Panic That Spreads Sociobehavioral Contagion in Pedestrian Evacuations

Crowds are a part of everyday public life, from stadiums and arenas to school hallways. Occasionally, pushing within the crowd spontaneously escalates to crushing behavior, resulting in injuries and even death. The rarity and unpredictability of these incidents provides few options to collect data for research on the prediction and prevention of hazardous emergent behaviors in crowds. This study takes a close look at the way states of agitation, such as panic, can spread through crowds. Group composition—mainly family groups composed of members with differing mobility levels—plays an important role in the spread of agitation through the crowd, ultimately affecting the exit density and evacuation clearance time of a simulated venue. This study used an agent-based model of pedestrian movement during the egress of a hypothetical room and adopted an emotional, cognitive, and social framework to explore the transference and dissipation of agitation through a crowd. The preliminary results reveal that average group size in a crowd is a primary contributor to the exit density and evacuation clearance time. The study provides the groundwork on which to build more elaborate models that incorporate sociobehavioral aspects to simulate human movement during panic situations and account for the potential for dangerous behavior to emerge in crowds

A Framework for Group Modeling in Agent-Based Pedestrian Crowd Simulations

Pedestrian crowd simulation explores crowd behaviors in virtual environments. It is extensively studied in many areas, such as safety and civil engineering, transportation, social science, entertainment industry and so on. As a common phenomenon in pedestrian crowds, grouping can play important roles in crowd behaviors. To achieve more realistic simulations, it is important to support group modeling in crowd behaviors. Nevertheless, group modeling is still an open and challenging problem. The influence of groups on the dynamics of crowd movement has not been incorporated into most existing crowd models because of the complexity nature of social groups. This research develops a framework for group modeling in agent-based pedestrian crowd simulations. The framework includes multiple layers that support a systematic approach for modeling social groups in pedestrian crowd simulations. These layers include a simulation engine layer that provides efficient simulation engines to simulate the crowd model; a behavior-based agent modeling layers that supports developing agent models using the developed BehaviorSim simulation software; a group modeling layer that provides a well-defined way to model inter-group relationships and intra-group connections among pedestrian agents in a crowd; and finally a context modeling layer that allows users to incorporate various social and psychological models into the study of social groups in pedestrian crowd. Each layer utilizes the layer below it to fulfill its functionality, and together these layers provide an integrated framework for supporting group modeling in pedestrian crowd simulations. To our knowledge this work is the first one to focus on a systematic group modeling approach for pedestrian crowd simulations. This systematic modeling approach allows users to create social group simulation models in a well-defined way for studying the effect of social and psychological factors on crowd’s grouping behavior. To demonstrate the capability of the group modeling framework, we developed an application of dynamic grouping for pedestrian crowd simulations

Simulation of nonverbal social interaction and small groups dynamics in virtual environments

How can the behaviour of humans who interact with other humans be simulated in virtual environments? This thesis investigates the issue by proposing a number of dedicated models, computer languages, software architectures, and specifications of computational components. It relies on a large knowledge base from the social sciences, which offers concepts, descriptions, and classifications that guided the research process. The simulation of nonverbal social interaction and group dynamics in virtual environments can be divided in two main research problems: (1) an action selection problem, where autonomous agents must be made capable of deciding when, with whom, and how they interact according to individual characteristics of themselves and others; and (2) a behavioural animation problem, where, on the basis of the selected interaction, 3D characters must realistically behave in their virtual environment and communicate nonverbally with others by automatically triggering appropriate actions such as facial expressions, gestures, and postural shifts. In order to introduce the problem of action selection in social environments, a high-level architecture for social agents, based on the sociological concepts of role, norm, and value, is first discussed. A model of action selection for members of small groups, based on proactive and reactive motivational components, is then presented. This model relies on a new tagbased language called Social Identity Markup Language (SIML), allowing the rich specification of agents' social identities and relationships. A complementary model controls the simulation of interpersonal relationship development within small groups. The interactions of these two models create a complex system exhibiting emergent properties for the generation of meaningful sequences of social interactions in the temporal dimension. To address the issues related to the visualization of nonverbal interactions, results are presented of an evaluation experiment aimed at identifying the application requirements through an analysis of how real people interact nonverbally in virtual environments. Based on these results, a number of components for MPEG-4 body animation, AML — a tag-based language for the seamless integration and synchronization of facial animation, body animation, and speech — and a high-level interaction visualization service for the VHD++ platform are described. This service simulates the proxemic and kinesic aspects of nonverbal social interactions, and comprises such functionalities as parametric postures, adapters and observation behaviours, the social avoidance of collisions, intelligent approach behaviours, and the calculation of suitable interaction distances and angles

A model for generating and animating groups of virtual agents

Abstract. This paper presents a model to generate and animate groups which emerge as a function of interaction among virtual agents. The agents are characterized through the following parameters: sociability, communication, comfort, perception and memory. The emergent groups are characterized through the cohesion parameter which describes the homogeneity of ideas of the group members. In this work we are mainly interested in investigating the formation of groups (membership and time for grouping), the groups characterization (cohesion parameter) and their visual representation (group formation). The overall results suggest that the interaction among agents contributes to larger groups and higher crowd cohesion values.