Comparative Evaluation of the Fast Marching Method and the Fast Evacuation Method for Heterogeneous Media

ABSTRACT The evacuation problem is usually addressed by assuming homogeneous media where pedestrians move freely in the presence of several exits and obstacles. From a more general perspective, this work considers heterogeneous media in which the velocity of pedestrians depends on their location. We use cellular automata with a floor field that indicates promising movements to pedestrians and, in this context, we extend two competitive evacuation methods in order for them to be applied to heterogeneous media: the Fast Marching Method and the Fast Evacuation Method. Furthermore, we evaluate the performance that these two methods exhibit over different simulated scenarios characterized by the presence of heterogeneous media. The resulting winning method in terms of evacuation effectiveness is greatly influenced by the particular problem being simulated.

[1]  José Rogan,et al.  Cellular automaton model for evacuation process with obstacles , 2007 .

[2]  Michael Schreckenberg,et al.  Simulation of competitive egress behavior: comparison with aircraft evacuation data , 2003 .

[3]  T. Nagatani,et al.  Scaling of pedestrian channel flow with a bottleneck , 2001 .

[4]  Song Xiang,et al.  Investigating pedestrian navigation in indoor open space environments using big data , 2018, Applied Mathematical Modelling.

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

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

[7]  Fernando Fernández,et al.  Modeling, Evaluation, and Scale on Artificial Pedestrians , 2017, ACM Comput. Surv..

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

[9]  Peter Vortisch,et al.  Quickest Paths in Simulations of Pedestrians , 2011, Adv. Complex Syst..

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

[11]  Peter Vortisch,et al.  Comparison of Various Methods for the Calculation of the Distance Potential Field , 2008, ArXiv.

[12]  Tommaso Toffoli,et al.  Cellular Automata Machines , 1987, Complex Syst..

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

[14]  Serge P. Hoogendoorn,et al.  State-of-the-art crowd motion simulation models , 2013 .

[15]  Tobias Kretz,et al.  The use of dynamic distance potential fields for pedestrian flow around corners , 2009, ArXiv.

[16]  Diana Francisca Adamatti,et al.  Multiagent Systems and Potential Fields to Smoke Dispersion Applied to Evacuation Simulations: The Case of Kiss Nightclub , 2019, Appl. Artif. Intell..

[17]  Georgios Ch. Sirakoulis,et al.  A PATH PLANNING METHOD BASED ON CELLULAR AUTOMATA FOR COOPERATIVE ROBOTS , 2011, Appl. Artif. Intell..

[18]  M. Fukui,et al.  Traffic Flow in 1D Cellular Automaton Model Including Cars Moving with High Speed , 1996 .

[19]  Eiichiro Tazaki,et al.  Adaptive behaviorin cellular automata using rough set theory , 2003, Appl. Artif. Intell..

[20]  Stephen Wolfram,et al.  A New Kind of Science , 2003, Artificial Life.

[21]  D. Helbing Traffic and related self-driven many-particle systems , 2000, cond-mat/0012229.

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

[23]  Jie Li,et al.  A Landscape of Crowd-management Support: An Integrative Approach , 2016 .

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

[25]  Charitha Dias,et al.  Calibrating cellular automaton models for pedestrians walking through corners , 2018 .

[26]  Haiying Li,et al.  Social force models for pedestrian traffic – state of the art , 2018 .

[27]  J. Zittartz,et al.  Cellular Automaton Approach to Pedestrian Dynamics - Applications , 2001, cond-mat/0112119.

[28]  Tobias Kretz,et al.  Pedestrian traffic: on the quickest path , 2009, ArXiv.

[29]  Charitha Dias,et al.  Towards Microscopic Calibration of Pedestrian Simulation Models Using Open Trajectory Datasets: The Case Study of the Edinburgh Informatics Forum , 2017, Traffic and Granular Flow '17.

[30]  Nirajan Shiwakoti,et al.  A review on the performance of an obstacle near an exit on pedestrian crowd evacuation , 2019, Safety Science.

[31]  Chia Hsun Chiang,et al.  A comparative study of implementing Fast Marching Method and A* SEARCH for mobile robot path planning in grid environment: Effect of map resolution , 2007, 2007 IEEE Workshop on Advanced Robotics and Its Social Impacts.

[32]  Serge P. Hoogendoorn,et al.  Continuum modelling of pedestrian flows - Part 2: Sensitivity analysis featuring crowd movement phenomena , 2016 .

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

[34]  Nirajan Shiwakoti,et al.  Modelling pedestrian behaviour under emergency conditions - State-of-the-art and future directions , 2008 .

[35]  Yoshihiro Ishibashi,et al.  Jamming Transition in Cellular Automaton Models for Pedestrians on Passageway , 1999 .

[36]  A. Schadschneider Cellular Automaton Approach to Pedestrian Dynamics - Theory , 2001, cond-mat/0112117.

[37]  Katsuhiro Nishinari,et al.  Chapter Eleven – Pedestrian Dynamics , 2011 .

[38]  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.

[39]  Severino F. Galán,et al.  Fast Evacuation Method: Using an effective dynamic floor field based on efficient pedestrian assignment , 2019 .

[40]  Nirajan Shiwakoti,et al.  A State-of-the-Art Review on Empirical Data Collection for External Governed Pedestrians Complex Movement , 2018, Journal of Advanced Transportation.