Literature DB >> 33500961

Virtual Sensing and Virtual Reality: How New Technologies Can Boost Research on Crowd Dynamics.

Mehdi Moussaïd1, Victor R Schinazi2, Mubbasir Kapadia3, Tyler Thrash2,4,5.   

Abstract

The collective behavior of human crowds often exhibits surprisingly regular patterns of movement. These patterns stem from social interactions between pedestrians such as when individuals imitate others, follow their neighbors, avoid collisions with other pedestrians, or push each other. While some of these patterns are beneficial and promote efficient collective motion, others can seriously disrupt the flow, ultimately leading to deadly crowd disasters. Understanding the dynamics of crowd movements can help urban planners manage crowd safety in dense urban areas and develop an understanding of dynamic social systems. However, the study of crowd behavior has been hindered by technical and methodological challenges. Laboratory experiments involving large crowds can be difficult to organize, and quantitative field data collected from surveillance cameras are difficult to evaluate. Nevertheless, crowd research has undergone important developments in the past few years that have led to numerous research opportunities. For example, the development of crowd monitoring based on the virtual signals emitted by pedestrians' smartphones has changed the way researchers collect and analyze live field data. In addition, the use of virtual reality, and multi-user platforms in particular, have paved the way for new types of experiments. In this review, we describe these methodological developments in detail and discuss how these novel technologies can be used to deepen our understanding of crowd behavior.
Copyright © 2018 Moussaïd, Schinazi, Kapadia and Thrash.

Entities:  

Keywords:  collective movement; complex systems; pedestrians; social interactions; tracking; virtual environment

Year:  2018        PMID: 33500961      PMCID: PMC7806084          DOI: 10.3389/frobt.2018.00082

Source DB:  PubMed          Journal:  Front Robot AI        ISSN: 2296-9144


  52 in total

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5.  Conducting behavioral research on Amazon's Mechanical Turk.

Authors:  Winter Mason; Siddharth Suri
Journal:  Behav Res Methods       Date:  2012-03

6.  Human responses to multiple sources of directional information in virtual crowd evacuations.

Authors:  Nikolai W F Bode; Armel U Kemloh Wagoum; Edward A Codling
Journal:  J R Soc Interface       Date:  2013-11-20       Impact factor: 4.118

7.  Competition in human groups-Impact on group cohesion, perceived stress and outcome satisfaction.

Authors:  Margarete Boos; Xaver Franiel; Michael Belz
Journal:  Behav Processes       Date:  2015-07-26       Impact factor: 1.777

8.  Behavioral dynamics of intercepting a moving target.

Authors:  Brett R Fajen; William H Warren
Journal:  Exp Brain Res       Date:  2007-02-02       Impact factor: 2.064

9.  Saving Human Lives: What Complexity Science and Information Systems can Contribute.

Authors:  Dirk Helbing; Dirk Brockmann; Thomas Chadefaux; Karsten Donnay; Ulf Blanke; Olivia Woolley-Meza; Mehdi Moussaid; Anders Johansson; Jens Krause; Sebastian Schutte; Matjaž Perc
Journal:  J Stat Phys       Date:  2014-06-05       Impact factor: 1.548

10.  Increased costs reduce reciprocal helping behaviour of humans in a virtual evacuation experiment.

Authors:  Nikolai W F Bode; Jordan Miller; Rick O'Gorman; Edward A Codling
Journal:  Sci Rep       Date:  2015-11-06       Impact factor: 4.379

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Authors:  Hengshan Li; Panagiotis Mavros; Jakub Krukar; Christoph Hölscher
Journal:  Cogn Process       Date:  2021-02-09

2.  A Novel Sentiment Analysis Model of Museum User Experience Evaluation Data Based on Unbalanced Data Analysis Technology.

Authors:  Xiang Chen; Zhiwei Chen; Lei Xiao; Ming Zhou
Journal:  Comput Intell Neurosci       Date:  2022-04-28
  2 in total

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