0 G:(DE-HGF)POF3-511
|c POF3-511
|f POF III
|x 0
588 _ _ |a Dataset connected to CrossRef
700 1 _ |a Ohtsuka, Kazumichi
|0 P:(DE-HGF)0
|b 1
700 1 _ |a Chraibi, Mohcine
|0 P:(DE-Juel1)132077
|b 2
|e Corresponding author
700 1 _ |a Boltes, Maik
|0 P:(DE-Juel1)132064
|b 3
|u fzj
700 1 _ |a Yanagisawa, Daichi
|0 P:(DE-HGF)0
|b 4
700 1 _ |a Seyfried, Armin
|0 P:(DE-Juel1)132266
|b 5
|u fzj
700 1 _ |a Schadschneider, Andreas
|0 P:(DE-HGF)0
|b 6
700 1 _ |a Nishinari, Katsuhiro
|0 P:(DE-HGF)0
|b 7
773 _ _ |a 10.17815/CD.2016.4
|g Vol. 1, p. A4
|0 PERI:(DE-600)2854776-7
|p A4
|t Collective dynamics
|v 1
|y 2016
|x 2366-8539
856 4 _ |y OpenAccess
|u https://juser.fz-juelich.de/record/818075/files/5-79-1-PB.pdf
856 4 _ |y OpenAccess
|x icon
|u https://juser.fz-juelich.de/record/818075/files/5-79-1-PB.gif?subformat=icon
856 4 _ |y OpenAccess
|x icon-1440
|u https://juser.fz-juelich.de/record/818075/files/5-79-1-PB.jpg?subformat=icon-1440
856 4 _ |y OpenAccess
|x icon-180
|u https://juser.fz-juelich.de/record/818075/files/5-79-1-PB.jpg?subformat=icon-180
856 4 _ |y OpenAccess
|x icon-640
|u https://juser.fz-juelich.de/record/818075/files/5-79-1-PB.jpg?subformat=icon-640
856 4 _ |y OpenAccess
|x pdfa
|u https://juser.fz-juelich.de/record/818075/files/5-79-1-PB.pdf?subformat=pdfa
909 C O |o oai:juser.fz-juelich.de:818075
|p openaire
|p open_access
|p driver
|p VDB
|p dnbdelivery
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 2
|6 P:(DE-Juel1)132077
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 3
|6 P:(DE-Juel1)132064
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 5
|6 P:(DE-Juel1)132266
913 1 _ |a DE-HGF
|b Key Technologies
|1 G:(DE-HGF)POF3-510
|0 G:(DE-HGF)POF3-511
|2 G:(DE-HGF)POF3-500
|v Computational Science and Mathematical Methods
|x 0
|4 G:(DE-HGF)POF
|3 G:(DE-HGF)POF3
|l Supercomputing & Big Data
914 1 _ |y 2016
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a Creative Commons Attribution CC BY 4.0
|0 LIC:(DE-HGF)CCBY4
|2 HGFVOC
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)JSC-20090406
|k JSC
|l Jülich Supercomputing Center
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)JSC-20090406
980 1 _ |a FullTexts


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21