a spatial evacuation

Pedestrian movements in crowd motion can be perceived in terms of agents who basically exhibit patient or impatient behavior. We model crowd motion subject to exit congestion under uncertainty conditions in a continuous space and compare the proposed model via simulations with the classical social force model. During a typical emergency evacuation scenario, agents might not be able to perceive with certainty the strategies of opponents (other agents) owing to the dynamic changes entailed by the neighborhood of opponents. In such uncertain scenarios, agents will try to update their strategy based on their own rules or their intrinsic behavior. We study risk seeking, risk averse and risk neutral behaviors of such agents via certain game theory notions. We found that risk averse agents tend to achieve faster evacuation time whenever the time delay in conflicts appears to be longer. The results of our simulations also comply with previous work and conform to the fact that evacuation time of agents becomes shorter once mutual cooperation among agents is achieved. Although the impatient strategy appears to be the rational strategy that might lead to faster evacuation times, our study scientifically shows that the more the agents are impatient, the slower is the egress time.

[1]  Kincho H. Law,et al.  A multi-agent based framework for the simulation of human and social behaviors during emergency evacuations , 2007, AI & SOCIETY.

[2]  Nicola Bellomo,et al.  On the Modeling of Traffic and Crowds: A Survey of Models, Speculations, and Perspectives , 2011, SIAM Rev..

[3]  David A. Smith,et al.  Dynamical pair approximation for cellular automata with shuffle update , 2007 .

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

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

[6]  Xiaoping Zheng,et al.  Conflict game in evacuation process: A study combining Cellular Automata model , 2011 .

[7]  Bernhard Steffen,et al.  New Insights into Pedestrian Flow Through Bottlenecks , 2009, Transp. Sci..

[8]  Axel Klar,et al.  Kinetic Derivation of Macroscopic Anticipation Models for Vehicular Traffic , 2000, SIAM J. Appl. Math..

[9]  Siuming Lo,et al.  A game theory based exit selection model for evacuation , 2006 .

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

[11]  Siuming Lo,et al.  An evacuation model: the SGEM package , 2004 .

[12]  Timo Korhonen,et al.  Fire Dynamics Simulator with Evacuation FDS+Evac, version 5. Technical Reference and User's Guide , 2008 .

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

[14]  D. Helbing,et al.  Lattice gas simulation of experimentally studied evacuation dynamics. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  Dirk Helbing,et al.  Patient and impatient pedestrians in a spatial game for egress congestion. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

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

[18]  Daniel R. Parisi,et al.  THE ROLE OF PANIC IN THE ROOM EVACUATION PROCESS , 2006 .

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

[20]  Harri Ehtamo,et al.  Game Theoretic Best-Response Dynamics for Evacuees' Exit Selection , 2010, Adv. Complex Syst..

[21]  S. Wolfram Statistical mechanics of cellular automata , 1983 .

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

[23]  Ibrahim Venkat,et al.  Simulation modelling and analysis of crowd evacuation by utilizing evolutionary stable strategy , 2016 .

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

[25]  Vicsek,et al.  Freezing by heating in a driven mesoscopic system , 1999, Physical review letters.

[26]  May Lim,et al.  Self-organized queuing and scale-free behavior in real escape panic , 2003, Proceedings of the National Academy of Sciences of the United States of America.

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

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

[29]  R. Colombo,et al.  Pedestrian flows and non‐classical shocks , 2005 .

[30]  Harri Ehtamo,et al.  Spatial game in cellular automaton evacuation model. , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.

[31]  Shukor Abd Razak,et al.  Agent-based approach for modeling evacuee uncertainty behavior using game theory model , 2013 .

[32]  T. Nagatani,et al.  Spatio-temporal distribution of escape time in evacuation process , 2003 .

[33]  K. G. Subramanian,et al.  Intelligent Evacuation Management Systems : A Review , 2022 .

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

[35]  Robert L. Goldstone,et al.  Computational models of collective behavior , 2005, Trends in Cognitive Sciences.

[36]  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.

[37]  Bing-Hong Wang,et al.  Evacuation of pedestrians from a single room by using snowdrift game theories. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

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