A multi-grid model for pedestrian evacuation in a room without visibility

The evacuation process from a room without visibility is investigated by both experiment and modeling. Some typical characteristics of blind evacuation, including the preference of choosing left-hand side direction and following behavior, are found from the experiment. Meanwhile, different strategies of conflict resolution are observed in the experiment. Based on the experimental observation, a multi-grid model for evacuation without visibility is built in this paper. Simulation results of the model agree well with the experiments. Furthermore, the effect of exit width, number of exits and initial density on evacuation are studied, and results show that exit width has little impact on evacuation time and increasing number of exits is an effective way to decrease evacuation time. Finally, simulations of evacuation under normal and no visibility are compared, and the differences for two conditions are predicted. The comparison results also demonstrate that the blind evacuation is much slower than evacuation under normal visibility, which is match with the practical experience. A similar point is that the distributions of time interval in both situations satisfy power-law relation approximately. The study may be useful for understanding the egress behaviors and developing efficient evacuation strategy and plan to guide pedestrian evacuation without visibility.

[1]  A. Seyfried,et al.  Basics of Modelling the Pedestrian Flow , 2005, physics/0506189.

[2]  A. Schadschneider,et al.  Ordering in bidirectional pedestrian flows and its influence on the fundamental diagram , 2012 .

[3]  Weiguo Song,et al.  Discretization effect in a multi-grid egress model , 2008 .

[4]  Xiwei Guo,et al.  A heterogeneous lattice gas model for simulating pedestrian evacuation , 2012 .

[5]  Dirk Helbing A Fluid-Dynamic Model for the Movement of Pedestrians , 1992, Complex Syst..

[6]  Won-Hwa Hong,et al.  Evacuation performance of individuals in different visibility conditions , 2011 .

[7]  B. Mandelbrot How Long Is the Coast of Britain? Statistical Self-Similarity and Fractional Dimension , 1967, Science.

[8]  Weiguo Song,et al.  Experiment and multi-grid modeling of evacuation from a classroom , 2008 .

[9]  Xiaodong Liu,et al.  Defining static floor field of evacuation model in large exit scenario , 2014, Simul. Model. Pract. Theory.

[10]  Hui Zhao,et al.  Effect of prediction on the self-organization of pedestrian counter flow , 2012 .

[11]  Xiaodong Zhou,et al.  A floor field cellular automaton for crowd evacuation considering different walking abilities , 2015 .

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

[13]  Hao Wu,et al.  Experiment and modeling of exit-selecting behaviors during a building evacuation , 2010 .

[14]  A. Schadschneider,et al.  Discretization effects and the influence of walking speed in cellular automata models for pedestrian dynamics , 2004 .

[15]  Hongyong Yuan,et al.  Small-grid analysis of discrete model for evacuation from a hall , 2007 .

[16]  Xingli Li,et al.  Analysis of pedestrian dynamics in counter flow via an extended lattice gas model. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[18]  Shuang Li,et al.  Occupant evacuation and casualty estimation in a building under earthquake using cellular automata , 2015 .

[19]  Jean-Daniel Zucker,et al.  Integration of Smoke Effect and Blind Evacuation Strategy (SEBES) within fire evacuation simulation , 2013, Simul. Model. Pract. Theory.

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

[21]  Yuan Weifeng,et al.  A novel algorithm of simulating multi-velocity evacuation based on cellular automata modeling and tenability condition , 2007 .

[22]  T. Nagatani,et al.  Statistical characteristics of evacuation without visibility in random walk model , 2004 .

[23]  L. F. Henderson,et al.  The Statistics of Crowd Fluids , 1971, Nature.

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

[25]  Bauke de Vries,et al.  Way finding during fire evacuation; an analysis of unannounced fire drills in a hotel at night , 2010 .

[26]  Dong,et al.  Evacuation of pedestrians from a hall by game strategy update , 2014 .

[27]  Dirk Helbing,et al.  Experiment, theory, and simulation of the evacuation of a room without visibility. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[28]  Daichi Yanagisawa,et al.  Study on Efficiency of Evacuation with an Obstacle on Hexagonal Cell Space , 2010 .

[29]  Paul M. Torrens,et al.  An extensible simulation environment and movement metrics for testing walking behavior in agent-based models , 2012, Comput. Environ. Urban Syst..

[30]  Vilis O. Nams,et al.  The VFractal: a new estimator for fractal dimension of animal movement paths , 1996, Landscape Ecology.

[31]  Mendeli H Vainstein,et al.  Percolation and cooperation with mobile agents: geometric and strategy clusters. , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

[32]  Andreas Schadschneider,et al.  Simulation of evacuation processes using a bionics-inspired cellular automaton model for pedestrian dynamics , 2002 .

