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Adaptive Agent-Based Modeling Framework for Collective Decision-Making in Crowd Building Evacuation


Crowd evacuation in different situations is an important topic in the research field of safety. This paper presents a hybrid model for heterogeneous pedestrian evacuation simulation. Our adaptive agent-based model (ABM) combines the strength of human crowd behavior description from classical social force models with discrete dynamics expression from cellular automaton models by extending the conception of floor field. Several important factors which may influence the results of decision-making of pedestrians are taken into consideration, such as the location of sign, the attraction of exit, and the interaction among pedestrians. To compare the effect of information on the pedestrians, we construct three decision-making mechanisms with different assumptions. To validate these three simulation models, we compare the numerical results from different perspectives with rational range in the case study where the Tampere Theater evacuation was carried out. The ABM framework is open for rules modification and could be applied to different building plans and has implication for architectural design of gates and signs in order to increase the evacuation efficiency.

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  1. [1]

    GWYNNE S, GALEA E R, OWEN M, et al. A review of the methodologies used in the computer simulation of evacuation from the built environment [J]. Building and Environment, 1999, 34(6): 741–749.

    Article  Google Scholar 

  2. [2]

    TEKNOMO K, TAKEYAMA Y, INAMURA H. Review on microscopic pedestrian simulation model [C]//Proceedings Japan Society of Civil Engineering Conference. Morioka, Japan: Japan Society of Civil Engineering, 2000: 1–2.

    Google Scholar 

  3. [3]

    KOK V J, LIM M K, CHAN C S. Crowd behavior analysis: A review where physics meets biology [J]. Neurocomputing, 2016, 177: 342–362.

    Article  Google Scholar 

  4. [4]

    ADRIAN J, BODE N, AMOS M, et al. A glossary for research on human crowd dynamics [J]. Collective Dynamics, 2019, 4: A19.

    Article  Google Scholar 

  5. [5]

    ZHENG X P, ZHONG T K, LIU M T. Modeling crowd evacuation of a building based on seven methodological approaches [J]. Building and Environment, 2009, 44(3): 437–445.

    Article  Google Scholar 

  6. [6]

    AL-NABHAN N, AL-ABOODY N, ALIM AL ISLAM A B M. A hybrid IoT-based approach for emergency evacuation [J]. Computer Networks, 2019, 155: 87–97.

    Article  Google Scholar 

  7. [7]

    HELBING D, MOLNÁR P. Social force model for pedestrian dynamics [J]. Physical Review E, 1995, 51(5): 4282.

    Article  Google Scholar 

  8. [8]

    HELBING D, TILCH B. Generalized force model of traffic dynamics [J]. Physical Review E, 1998, 58(1): 133.

    Article  Google Scholar 

  9. [9]

    FREDKIN E, TOFFOLI T. Conservative logic [J]. International Journal of Theoretical Physics, 1982, 21(3/4): 219–253.

    MathSciNet  MATH  Article  Google Scholar 

  10. [10]

    BLUE V J, ADLER J L. Cellular automata microsimulation for modeling bi-directional pedestrian walkways [J]. Transportation Research Part B: Methodological, 2001, 35(3): 293–312.

    Article  Google Scholar 

  11. [11]

    HENDERSON L F. The statistics of crowd fluids [J]. Nature, 1971, 229(5284): 381–383.

    Article  Google Scholar 

  12. [12]

    HA V, LYKOTRAFITIS G. Agent-based modeling of a multi-room multi-floor building emergency evacuation [J]. Physica A: Statistical Mechanics and Its Applications, 2012, 391(8): 2740–2751.

    Article  Google Scholar 

  13. [13]

    LO S M, HUANG H C, WANG P, et al. A game theory based exit selection model for evacuation [J]. Fire Safety Journal, 2006, 41(5): 364–369.

    Article  Google Scholar 

  14. [14]

    SALOMA C, PEREZ G J, TAPANG G, et al. Self-organized queuing and scale-free behavior in real escape panic [J]. Proceedings of the National Academy of Sciences of the United States of America, 2003, 100(21): 11947–11952.

    Article  Google Scholar 

  15. [15]

    LEWIN K. Field theory in social science [J]. The American Catholic Sociological Review, 1951, 12(2): 103–104.

    Article  Google Scholar 

  16. [16]

    JOHANSSON F, PETERSON A, TAPANI A. Waiting pedestrians in the social force model [J]. Physica A: Statistical Mechanics and Its Applications, 2015, 419: 95–107.

    Article  Google Scholar 

  17. [17]

    LI W H, GONG J H, YU P, et al. Simulation and analysis of congestion risk during escalator transfers using a modified social force model [J]. Physica A: Statistical Mechanics and Its Applications, 2015, 420: 28–40.

    MathSciNet  MATH  Article  Google Scholar 

  18. [18]

    HAN Y B, LIU H. Modified social force model based on information transmission toward crowd evacuation simulation [J]. Physica A: Statistical Mechanics and Its Applications, 2017, 469: 499–509.

    Article  Google Scholar 

  19. [19]

    FARINA F, FONTANELLI D, GARULLI A, et al. Walking ahead: The headed social force model [J]. PLoS One, 2017, 12(1): e0169734.

    Article  Google Scholar 

  20. [20]

    LIU B, LIU H, ZHANG H, et al. A social force evacuation model driven by video data [J]. Simulation Modelling Practice and Theory, 2018, 84: 190–203.

