Humanitarian Logistics and Cultural Diversity within Crowd Simulation

Human stampedes occur frequently because of abnormal events (e.g., a fire or explosion) produced in collective events (e.g., religious pilgrimages, music concerts and sportive events). These events provoke panic and when people are agglomerated they try to escape pushing each other without realizing that others are being crushed. Since crowds can consist of individuals with diverse physical and social characteristics determined by cultural diversity, it is difficult to configure the space in advance and find solutions in real-time to save people and reduce catastrophe. This paper proposes an approach to explore the impact of anthropometry and cultural diversity in the behaviour of crowds in panic situations. Our approach includes techniques for reproducing and simulating the behaviour of the crowd to generate models that can help decision making to control such situations. The main contribution of our work is to use computational science, data processing and visualization techniques to perform our simulation and study, for eventually supporting critical decision making.

[1]  B. D. Hankin,et al.  Passenger Flow in Subways , 1958 .

[2]  N Ashford,et al.  STOCHASTIC MODELLING OF PASSENGER AND BAGGAGE FLOWS THROUGH AN AIRPORT TERMINAL , 1976 .

[3]  Ko Nishino,et al.  Tracking with local spatio-temporal motion patterns in extremely crowded scenes , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[4]  Craig W. Reynolds Evolution of corridor following behavior in a noisy world , 1994 .

[5]  D Garbrecht DESCRIBING PEDESTRIAN AND CAR TRIPS BY TRANSITION MATRICES , 1973 .

[6]  John J Fruin,et al.  DESIGNING FOR PEDESTRIANS: A LEVEL-OF-SERVICE CONCEPT , 1971 .

[7]  H. Timmermans,et al.  City centre entry points, store location patterns and pedestrian route choice behaviour : a microlevel simulation model , 1986 .

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

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

[10]  Marcel Worring,et al.  Tracking individuals in surveillance video of a high-density crowd , 2012, Defense + Commercial Sensing.

[11]  Yoshiaki Shirai,et al.  Optical flow-based person tracking by multiple cameras , 2001, Conference Documentation International Conference on Multisensor Fusion and Integration for Intelligent Systems. MFI 2001 (Cat. No.01TH8590).

[12]  Ram Nevatia,et al.  Detection and Segmentation of Multiple, Partially Occluded Objects by Grouping, Merging, Assigning Part Detection Responses , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Ramakant Nevatia,et al.  Tracking multiple humans in crowded environment , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

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

[15]  D. Helbing A Mathematical Model for the Behavior of Individuals in a Social Field , 1994, cond-mat/9805194.

[16]  Roger L. Hughes,et al.  A continuum theory for the flow of pedestrians , 2002 .

[17]  Luc Van Gool,et al.  Coupled Detection and Trajectory Estimation for Multi-Object Tracking , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[18]  Koch Sigmund Ed,et al.  Psychology: A Study of A Science , 1962 .

[19]  Terence R. Smith,et al.  NAVIGATOR: An Al-Based Model of Human Way-Finding in an Urban Environment , 1990 .

[20]  Ulrich Weidmann,et al.  Transporttechnik der Fussgänger , 1992 .

[21]  Craig W. Reynolds Flocks, herds, and schools: a distributed behavioral model , 1987, SIGGRAPH.

[22]  Pascal Fua,et al.  Robust People Tracking with Global Trajectory Optimization , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[23]  D. Elliott,et al.  Football stadia disasters in the United Kingdom: learning from tragedy? , 1993 .

[24]  David V. Canter,et al.  Fires and human behaviour , 1980 .

[25]  J L Adler,et al.  Emergent Fundamental Pedestrian Flows from Cellular Automata Microsimulation , 1998 .

[26]  Mubarak Shah,et al.  A Lagrangian Particle Dynamics Approach for Crowd Flow Segmentation and Stability Analysis , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[27]  K. Lewin Field theory in social science , 1951 .

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

[29]  Takahiro Okabe,et al.  Using individuality to track individuals: Clustering individual trajectories in crowds using local appearance and frequency trait , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[30]  Ramakant Nevatia,et al.  Bayesian human segmentation in crowded situations , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[31]  Dirk Helbing,et al.  A mathematical model for the behavior of pedestrians , 1991, cond-mat/9805202.

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

[33]  Yuan Li,et al.  Fast Detection of Independent Motion in Crowds Guided by Supervised Learning , 2007, 2007 IEEE International Conference on Image Processing.

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

[35]  Ramakant Nevatia,et al.  Global data association for multi-object tracking using network flows , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[36]  James J. Little,et al.  A Linear Programming Approach for Multiple Object Tracking , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[37]  Ramakant Nevatia,et al.  Learning to associate: HybridBoosted multi-target tracker for crowded scene , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[38]  J. MacGregor Smith,et al.  Modeling circulation systems in buildings using state dependent queueing models , 1989, Queueing Syst. Theory Appl..

[39]  Yoshiaki Shirai,et al.  Person tracking by integrating optical flow and uniform brightness regions , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

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

[41]  Roberto Cipolla,et al.  Unsupervised Bayesian Detection of Independent Motion in Crowds , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[42]  Leonidas J. Guibas,et al.  Counting people in crowds with a real-time network of simple image sensors , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[43]  P G Gipps,et al.  A micro simulation model for pedestrian flows , 1985 .

[44]  Hans-Paul Schwefel,et al.  TWO-PHASE NOZZLE AND HOLLOW CORE JET EXPERIMENTS. , 1970 .

[45]  Michael Schreckenberg,et al.  Microscopic Simulation of Evacuation Processes on Passenger Ships , 2000, ACRI.

[46]  Dirk Helbing,et al.  Self-Organizing Pedestrian Movement , 2001 .

[47]  Takeo Kanade,et al.  Tracking in unstructured crowded scenes , 2009, 2009 IEEE 12th International Conference on Computer Vision.

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

[49]  Ramakant Nevatia,et al.  Robust Object Tracking by Hierarchical Association of Detection Responses , 2008, ECCV.