A CA-Based Model of Dyads in Pedestrian Crowds: The Case of Counter Flow

The calibration and validation of pedestrian dynamics simulation require the acquisition of empirical evidences of human behaviour. In this framework, this paper firstly presents the results of an experimental study focused on the negative impact of counter flow and grouping on pedestrian speed. In particular, we focused on two member groups (dyads) as the most frequently observed and basic interacting element of crowds. On the basis of the behavioural effects observed with the experiment, a novel cellular automaton is proposed to represent the different behaviour of individuals and dyads, with particular reference to the group spatial alignment and the dynamic leader-follower structure. This has been demonstrated to modulate the speed of dyads by maintaining the spatial cohesion among the two members. In addition, the model is also able to reproduce the significant impact of flow ratio observed in the experiment results.

[1]  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.

[2]  Stefania Bandini,et al.  Towards an Integrated Approach to Crowd Analysis and Crowd Synthesis: a Case Study and First Results , 2013, Pattern Recognit. Lett..

[3]  Armin Seyfried,et al.  Collecting pedestrian trajectories , 2013, Neurocomputing.

[4]  Stefania Bandini,et al.  Empirical Investigation on Pedestrian Crowd Dynamics and Grouping , 2015 .

[5]  Takayuki Kanda,et al.  Potential for the dynamics of pedestrians in a socially interacting group. , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  Demetri Terzopoulos,et al.  Autonomous pedestrians , 2007, Graph. Model..

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

[8]  Daichi Yanagisawa,et al.  Anticipation effect in pedestrian dynamics: Modeling and experiments , 2012 .

[9]  Andreas Schadschneider,et al.  Evacuation Dynamics: Empirical Results, Modeling and Applications , 2008, Encyclopedia of Complexity and Systems Science.

[10]  Helbing,et al.  Social force model for pedestrian dynamics. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[11]  Mohcine Chraibi,et al.  Generalized centrifugal-force model for pedestrian dynamics. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  Andreas Schadschneider,et al.  Quantitative analysis of pedestrian counterflow in a cellular automaton model. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[14]  Stefania Bandini,et al.  When reactive agents are not enough: Tactical level decisions in pedestrian simulation , 2015, Intelligenza Artificiale.

[15]  D. Helbing,et al.  The Walking Behaviour of Pedestrian Social Groups and Its Impact on Crowd Dynamics , 2010, PloS one.

[16]  Andrew Philippides,et al.  From Mindless Masses to Small Groups: Conceptualizing Collective Behavior in Crowd Modeling , 2015, Review of general psychology : journal of Division 1, of the American Psychological Association.

[17]  Jaroslaw Was,et al.  Proxemics in Discrete Simulation of Evacuation , 2012, ACRI.

[18]  Stefania Bandini,et al.  Heterogeneous Pedestrian Walking Speed in Discrete Simulation Models , 2015 .

[19]  Serge P. Hoogendoorn,et al.  Influence of Group Size and Group Composition on the Adhered Distance Headway , 2014 .

[20]  Andreas Schadschneider,et al.  Study of Influence of Groups on Evacuation Dynamics Using a Cellular Automaton Model , 2014 .

[21]  Gregor Lämmel,et al.  Multidestination Pedestrian Flows in Equilibrium: A Cellular Automaton‐Based Approach , 2016, Comput. Aided Civ. Infrastructure Eng..