7,885 research outputs found
Dynamics of pedestrians in regions with no visibility - a lattice model without exclusion
We investigate the motion of pedestrians through obscure corridors where the
lack of visibility (due to smoke, fog, darkness, etc.) hides the precise
position of the exits. We focus our attention on a set of basic mechanisms,
which we assume to be governing the dynamics at the individual level. Using a
lattice model, we explore the effects of non-exclusion on the overall exit flux
(evacuation rate). More precisely, we study the effect of the buddying
threshold (of no-exclusion per site) on the dynamics of the crowd and
investigate to which extent our model confirms the following pattern revealed
by investigations on real emergencies: If the evacuees tend to cooperate and
act altruistically, then their collective action tends to favor the occurrence
of disasters.Comment: 20 page
Multiscale modeling of granular flows with application to crowd dynamics
In this paper a new multiscale modeling technique is proposed. It relies on a
recently introduced measure-theoretic approach, which allows to manage the
microscopic and the macroscopic scale under a unique framework. In the
resulting coupled model the two scales coexist and share information. This
allows to perform numerical simulations in which the trajectories and the
density of the particles affect each other. Crowd dynamics is the motivating
application throughout the paper.Comment: 30 pages, 9 figure
Multiscale modeling of granular flows with application to crowd dynamics
In this paper a new multiscale modeling technique is proposed. It relies on a
recently introduced measure-theoretic approach, which allows to manage the
microscopic and the macroscopic scale under a unique framework. In the
resulting coupled model the two scales coexist and share information. This
allows to perform numerical simulations in which the trajectories and the
density of the particles affect each other. Crowd dynamics is the motivating
application throughout the paper.Comment: 30 pages, 9 figure
Traffic Instabilities in Self-Organized Pedestrian Crowds
In human crowds as well as in many animal societies, local interactions among
individuals often give rise to self-organized collective organizations that
offer functional benefits to the group. For instance, flows of pedestrians
moving in opposite directions spontaneously segregate into lanes of uniform
walking directions. This phenomenon is often referred to as a smart collective
pattern, as it increases the traffic efficiency with no need of external
control. However, the functional benefits of this emergent organization have
never been experimentally measured, and the underlying behavioral mechanisms
are poorly understood. In this work, we have studied this phenomenon under
controlled laboratory conditions. We found that the traffic segregation
exhibits structural instabilities characterized by the alternation of organized
and disorganized states, where the lifetime of well-organized clusters of
pedestrians follow a stretched exponential relaxation process. Further analysis
show that the inter-pedestrian variability of comfortable walking speeds is a
key variable at the origin of the observed traffic perturbations. We show that
the collective benefit of the emerging pattern is maximized when all
pedestrians walk at the average speed of the group. In practice, however, local
interactions between slow- and fast-walking pedestrians trigger global
breakdowns of organization, which reduce the collective and the individual
payoff provided by the traffic segregation. This work is a step ahead toward
the understanding of traffic self-organization in crowds, which turns out to be
modulated by complex behavioral mechanisms that do not always maximize the
group's benefits. The quantitative understanding of crowd behaviors opens the
way for designing bottom-up management strategies bound to promote the
emergence of efficient collective behaviors in crowds.Comment: Article published in PLoS Computational biology. Freely available
here:
http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.100244
Social Influence and the Collective Dynamics of Opinion Formation
Social influence is the process by which individuals adapt their opinion,
revise their beliefs, or change their behavior as a result of social
interactions with other people. In our strongly interconnected society, social
influence plays a prominent role in many self-organized phenomena such as
herding in cultural markets, the spread of ideas and innovations, and the
amplification of fears during epidemics. Yet, the mechanisms of opinion
formation remain poorly understood, and existing physics-based models lack
systematic empirical validation. Here, we report two controlled experiments
showing how participants answering factual questions revise their initial
judgments after being exposed to the opinion and confidence level of others.
Based on the observation of 59 experimental subjects exposed to peer-opinion
for 15 different items, we draw an influence map that describes the strength of
peer influence during interactions. A simple process model derived from our
observations demonstrates how opinions in a group of interacting people can
converge or split over repeated interactions. In particular, we identify two
major attractors of opinion: (i) the expert effect, induced by the presence of
a highly confident individual in the group, and (ii) the majority effect,
caused by the presence of a critical mass of laypeople sharing similar
opinions. Additional simulations reveal the existence of a tipping point at
which one attractor will dominate over the other, driving collective opinion in
a given direction. These findings have implications for understanding the
mechanisms of public opinion formation and managing conflicting situations in
which self-confident and better informed minorities challenge the views of a
large uninformed majority.Comment: Published Nov 05, 2013. Open access at:
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.007843
Computer simulation of leadership, consensus decision making and collective behaviour in humans
The aim of this study is to evaluate the reliability of a
crowd simulation model developed by the authors by reproducing Dyer et al.’s experiments(published in Philosophical Transactions in 2009) on human leadership and
consensus decision-Âmaking in a computer-Âbased environment.
The theoretical crowd model of the simulation environment is presented, and its results are compared and analysed against Dyer et al.’s original experiments.
It is concluded that the results are 11 largely consistent
with the experiments, which demonstrates the reliability of
the crowd model. Furthermore, the simulation data also reveals several additional new findings, namely:
1) the phenomena of sacrificing accuracy to reach a quicker
consensus decision found in ants colonies was also discovered in the simulation;
2) the ability of reaching consensus in groups has a direct
impact on the time and accuracy of arriving at the target
position;
3) the positions of the informed individuals or leaders
in the crowd could have significant impact on the overall
crowd movement;
4) the simulation also confirmed Dyer et al.’s anecdotal
evidence of the proportion of the leadership in large crowds
and its effect on crowd movement.
The potential applications of these findings are highlighted in the final discussion of this paper
From Groups to Leaders and Back. Exploring Mutual Predictability Between Social Groups and Their Leaders
Recently, social theories and empirical observations identified small groups and leaders as the basic elements which shape a crowd. This leads to an intermediate level of abstraction that is placed between the crowd as a flow of people, and the crowd as a collection of individuals. Consequently, automatic analysis of crowds in computer vision is also experiencing a shift in focus from individuals to groups and from small groups to their leaders. In this chapter, we present state-of-the-art solutions to the groups and leaders detection problem, which are able to account for physical factors as well as for sociological evidence observed over short time windows. The presented algorithms are framed as structured learning problems over the set of individual trajectories. However, the way trajectories are exploited to predict the structure of the crowd is not fixed but rather learned from recorded and annotated data, enabling the method to adapt these concepts to different scenarios, densities, cultures, and other unobservable complexities. Additionally, we investigate the relation between leaders and their groups and propose the first attempt to exploit leadership as prior knowledge for group detection
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