Quantitative comparison between crowd models for evacuation planning and evaluation

Crowd simulation is rapidly becoming a standard tool for evacuation planning and evaluation. However, the many crowd models in the literature are structurally different, and few have been rigorously calibrated against real-world egress data, especially in emergency situations. In this paper we describe a procedure to quantitatively compare different crowd models or between models and real-world data. We simulated three models: (1) the lattice gas model, (2) the social force model, and (3) the RVO2 model, and obtained the distributions of six observables: (1) evacuation time, (2) zoned evacuation time, (3) passage density, (4) total distance traveled, (5) inconvenience, and (6) flow rate. We then used the DISTATIS procedure to compute the compromise matrix of statistical distances between the three models. Projecting the three models onto the first two principal components of the compromise matrix, we find the lattice gas and RVO2 models are similar in terms of the evacuation time, passage density, and flow rates, whereas the social force and RVO2 models are similar in terms of the total distance traveled. Most importantly, we find that the zoned evacuation times of the three models to be very different from each other. Thus we propose to use this variable, if it can be measured, as the key test between different models, and also between models and the real world. Finally, we compared the model flow rates against the flow rate of an emergency evacuation during the May 2008 Sichuan earthquake, and found the social force model agrees best with this real data.

[1]  Bastien Chopard,et al.  Mesoscopical Modelling of Complex Systems. , 2003 .

[2]  T. VaisaghViswanathan,et al.  Modeling and Analyzing the Human Cognitive Limits for Perception in Crowd Simulation , 2012, Trans. Comput. Sci..

[3]  Dietmar Bauer,et al.  Comparing pedestrian movement simulation models for a crossing area based on real world data , 2011 .

[4]  Peter M. A. Sloot,et al.  Simulating Complex Systems by Cellular Automata , 2010, Simulating Complex Systems by Cellular Automata.

[6]  Gao Peng,et al.  3-Tier Architecture for Pedestrian Agent in Crowd Simulation , 2010 .

[7]  T. Nagatani,et al.  Effect of exit configuration on evacuation of a room without visibility , 2004 .

[8]  Alice J. O'Toole,et al.  DISTATIS: The Analysis of Multiple Distance Matrices , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[9]  Dinesh Manocha,et al.  Reciprocal Velocity Obstacles for real-time multi-agent navigation , 2008, 2008 IEEE International Conference on Robotics and Automation.

[10]  Stéphane Donikian,et al.  A synthetic-vision based steering approach for crowd simulation , 2010, SIGGRAPH 2010.

[11]  Dinesh Manocha,et al.  Modeling collision avoidance behavior for virtual humans , 2010, AAMAS.

[12]  George J. Klir,et al.  Simulating complex systems by cellular automata , 2012, Int. J. Gen. Syst..

[13]  Dinesh Manocha,et al.  Reciprocal n-Body Collision Avoidance , 2011, ISRR.

[14]  Bastien Chopard,et al.  A Multiparticle Lattice Gas Automata Model for a Crowd , 2002, ACRI.

[15]  Dinesh Manocha,et al.  Geometric methods for multi-agent collision avoidance , 2010, SoCG '10.

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

[17]  Wentong Cai,et al.  Crowd modeling and simulation technologies , 2010, TOMC.

[18]  Dinesh Manocha,et al.  Interactive navigation of multiple agents in crowded environments , 2008, I3D '08.

[19]  Xiaolin Yang,et al.  Civilian monitoring video records for earthquake intensity: a potentially unbiased online information source of macro-seismology , 2012, Natural Hazards.

[20]  Michael Schreckenberg,et al.  Models for Crowd Movement and Egress Simulation , 2005 .

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

[22]  Peter M. A. Sloot,et al.  Simulation of City Evacuation Coupled to Flood Dynamics , 2014 .

[23]  Sébastien Paris,et al.  Pedestrian Reactive Navigation for Crowd Simulation: a Predictive Approach , 2007, Comput. Graph. Forum.

[24]  Dinesh Manocha,et al.  ClearPath: highly parallel collision avoidance for multi-agent simulation , 2009, SCA '09.

[25]  Jianhua Lin,et al.  Divergence measures based on the Shannon entropy , 1991, IEEE Trans. Inf. Theory.

[26]  Dinesh Manocha,et al.  PLEdestrians: a least-effort approach to crowd simulation , 2010, SCA '10.

[27]  Sean Luke,et al.  MASON: A Multiagent Simulation Environment , 2005, Simul..

[28]  J J Fruin,et al.  DESIGNING FOR PEDESTRIANS , 1970 .

[29]  Andreas Schadschneider,et al.  Fundamental Diagram and Validation of Crowd Models , 2008, ACRI.

[30]  Erica D. Kuligowski,et al.  Pedestrian and Evacuation Dynamics , 2011 .

[31]  Seungho Lee,et al.  An integrated pedestrian behavior model based on Extended Decision Field Theory and Social Force model , 2010, Proceedings of the 2010 Winter Simulation Conference.

[32]  Michael Lees,et al.  A pattern-based modeling framework for simulating human-like pedestrian steering behaviors , 2013, VRST '13.

[33]  Sean Luke,et al.  MASON : A Multi-Agent Simulation Environment , 2008 .

[34]  Dirk Helbing,et al.  From Crowd Dynamics to Crowd Safety: a Video-Based Analysis , 2008, Adv. Complex Syst..

[35]  Adrien Bousseau,et al.  Real-time rough refraction , 2011, SI3D.

[36]  Xiaoshan Pan,et al.  Computational modeling of human and social behaviors for emergency egress analysis , 2006 .

[37]  Craig W. Reynolds Flocks, herds, and schools: a distributed behavioral model , 1998 .

[38]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

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

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

[41]  Shigeyuki Okazaki,et al.  A study of simulation model for pedestrian movement with evacuation and queuing , 1993 .

[42]  J. Pettré,et al.  A synthetic-vision based steering approach for crowd simulation , 2010, ACM Trans. Graph..

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

[44]  T. Nagatani,et al.  Scaling behavior of crowd flow outside a hall , 2001 .

[45]  T. Nagatani,et al.  Experiment and simulation of pedestrian counter flow , 2004 .

[46]  Mark H. Overmars,et al.  Eurographics/ACM SIGGRAPH Symposium on Computer Animation (2004) , 2022 .

[47]  P. Fiorini,et al.  Motion planning in dynamic environments using the relative velocity paradigm , 1993, [1993] Proceedings IEEE International Conference on Robotics and Automation.

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

[49]  Edwin R. Galea,et al.  A review of the methodologies used in the computer simulation of evacuation from the built environment , 1999 .

[50]  John M. Watts,et al.  Computer models for evacuation analysis , 1987 .

[51]  Zhongliang Wu,et al.  Difference between real-life escape panic and mimic exercises in simulated situation with implications to the statistical physics models of emergency evacuation: The 2008 Wenchuan earthquake , 2011 .

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