Microscopic information processing and communication in crowd dynamics

Due, perhaps, to the historical division of crowd dynamics research into psychological and engineering approaches, microscopic crowd models have tended toward modelling simple interchangeable particles with an emphasis on the simulation of physical factors. Despite the fact that people have complex (non-panic) behaviours in crowd disasters, important human factors in crowd dynamics such as information discovery and processing, changing goals and communication have not yet been well integrated at the microscopic level. We use our Microscopic Human Factors methodology to fuse a microscopic simulation of these human factors with a popular microscopic crowd model. By tightly integrating human factors with the existing model we can study the effects on the physical domain (movement, force and crowd safety) when human behaviour (information processing and communication) is introduced.

[1]  Steve Gwynne,et al.  The Use of a Structure and Its Influence on Evacuation Behavior , 2010 .

[2]  Erica D. Kuligowski,et al.  Application Modes of Egress Simulation , 2010 .

[3]  Robert A. Wilson,et al.  Book Reviews: The MIT Encyclopedia of the Cognitive Sciences , 2000, CL.

[4]  Michael Schreckenberg,et al.  The F.A.S.T.-Model , 2006, ACRI.

[5]  Vicsek,et al.  Freezing by heating in a driven mesoscopic system , 1999, Physical review letters.

[6]  Wei-Zhen Lu,et al.  Lattice hydrodynamic model with bidirectional pedestrian flow , 2009 .

[7]  T. Nagatani,et al.  Jamming transition in pedestrian counter flow , 1999 .

[8]  J. Zittartz,et al.  Cellular Automaton Approach to Pedestrian Dynamics - Applications , 2001, cond-mat/0112119.

[9]  Samira El Yacoubi,et al.  Cellular Automata: 7th International Conference on Cellular Automatafor Research and Industry, ACRI 2006Perpignan, France, September 20-23, 2006Proceedings (Lecture Notes in Computer Science) , 2006 .

[10]  Jerome M. Chertkoff,et al.  Don't Panic: The Psychology of Emergency Egress and Ingress , 1999 .

[11]  Daichi Yanagisawa,et al.  Mean-field theory for pedestrian outflow through an exit. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  M. Quintanilla,et al.  Jamming threshold of dry fine powders. , 2004, Physical review letters.

[13]  John J. Fruin,et al.  THE CAUSES AND PREVENTION OF CROWD DISASTERS , 2002 .

[14]  Michael Schreckenberg,et al.  Pedestrian and evacuation dynamics , 2002 .

[15]  Ian Donald Engineering for crowd safety: edited by R.A. Smith and J.F. Dickie. Elsevier Science B.V., Amsterdam, 1993, pp. 428 , 1995 .

[16]  Tony White,et al.  Agent-Based Modelling of Forces in Crowds , 2004, MABS.

[17]  Eisma Tl Cool under fire. , 1989 .

[18]  Teresa L. Young,et al.  The F.A.S.T. Model , 2007 .

[19]  Lizhong Yang,et al.  Occupants’ behavior of going with the crowd based on cellular automata occupant evacuation model , 2008 .

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

[21]  Dirk Helbing,et al.  Dynamics of crowd disasters: an empirical study. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[22]  N. R. Johnson Panic at “The Who Concert Stampede”: An Empirical Assessment , 1987 .

[23]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[24]  Colin Marc Henein Crowds are made of people: Human factors in microscopic crowd models , 2008 .

[25]  Andreas Schadschneider,et al.  Friction effects and clogging in a cellular automaton model for pedestrian dynamics. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[26]  Leonard Y. Cooper A concept for estimating available safe egress time in fires , 1983 .

[27]  Welch Bl THE GENERALIZATION OF ‘STUDENT'S’ PROBLEM WHEN SEVERAL DIFFERENT POPULATION VARLANCES ARE INVOLVED , 1947 .

[28]  Andreas Schadschneider,et al.  Extended Floor Field CA Model for Evacuation Dynamics , 2004, IEICE Trans. Inf. Syst..

[29]  Tony White,et al.  The Microscopic Human Factors methodology for modelling cognition in crowds and swarm systems , 2010 .

[30]  T L Eisma,et al.  Cool under fire. , 1989, Occupational health & safety.

[31]  Victor J. Blue,et al.  Modeling Four-Directional Pedestrian Flows , 2000 .

[32]  A. Johansson,et al.  Constant-net-time headway as a key mechanism behind pedestrian flow dynamics. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[33]  Håkan Frantzich Occupant behaviour and response time , 2001 .

[34]  C. Lebiere,et al.  The Atomic Components of Thought , 1998 .

[35]  Marco Dorigo,et al.  Swarm intelligence: from natural to artificial systems , 1999 .

[36]  W. Marsden I and J , 2012 .

[37]  Dirk Helbing,et al.  Simulating dynamical features of escape panic , 2000, Nature.

[38]  Garry J. Smith Serious Fun: A History of Spectator Sports in the USSR , 1994 .

[39]  Beate Schmittmann,et al.  Onset of Spatial Structures in Biased Diffusion of Two Species , 1992 .

[40]  Tobias Kretz,et al.  Pedestrian Traffic - Simulation and Experiments , 2007 .

[41]  Andreas Schadschneider,et al.  Simulation of evacuation processes using a bionics-inspired cellular automaton model for pedestrian dynamics , 2002 .

[42]  C. Bishop The MIT Encyclopedia of the Cognitive Sciences , 1999 .

[43]  Tony White,et al.  The Microscopic Model and the Panicking Ball-Bearing , 2010 .

[44]  Jason D. Averill,et al.  Occupant behavior, egress, and emergency communications , 2005 .

[45]  Yoshihiro Ishibashi,et al.  Self-Organized Phase Transitions in Cellular Automaton Models for Pedestrians , 1999 .

[46]  Katsuhiro Nishinari,et al.  Simulation for pedestrian dynamics by real-coded cellular automata (RCA) , 2007 .

[47]  G. Proulx,et al.  To Prevent 'Panic' In An Underground Emergency: Why Not Tell People The Truth? , 1991 .

[48]  Peter Vortisch,et al.  Comparison of Various Methods for the Calculation of the Distance Potential Field , 2008, ArXiv.

[49]  A. Schadschneider,et al.  Simulation of pedestrian dynamics using a two dimensional cellular automaton , 2001 .

[50]  Wenjian Yu,et al.  Modeling crowd turbulence by many-particle simulations. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[51]  Tony White,et al.  Macroscopic effects of microscopic forces between agents in crowd models , 2007 .