Spontaneous synchronization of motion in pedestrian crowds of different densities

Ma, Yi; Lee, Eric Wai Ming; Shi, Meng; Yuen, Richard K.K.

doi:10.1038/s41562-020-00997-3

Cited by 38 publications

(15 citation statements)

References 60 publications

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“…With the increasing rate of vaccination 2 and the loosening of the epidemic prevention policies 3 , crowd gathering is more and more appearing in life. In crowd gathering situations, because of the backwardness of public facilities and the deficiency of crowd management, some different crowd states, such as turbulence [4][5][6] and synchronization 7,8 , may lead to overcrowding or resonant vibrations in crowd activities and may even cause serious casualties 9,10 . Previous research has shown that strong interactions among a dense crowd, especially physical contacts, that are caused by irrational behaviours of individuals occur frequently, which may result in the evolution of the crowd motion state and even crowd disasters 4,[11][12][13][14][15][16][17] .…”

Section: Modelling Physical Contacts To Evaluate the Individual Risk ...mentioning

confidence: 99%

Modelling physical contacts to evaluate the individual risk in a dense crowd

Wang

Shen

Weng

2023

Sci Rep

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Tumble and stampede in a dense crowd may be caused by irrational behaviours of individuals and always troubles the safety management of crowd activities. Risk evaluation based on pedestrian dynamical models can be regarded as an effective method of preventing crowd disasters. Here, a method depending on a combination of collision impulses and pushing forces was used to model the physical contacts between individuals in a dense crowd, by which the acceleration error during physical contacts caused by a traditional dynamical equation can be avoided. The human domino effect in a dense crowd could be successfully reproduced, and the crushing and trampling risk of a microscopic individual in a crowd could be quantitatively evaluated separately. This method provides a more reliable and integral data foundation for evaluating individual risk that shows better portability and repeatability than macroscopic crowd risk evaluation methods and will also be conducive to preventing crowd disasters.

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Section: Modelling Physical Contacts To Evaluate the Individual Risk ...mentioning

confidence: 99%

Modelling physical contacts to evaluate the individual risk in a dense crowd

Wang

Shen

Weng

2023

Sci Rep

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“…Besides, they also suggested an increasing trend in the proportion of step synchronization with the increase of density. In the work of Ma et al, [16] they revealed that spontaneous synchronization is most likely to be activated at a determined characteristic spatial headway of 1.15 m, at which the insufficient safety distance enables the pedestrians to synchronize their footstep with their predecessors to avoid the possible inter-person collisions.…”

Section: Introductionmentioning

confidence: 99%

Investigating the characteristic delay time in the leader-follower behavior in children single-file movement

Xue¹,

Shiwakoti²,

Shi³

et al. 2023

Chinese Phys. B

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The single-file movement experiment offers a convenient way to investigate the one-dimensional leader-follower behavior of pedestrians. This study investigated the time delays of children pedestrians in the leader-follower behavior by introducing a time-dependent delayed speed correlation. A total of 118 German students from the fifth grade (aged 11-12 years old) and the 11th grade (aged 17-18 years old) participated the single-file experiment. The characteristic delay time for each pedestrian was identified by optimising the time-dependent delayed speed correlation. The influences of the curvature of the experimental scenario, density, age, and gender on the delay time were statistically examined. The results suggested that to a large extent, the revealed characteristic delay time was a density-dependent variable, and none of the curvatures, the age and gender of the individual, and the age and gender of the leader have a significant influence on it. The findings from this study are variable resources to understand the leader-follower behavior among children pedestrians and to build related simulation models.

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“…To combine them into a concrete case, we established a simulation model to describe a typical evacuation behaviour in a close space [ 12 ]. Although many works were applying macroscopic or microscopic models to study the evacuation behaviours [ 2 , 9 , 28 , 29 , 30 ], to evaluate individual strategies, micro-models represented by Social force model, CA model, and magnetic field force model [ 31 , 32 , 33 ] could give a more accurate description of individual behaviours [ 34 , 35 , 36 , 37 , 38 ]. Thus, to measure the deviation from optimum decisions, we further build a deep learning framework based on the CA evacuation simulation, in which the optimality of exit decisions is quantified as a dynamical parameter in our CA simulations [ 12 ], and more details are shown in Section 2.1 .…”

Section: Introductionmentioning

confidence: 99%

Measuring Dynamics in Evacuation Behaviour with Deep Learning

Hou

Wang

2022

Entropy

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Bounded rationality is one crucial component in human behaviours. It plays a key role in the typical collective behaviour of evacuation, in which heterogeneous information can lead to deviations from optimal choices. In this study, we propose a framework of deep learning to extract a key dynamical parameter that drives crowd evacuation behaviour in a cellular automaton (CA) model. On simulation data sets of a replica dynamic CA model, trained deep convolution neural networks (CNNs) can accurately predict dynamics from multiple frames of images. The dynamical parameter could be regarded as a factor describing the optimality of path-choosing decisions in evacuation behaviour. In addition, it should be noted that the performance of this method is robust to incomplete images, in which the information loss caused by cutting images does not hinder the feasibility of the method. Moreover, this framework provides us with a platform to quantitatively measure the optimal strategy in evacuation, and this approach can be extended to other well-designed crowd behaviour experiments.

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Spontaneous synchronization of motion in pedestrian crowds of different densities

Cited by 38 publications

References 60 publications

Modelling physical contacts to evaluate the individual risk in a dense crowd

Modelling physical contacts to evaluate the individual risk in a dense crowd

Investigating the characteristic delay time in the leader-follower behavior in children single-file movement

Measuring Dynamics in Evacuation Behaviour with Deep Learning

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