Literature DB >> 25526171

Universal power law governing pedestrian interactions.

Ioannis Karamouzas1, Brian Skinner2, Stephen J Guy1.   

Abstract

Human crowds often bear a striking resemblance to interacting particle systems, and this has prompted many researchers to describe pedestrian dynamics in terms of interaction forces and potential energies. The correct quantitative form of this interaction, however, has remained an open question. Here, we introduce a novel statistical-mechanical approach to directly measure the interaction energy between pedestrians. This analysis, when applied to a large collection of human motion data, reveals a simple power-law interaction that is based not on the physical separation between pedestrians but on their projected time to a potential future collision, and is therefore fundamentally anticipatory in nature. Remarkably, this simple law is able to describe human interactions across a wide variety of situations, speeds, and densities. We further show, through simulations, that the interaction law we identify is sufficient to reproduce many known crowd phenomena.

Entities:  

Year:  2014        PMID: 25526171     DOI: 10.1103/PhysRevLett.113.238701

Source DB:  PubMed          Journal:  Phys Rev Lett        ISSN: 0031-9007            Impact factor:   9.161


  13 in total

1.  Lévy walk process in self-organization of pedestrian crowds.

Authors:  Hisashi Murakami; Claudio Feliciani; Katsuhiro Nishinari
Journal:  J R Soc Interface       Date:  2019-04-26       Impact factor: 4.118

2.  General scaling in bidirectional flows of self-avoiding agents.

Authors:  Javier Cristín; Vicenç Méndez; Daniel Campos
Journal:  Sci Rep       Date:  2019-12-06       Impact factor: 4.379

3.  Drawing power of virtual crowds.

Authors:  Elham Mohammadi Jorjafki; Brad J Sagarin; Sachit Butail
Journal:  J R Soc Interface       Date:  2018-08       Impact factor: 4.118

4.  Collective dynamics of capacity-constrained ride-pooling fleets.

Authors:  Robin M Zech; Nora Molkenthin; Marc Timme; Malte Schröder
Journal:  Sci Rep       Date:  2022-06-27       Impact factor: 4.996

5.  Crowd flow forecasting via agent-based simulations with sequential latent parameter estimation from aggregate observation.

Authors:  Fumiyasu Makinoshima; Yusuke Oishi
Journal:  Sci Rep       Date:  2022-07-01       Impact factor: 4.996

6.  Collective motion of predictive swarms.

Authors:  Nathaniel Rupprecht; Dervis Can Vural
Journal:  PLoS One       Date:  2017-10-24       Impact factor: 3.240

7.  Emergence of a coherent and cohesive swarm based on mutual anticipation.

Authors:  Hisashi Murakami; Takayuki Niizato; Yukio-Pegio Gunji
Journal:  Sci Rep       Date:  2017-04-13       Impact factor: 4.379

8.  Quantifying people's experience during flood events with implications for hazard risk communication.

Authors:  Nataliya Tkachenko; Rob Procter; Stephen Jarvis
Journal:  PLoS One       Date:  2021-01-07       Impact factor: 3.240

9.  Active and reactive behaviour in human mobility: the influence of attraction points on pedestrians.

Authors:  M Gutiérrez-Roig; O Sagarra; A Oltra; J R B Palmer; F Bartumeus; A Díaz-Guilera; J Perelló
Journal:  R Soc Open Sci       Date:  2016-07-13       Impact factor: 2.963

10.  Energy Level-Based Abnormal Crowd Behavior Detection.

Authors:  Xuguang Zhang; Qian Zhang; Shuo Hu; Chunsheng Guo; Hui Yu
Journal:  Sensors (Basel)       Date:  2018-02-01       Impact factor: 3.576

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