Insights Toward Characteristics of Merging Streams of Pedestrian Crowds Based on Experiments with Panicked Ants

Geometric design of egress and ingress configurations affects collective movement of pedestrians. Merging streams of pedestrian crowds is, in particular, one of the frequently observed features in public infrastructures and mass gatherings. However, few qualitative and quantitative studies have addressed this phenomenon in emergency situations because of the scarcity of data on human panic conditions. This paper studies the underlying geometric factors that affect dynamics and flow characteristics of merging streams in panic conditions through the use of nonhuman entities. Many experiments with panicked ants were performed in different angles and geometric characteristics. Flow rates, headway distributions, and escape speed of merging streams were studied. Results suggest that merging layouts can lead to the creation of stop-and-go phenomena and cause significant variation in the velocities of the joining paths over time. The authors also found that setups with tributary merging paths, in which a deviated stream joins a main branch, performed poorly compared with equivalent symmetrical setups. In addition, results show a dependency between traffic flow characteristics of the merged flow and the merging angle. Analyses also suggest nonexistence of a monotonic relation (or trend) between the merging angle and the merging throughput. This outcome specifically highlights the possibility of the presence of certain merging angles below and above which the overall flow performance is poorer (i.e., optimal angles). The findings provide a better understanding of the macroscopic characteristics of escaping flows in merging sections.

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

[2]  Nirajan Shiwakoti,et al.  Examining influence of merging architectural features on pedestrian crowd movement , 2015 .

[3]  Wei Lv,et al.  Analyzing pedestrian merging flow on a floor–stair interface using an extended lattice gas model , 2014, Simul..

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

[5]  Omid Ejtemai,et al.  Modeling Pedestrian Crowd Exit Choice through Combining Sources of Stated Preference Data , 2015 .

[6]  Bernhard Steffen,et al.  T-junction: Experiments, trajectory collection, and analysis , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[7]  Karen Boyce,et al.  Experimental studies to investigate merging behaviour in a staircase , 2012 .

[8]  J. F. Dickie Major crowd catastrophes , 1995 .

[9]  Christian Bauckhage,et al.  Loveparade 2010: Automatic video analysis of a crowd disaster , 2012, Comput. Vis. Image Underst..

[10]  Victor J. Blue,et al.  Cellular Automata Microsimulation of Bidirectional Pedestrian Flows , 1999 .

[11]  Omid Ejtemai,et al.  Random Utility Models of Pedestrian Crowd Exit Selection based on SP-off-RP Experiments , 2014 .

[12]  Nirajan Shiwakoti,et al.  Understanding pedestrian crowd panic: a review on model organisms approach , 2013 .

[13]  B. Hölldobler,et al.  Multimodal signals in ant communication , 1999, Journal of Comparative Physiology A.

[14]  Hitoshi Watanabe,et al.  Characteristics of merging occupants in a staircase , 2005 .

[15]  Jun Zhang,et al.  Empirical study of turning and merging of pedestrian streams in T-junction , 2011, 1112.5299.

[16]  Charitha Dias,et al.  Turning Angle Effect on Emergency Egress: Experimental Evidence and Pedestrian Crowd Simulation , 2012 .

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

[18]  Takashi Nagatani,et al.  Dynamical transition in merging pedestrian flow without bottleneck , 2002 .

[19]  Hani S. Mahmassani,et al.  Exit Choice Decisions during Pedestrian Evacuations of Buildings , 2012 .

[20]  Shing Chung Josh Wong,et al.  Bidirectional Pedestrian Stream Model with Oblique Intersecting Angle , 2010 .

[21]  Charitha Dias,et al.  Intersecting and merging pedestrian crowd flows under panic conditions: insights from biological entities , 2012 .

[22]  Majid Sarvi,et al.  Accommodating taste heterogeneity and desired substitution pattern in exit choices of pedestrian crowd evacuees using a mixed nested logit model , 2015 .

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

[24]  Angel Garcimartín,et al.  The Conference in Pedestrian and Evacuation Dynamics 2014 ( PED 2014 ) Experimental evidence of the “ Faster Is Slower ” effect , 2014 .

[25]  Dong Zhang,et al.  Study on evacuation behaviors at a T-shaped intersection by a force-driving cellular automata model , 2012 .

[26]  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.

[27]  Nirajan Shiwakoti,et al.  Enhancing the panic escape of crowd through architectural design , 2013 .

[28]  Serge P. Hoogendoorn,et al.  Pedestrian Free Speed Behavior in Crossing Flows , 2007 .

[29]  Daniel R. Parisi,et al.  Experimental evidence of the "Faster is Slower" effect in the evacuation of ants , 2012 .

[30]  Jian Ma,et al.  New insights into turbulent pedestrian movement pattern in crowd-quakes , 2013 .

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

[32]  Majid Sarvi,et al.  Identifying Latent Classes of Pedestrian Crowd Evacuees , 2016 .

[33]  Benigno E Aguirre,et al.  Emergency Evacuations, Panic, and Social Psychology , 2005, Psychiatry.

[34]  Armin Seyfried,et al.  Performance of stairs: fundamental diagram and topographical measurements , 2013 .

[35]  Chung-I Chou,et al.  Simulation of pedestrian flow through a "T" intersection: A multi-floor field cellular automata approach , 2011, Comput. Phys. Commun..

[36]  G. Courtine,et al.  Human walking along a curved path. II. Gait features and EMG patterns , 2003, The European journal of neuroscience.

[37]  T. Nagatani,et al.  Clogging transition of pedestrian flow in T-shaped channel , 2002 .

[38]  Masao Kuwahara,et al.  Multi-Directional Pedestrian Flow Model Based on Empirical Data , 2007 .

[39]  Dirk Helbing,et al.  Crowd disasters as systemic failures: analysis of the Love Parade disaster , 2012, EPJ Data Science.

[40]  Weiguo Song,et al.  Behavior of Ants Escaping from a Single-Exit Room , 2015, PloS one.

[41]  Meead Saberi,et al.  Spatial fluctuations of pedestrian velocities in bidirectional streams: Exploring the effects of self-organization , 2015 .

[42]  Daniel R. Parisi,et al.  Efficient Egress of Escaping Ants Stressed with Temperature , 2013, PloS one.

[43]  Omid Ejtemai,et al.  Understanding pedestrian crowd merging behavior , 2014 .

[44]  Meead Saberi,et al.  Exploring Areawide Dynamics of Pedestrian Crowds , 2014 .

[45]  William Thielicke,et al.  PIVlab – Towards User-friendly, Affordable and Accurate Digital Particle Image Velocimetry in MATLAB , 2014 .

[46]  N Kugihara,et al.  Effects of aggressive behaviour and group size on collective escape in an emergency: a test between a social identity model and deindividuation theory. , 2001, The British journal of social psychology.

[47]  Charitha Dias,et al.  Investigating Collective Escape Behaviours in Complex Situations , 2013 .