Understanding Crowd Panic at Turning and Intersection Through Model Organisms

Previous studies on crowd disasters have highlighted the importance of considering turning and intersecting movement patterns in an escape area. Given the scarcity of data on human panic, there may be merit to use insights from non-human organisms to understand crowd panic as collective behaviour patterns also occur in non-human biological systems. We use model organisms approach by examining empirical data collected from panicking Argentine ants to study crowd panic at turning and intersection. The empirical data showed that the outflow of ants do not decrease proportionately with the increase in turning angles. Likewise, at intersection it was observed that one stream of ants is blocked by another stream of ants for considerable duration resulting in disproportionate flow at the intersection. Although the results are preliminary for statistical significance, these can have implications in testing the models of pedestrian crowds and in development of design solutions that enhances crowd safety.

[1]  Nirajan Shiwakoti,et al.  Nest architecture and traffic flow: large potential effects from small structural features , 2010 .

[2]  Takamasa Iryo,et al.  Microscopic pedestrian simulation model combined with a tactical model for route choice behaviour , 2010 .

[3]  Armin Seyfried,et al.  Modelling of pedestrian movement around 90° and 180° bends , 2009 .

[4]  D. Helbing,et al.  Self-Organization Phenomena in Pedestrian Crowds , 1998, cond-mat/9806152.

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

[6]  Nirajan Shiwakoti,et al.  Enhancing the Safety of Pedestrians during Emergency Egress , 2009 .

[7]  F. Ratnieks,et al.  Trail geometry gives polarity to ant foraging networks , 2004, Nature.

[8]  I. Couzin,et al.  Self-Organization and Collective Behavior in Vertebrates , 2003 .

[9]  Hai-Jun Huang,et al.  A microscopic pedestrian-simulation model and its application to intersecting flows , 2010 .

[10]  Majid Sarvi,et al.  Animal dynamics based approach for modeling pedestrian crowd egress under panic conditions , 2011 .

[11]  May Lim,et al.  Self-organized queuing and scale-free behavior in real escape panic , 2003, Proceedings of the National Academy of Sciences of the United States of America.

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

[13]  Serge P. Hoogendoorn,et al.  Experimental Research of Pedestrian Walking Behavior , 2003 .

[14]  G. Courtine,et al.  Human walking along a curved path. I. Body trajectory, segment orientation and the effect of vision , 2003, The European journal of neuroscience.

[15]  R. Hughes The flow of human crowds , 2003 .

[16]  A. J. Batista-Leyva,et al.  Symmetry Breaking in Escaping Ants , 2005, The American Naturalist.

[17]  Lubos Buzna,et al.  Self-Organized Pedestrian Crowd Dynamics: Experiments, Simulations, and Design Solutions , 2005, Transp. Sci..

[18]  Nirajan Shiwakoti,et al.  Consequence of Turning Movements in Pedestrian Crowds during Emergency Egress , 2011 .

[19]  Nirajan Shiwakoti,et al.  Biologically Inspired Modeling Approach for Collective Pedestrian Dynamics under Emergency Conditions , 2010 .