A Framework for Modeling Social Groups in Agent-Based Pedestrian Crowd Simulations

Grouping is a common phenomenon in pedestrian crowds and social groups can have significant impacts on crowd behavior. Despite its importance, how to model social groups in pedestrian crowd simulations is still an open and challenging issue. This paper presents a framework for modeling social groups in agent-based pedestrian crowd simulations. The developed framework integrates agent behavior modeling, group modeling, and social context modeling in a layered architecture, where each layer focuses on modeling a specific aspect of pedestrian crowds. A model of dynamic grouping behavior is developed to demonstrate the utility of the developed framework, and experimental results are presented.

[1]  Tuncer Ören,et al.  Cognitive complexity and dynamic personality in agent simulation , 2007, Comput. Hum. Behav..

[2]  W. Mcdougall.  The Group Mind: A Sketch of the Principles of Collective Psychology, with Some Attempt to Apply Them to the Interpretation of National Life and Character , 2005 .

[3]  Xiaolin Hu,et al.  BehaviorSim: A Learning Environment for Behavior-Based Agent , 2008, SAB.

[4]  E. Rubin,et al.  Perspectives for vascular genomics , 2000, Nature.

[5]  Marco Colombetti,et al.  Specifying Artificial Institutions in the Event Calculus , 2009, Handbook of Research on Multi-Agent Systems.

[6]  JI Qingge,et al.  Simulating Crowd Evacuation with a Leader-Follower Model , 2006 .

[7]  Virginia. Virginia Dignum . Dignum,et al.  Handbook of Research on Multi-Agent Systems - Semantics and Dynamics of Organizational Models , 2009, Handbook of Research on Multi-Agent Systems.

[8]  Norman I. Badler,et al.  Creating crowd variation with the OCEAN personality model , 2008, AAMAS.

[9]  A. Aveni.  The not-so-lonely crowd: Friendship groups in collective behavior. , 1977 .

[10]  Craig W. Reynolds.  Steering Behaviors For Autonomous Characters , 1999 .

[11]  Victor J. Blue,et al.  Cellular automata microsimulation for modeling bi-directional pedestrian walkways , 2001 .

[12]  L. Festinger.  A Theory of Social Comparison Processes , 1954 .

[13]  Daniel Thalmann,et al.  Hierarchical Model for Real Time Simulation of Virtual Human Crowds , 2001, IEEE Trans. Vis. Comput. Graph..

[14]  Soraia Raupp Musse,et al.  Modeling individual behaviors in crowd simulation , 2003, Proceedings 11th IEEE International Workshop on Program Comprehension.

[15]  Eric Bonabeau,et al.  Agent-based modeling: Methods and techniques for simulating human systems , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[16]  N. Badler,et al.  Toward Representing Agent Behaviors Modified by Personality and Emotion , 2002 .

[17]  Natalie Fridman,et al.  Towards a Cognitive Model of Crowd Behavior Based on Social Comparison Theory , 2007, AAAI.

[18]  Norman I. Badler,et al.  Controlling individual agents in high-density crowd simulation , 2007, SCA '07.

[19]  A. Mintz,et al.  Non-adaptive group behavior. , 1951, Journal of abnormal psychology.

[20]  Céline Loscos,et al.  Intuitive crowd behavior in dense urban environments using local laws , 2003, Proceedings of Theory and Practice of Computer Graphics, 2003..

[21]  M. Poppe,et al.  Social comparison of power: Interpersonal versus intergroup effect , 2004 .

[22]  Elisabeth André,et al.  Lets Come Together - Social Navigation Behaviors of Virtual and Real Humans , 2005, INTETAIN.

[23]  Taras I. Lakoba,et al.  Modifications of the Helbing-Molnár-Farkas-Vicsek Social Force Model for Pedestrian Evolution , 2005, Simul..

[24]  L. Cosmides,et al.  The Adapted mind : evolutionary psychology and the generation of culture , 1992 .

[25]  Ying Guo,et al.  A Reinforcement Learning Approach to Setting Multi-Objective Goals for Energy Demand Management , 2009, Int. J. Agent Technol. Syst..

[26]  A. Schadschneider.  Cellular Automaton Approach to Pedestrian Dynamics - Theory , 2001, cond-mat/0112117.

[27]  Kincho H. Law,et al.  A multi-agent based framework for the simulation of human and social behaviors during emergency evacuations , 2007, AI & SOCIETY.

[28]  Deborah Duong.  The Generative Power of Signs: The Importance of the Autonomous Perception of Tags to the Strong Emergence of Institutions , 2009 .

[29]  David J. Low,et al.  Following the crowd , 2000 .

[30]  Hubert Klüpfel,et al.  Models for Crowd Movement and Egress Simulation , 2005 .

[31]  Jian Li,et al.  Simulation of the kin behavior in building occupant evacuation based on Cellular Automaton , 2005 .

[32]  Soraia Raupp Musse,et al.  A Model for Generating and Animating Groups of Virtual Agents , 2003, IVA.

[33]  Michael J. North,et al.  Tutorial on Agent-Based Modeling and Simulation PART 2: How to Model with Agents , 2006, Proceedings of the 2006 Winter Simulation Conference.

[34]  Benigno E. Aguirre,et al.  A Critical Review Of Emergency Evacuation Simulation Models , 2004 .

[35]  Soraia Raupp Musse,et al.  A Model of Human Crowd Behavior : Group Inter-Relationship and Collision Detection Analysis , 1997, Computer Animation and Simulation.

[36]  Roger L. Hughes,et al.  A continuum theory for the flow of pedestrians , 2002 .

[37]  W. Mcdougall,et al.  The Group Mind: A Sketch of the Principles of Collective Psychology, with some Attempt to Apply them to the Interpretation of National Life and Character. , 1923 .

[38]  Xiaolin Hu,et al.  Modeling group structures in pedestrian crowd simulation , 2010, Simul. Model. Pract. Theory.

[39]  J. Sime.  Crowd psychology and engineering , 1995 .

[40]  L. F. Henderson.  On the fluid mechanics of human crowd motion , 1974 .

[41]  T. Vicsek,et al.  Simulation of pedestrian crowds in normal and evacuation situations , 2002 .