3,834 research outputs found
Measuring and Modeling Behavioral Decision Dynamics in Collective Evacuation
Identifying and quantifying factors influencing human decision making remains
an outstanding challenge, impacting the performance and predictability of
social and technological systems. In many cases, system failures are traced to
human factors including congestion, overload, miscommunication, and delays.
Here we report results of a behavioral network science experiment, targeting
decision making in a natural disaster. In each scenario, individuals are faced
with a forced "go" versus "no go" evacuation decision, based on information
available on competing broadcast and peer-to-peer sources. In this controlled
setting, all actions and observations are recorded prior to the decision,
enabling development of a quantitative decision making model that accounts for
the disaster likelihood, severity, and temporal urgency, as well as competition
between networked individuals for limited emergency resources. Individual
differences in behavior within this social setting are correlated with
individual differences in inherent risk attitudes, as measured by standard
psychological assessments. Identification of robust methods for quantifying
human decisions in the face of risk has implications for policy in disasters
and other threat scenarios.Comment: Approved for public release; distribution is unlimite
How simple rules determine pedestrian behavior and crowd disasters
With the increasing size and frequency of mass events, the study of crowd
disasters and the simulation of pedestrian flows have become important research
areas. Yet, even successful modeling approaches such as those inspired by
Newtonian force models are still not fully consistent with empirical
observations and are sometimes hard to calibrate. Here, a novel cognitive
science approach is proposed, which is based on behavioral heuristics. We
suggest that, guided by visual information, namely the distance of obstructions
in candidate lines of sight, pedestrians apply two simple cognitive procedures
to adapt their walking speeds and directions. While simpler than previous
approaches, this model predicts individual trajectories and collective patterns
of motion in good quantitative agreement with a large variety of empirical and
experimental data. This includes the emergence of self-organization phenomena,
such as the spontaneous formation of unidirectional lanes or stop-and-go waves.
Moreover, the combination of pedestrian heuristics with body collisions
generates crowd turbulence at extreme densities-a phenomenon that has been
observed during recent crowd disasters. By proposing an integrated treatment of
simultaneous interactions between multiple individuals, our approach overcomes
limitations of current physics-inspired pair interaction models. Understanding
crowd dynamics through cognitive heuristics is therefore not only crucial for a
better preparation of safe mass events. It also clears the way for a more
realistic modeling of collective social behaviors, in particular of human
crowds and biological swarms. Furthermore, our behavioral heuristics may serve
to improve the navigation of autonomous robots.Comment: Article accepted for publication in PNA
Inflow process of pedestrians to a confined space
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.Comment: 23 pages, 6 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
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