[33]  Jun Zhang,et al.  Transitions in pedestrian fundamental diagrams of straight corridors and T-junctions , 2011, 1102.4766.

[34]  Wei Lv,et al.  Analyzing the Characteristics of Unidirectional Bicycle Movement Around a Track based on Digital Image Processing , 2013 .

[35]  W. Song,et al.  Effect of traffic rule breaking behavior on pedestrian counterflow in a channel with a partition line. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[36]  K. Nishinari,et al.  Introduction of frictional and turning function for pedestrian outflow with an obstacle. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[37]  Yu-Chun Wang,et al.  Integrated network approach of evacuation simulation for large complex buildings , 2009 .

[38]  Dirk Helbing,et al.  Experimental study of the behavioural mechanisms underlying self-organization in human crowds , 2009, Proceedings of the Royal Society B: Biological Sciences.

[39]  Stefania Bandini,et al.  Heterogeneous Speed Profiles in Discrete Models for Pedestrian Simulation , 2014, ArXiv.

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

[41]  Mohammed Mahmod Shuaib Preserving socially expected crowd density in front of an exit for the reproduction of experimental data by modeling pedestrians' rear perception , 2014 .

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

[43]  R. Jiang,et al.  Pedestrian behaviors in a lattice gas model with large maximum velocity , 2007 .

[44]  Wei Lv,et al.  A Two-Dimensional Optimal Velocity Model for Unidirectional Pedestrian Flow Based on Pedestrian's Visual Hindrance Field , 2013, IEEE Transactions on Intelligent Transportation Systems.

[45]  Mao-Bin Hu,et al.  Pedestrian flow dynamics in a lattice gas model coupled with an evolutionary game. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[47]  Hai-Jun Huang,et al.  Static floor field and exit choice for pedestrian evacuation in rooms with internal obstacles and multiple exits. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[48]  Vilis O. Nams,et al.  Using animal movement paths to measure response to spatial scale , 2005, Oecologia.

[49]  Wang Bing-Hong,et al.  Evacuation behaviors at exit in CA model with force essentials: A comparison with social force model , 2006 .

[50]  Qi Zhang,et al.  Simulation model of bi-directional pedestrian considering potential effect ahead and behind , 2015 .

[51]  Gerta Köster,et al.  How update schemes influence crowd simulations , 2014 .

[52]  Gerta Köster,et al.  Natural discretization of pedestrian movement in continuous space. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[53]  Hairong Dong,et al.  Guided crowd dynamics via modified social force model , 2014 .

[54]  Yuan Cheng,et al.  Modeling cooperative and competitive behaviors in emergency evacuation: A game-theoretical approach , 2011, Comput. Math. Appl..

[55]  Shing Chung Josh Wong,et al.  Route choice in pedestrian evacuation under conditions of good and zero visibility: Experimental and simulation results , 2012 .

[56]  Lizhong Yang,et al.  Simulation of pedestrian counter-flow with right-moving preference , 2008 .

[57]  Hsin-Yun Lee Using a Guiding Network to Determine Efficient Evacuation Routes in a Public Building , 2013 .

[58]  Hai-Jun Huang,et al.  Theoretical analysis and simulation of pedestrian evacuation under invisible conditions , 2012, Simul..

[59]  Shing Chung Josh Wong,et al.  Collection, spillback, and dissipation in pedestrian evacuation: A network-based method , 2011 .

[60]  A. Tomoeda,et al.  Analytical investigation of the faster-is-slower effect with a simplified phenomenological model. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[61]  Hao Yue,et al.  Simulation of pedestrian evacuation with affected visual field and absence of evacuation signs , 2010, 2010 Sixth International Conference on Natural Computation.

[62]  Bing-Hong Wang,et al.  Simulation of evacuation processes using a multi-grid model for pedestrian dynamics , 2006 .

[63]  S Bouzat,et al.  Game theory in models of pedestrian room evacuation. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[64]  Weiguo Song,et al.  A Multi-Grid Model for Evacuation Coupling with the Effects of Fire Products , 2012 .

[65]  Srinivas Peeta,et al.  Planning for Evacuation : Insights from an Efficient Network Design Model , 2009 .

[66]  Isabella von Sivers,et al.  How Stride Adaptation in Pedestrian Models Improves Navigation , 2014, ArXiv.

[67]  Taras I. Lakoba,et al.  Modifications of the Helbing-Molnár-Farkas-Vicsek Social Force Model for Pedestrian Evolution , 2005, Simul..

[68]  Li-Yun Dong,et al.  Self-organized phenomena of pedestrian counter flow in a channel under periodic boundary conditions , 2012 .