Self-organized queuing and scale-free behavior in real escape panic

Abstract

Numerical investigations of escape panic of confined pedestrians have revealed interesting dynamical features such as pedestrian arch formation around an exit, disruptive interference, self-organized queuing, and scale-free behavior. However, these predictions have remained unverified because escape panic experiments with real systems are difficult to perform. For mice escaping out of a water pool, we found that for a critical sampling rate the escape behavior exhibits the predicted features even at short observation times. The mice escaped via an exit in bursts of different sizes that obey exponential and (truncated) power-law distributions depending on exit width. Oversampling or undersampling the mouse escape rate prevents the observation of the predicted features. Real systems are normally subject to unavoidable constraints arising from occupancy rate, pedestrian exhaustion, and nonrigidity of pedestrian bodies. The effect of these constraints on the dynamics of real escape panic is also studied.social behavior ͉ structures in complex systems ͉ dynamics of social systems S earches of disaster databases § would readily show that the escape panic of confined pedestrians is costly in terms of fatalities and property loss. Despite the huge toll inflicted by these incidents to society, the dynamics of escape panic are not completely understood because studies have been largely confined to numerical simulations that revealed a number of interesting dynamical features such as pedestrian arch formation around an exit, herding, and interference between arches in multiple-exit rooms (1). Recently, additional features such as disruptive interference, self-organized queuing, and scale-free escape dynamics (2) were found. Experiments in genuine escape panic are difficult, especially with humans because of possible ethical and even legal concerns.This work addresses the behavior of panicking groups and how it is influenced by the architecture of the space in which they are confined. It determines whether the dynamical features predicted earlier in numerical experiments are observed in a group of real biological (nonrigid) agents undergoing escape panic. Experimental results from mice escaping out of a water pool show that for a critical sampling interval their escape behavior agrees with the numerically predicted exponential and powerlaw frequency distributions of the exit burst size even for short time durations. Oversampling or undersampling of the mouse escape rate prevents the observation of the predicted features.Escape panic could happen in different confinement sizes, from a rioting crowd in a packed stadium to stunned customers in a smoke-filled bar. It is characterized by strong contact interactions between selfish individuals that quickly gives rise to herding, stampede, and clogging (3-7). Escape panic is simulated by solving a set of coupled differential equations (1,8) or by the cellular automata (CA) technique (2) where the movement of confined pedestrians is tracked over time. Both approaches yield consiste...

Role of driving force on the clogging of inert particles in a bottleneck

“…A readily accessible parameter that is related with clogging is the mean avalanche size s . In fact, the distribution of avalanche (burst) sizes has been reported to decay exponentially both in the case of the silo [22] and in mice exiting a very small room [29]. This exponential-which can be explained by random alternation between particle and gap propagations [30] and also described in terms of a probabilistic model [22]-is characterized by the mean avalanche size s .…”

Self-Organized Pedestrian Crowd Dynamics: Experiments, Simulations, and Design Solutions

“…Herding is also observed when the orientation in an unfamiliar environment is unsatisfactory (Quarantelli 1957;Keating 1982;Johnson 1987;Elliott and Smith 1993;Helbing, Farkas, and Vicsek 2000b;Saloma et al 2003): People either follow other people who are believed to know the best way, or they use the exit they are familiar with (typically the main exit they have entered). This can cause severe congestion at a few exits, and high pressures, as all people want to leave at the same time.…”

Integrating Decentralized Indoor Evacuation with Information Depositories in the Field

“…Examples of microscopic models are cellular automata models (e.g., [10,[25][26][27][28][29][30][31]), lattice gas models (e.g., [32]), social force models (e.g., [4,11,[33][34][35]), motion planning with velocity obstacles (e.g., [36,37]), agent-based models (e.g., [38][39][40]), game theoretic models (e.g., [41][42][43]), approaches based on experiments with animals (e.g., [44][45][46][47]), and hybrid models (e.g., [48]). Cellular automata models and lattice gas models partition the space into grids or hexagons.…”

“…Agent-based models describe the evacuation system by simulating each evacuee (or a household [40]) as an autonomous agent with certain behavior and situated in an environment, and studying the emergent system behavior. In contrast, approaches based on experiments with animals study escape dynamics with real biological agents such as mice [44] or ants [45][46][47]. Hybrid models combine the previously mentioned microscopic models to benefit from their advantages and to avoid their drawbacks.…”