    Article  Google Scholar 

  21. [21]

    PELECHANO N, ALLBECK J M, BADLER N I. Controlling individual agents in high-density crowd simulation [C]//ACM SIGGRAPH/Eurographics Symposium on Computer Animation. San Diego, USA: ACM, 2007: 99–108.

    Google Scholar 

  22. [22]

    HAGHANI M, SARVI M. Crowd behaviour and motion: Empirical methods [J]. Transportation Research Part B: Methodological, 2018, 107: 253–294.

    Article  Google Scholar 

  23. [23]

    KRETZ T. On oscillations in the social force model [J]. Physica A: Statistical Mechanics and Its Applications, 2015, 438: 272–285.

    Article  Google Scholar 

  24. [24]

    WENG W G, CHEN T, YUAN H Y, et al. Cellular automaton simulation of pedestrian counter flow with different walk velocities [J]. Physical Review E, 2006, 74: 036102.

    Article  Google Scholar 

  25. [25]

    SONG W G, YU Y F, WANG B H, et al. Evacuation behaviors at exit in CA model with force essentials: A comparison with social force model [J]. Physica A: Statistical Mechanics and Its Applications, 2006, 371(2): 658–666.

    Article  Google Scholar 

  26. [26]

    YANG L Z, ZHAO D L, LI J, et al. Simulation of evacuation behaviors in fire using spacial grid [J]. Progress in Natural Science, 2004, 14(7): 614–618.

    Article  Google Scholar 

  27. [27]

    GUO Y W, MILEHAM A R, OWEN G W, et al. Operation sequencing optimization for five-axis prismatic parts using a particle swarm optimization approach [J]. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 2009, 223(5): 485–497.

    Article  Google Scholar 

  28. [28]

    LU L L, CHAN C Y, WANG J, et al. A study of pedestrian group behaviors in crowd evacuation based on an extended floor field cellular automaton model [J]. Transportation Research Part C: Emerging Technologies, 2017, 81: 317–329.

    Article  Google Scholar 

  29. [29]

    PEREIRA L A, BURGARELLI D, DUCZMAL L H, et al. Emergency evacuation models based on cellular automata with route changes and group fields [J]. Physica A: Statistical Mechanics and Its Applications, 2017, 473: 97–110.

    MATH  Article  Google Scholar 

  30. [30]

    CZERNIAK J M, ZARZYCKI H, APIECIONEK L, et al. A cellular automata-based simulation tool for real fire accident prevention [J]. Mathematical Problems in Engineering, 2018, 2018: 1–12.

    Article  Google Scholar 

  31. [31]

    GOLDSTONE R L, JANSSEN M A. Computational models of collective behavior [J]. Trends in Cognitive Sciences, 2005, 9(9): 424–430.

    Article  Google Scholar 

  32. [32]

    LUBAŚ R, MYCEK M, PORZYCKI J, et al. Verification and validation of evacuation models-methodology expansion proposition [J]. Transportation Research Procedia, 2014, 2: 715–723.

    Article  Google Scholar 

  33. [33]

    MACAL C M. Everything you need to know about agent-based modelling and simulation [J]. Journal of Simulation, 2016, 10(2): 144–156.

    Article  Google Scholar 

  34. [34]

    MARZOUK M, MOHAMED B. Integrated agent-based simulation and multi-criteria decision making approach for buildings evacuation evaluation [J]. Safety Science, 2019, 112: 57–65.

    Article  Google Scholar 

  35. [35]

    BONABEAU E. Agent-based modeling: Methods and techniques for simulating human systems [J]. PNAS, 2002, 99(Sup. 3): 7280–7287.

    Article  Google Scholar 

  36. [36]

    AL HATTAB M, HAMZEH F. Simulating the dynamics of social agents and information flows in BIM-based design [J]. Automation in Construction, 2018, 92: 1–22.

    Article  Google Scholar 

  37. [37]

    POULOS A, TOCORNAL F, DE LA LLERA J C, et al. Validation of an agent-based building evacuation model with a school drill [J]. Transportation Research Part C: Emerging Technologies, 2018, 97: 82–95.

    Article  Google Scholar 

  38. [38]

    HELBING D, FARKAS I, VICSEK T. Simulating dynamical features of escape panic [J]. Nature, 2000, 407(6803): 487–490.

    Article  Google Scholar 

  39. [39]

    PAN X, HAN C S, DAUBER K, et al. A multi-agent based framework for the simulation of human and social behaviors during emergency evacuations [J]. AI & Society, 2007, 22(2): 113–132.

    Article  Google Scholar 

  40. [40]

    FANG J, EL-TAWIL S, AGUIRRE B. Leader-follower model for agent based simulation of social collective behavior during egress [J]. Safety Science, 2016, 83: 40–47.

    Article  Google Scholar 

  41. [41]

    KASEREKA S, KASORO N, KYAMAKYA K, et al. Agent-Based Modelling and Simulation for evacuation of people from a building in case of fire [J]. Procedia Computer Science, 2018, 130: 10–17.

    Article  Google Scholar 

  42. [42]

    HELBING D, BROCKMANN D, CHADEFAUX T, et al. Saving human lives: What complexity science and information systems can contribute [J]. Journal of Statistical Physics, 2015, 158(3): 735–781.

    MathSciNet  MATH  Article  Google Scholar 

  43. [43]

    BELLOMO N, CLARKE D, GIBELLI L, et al. Human behaviours in evacuation crowd dynamics: From modelling to “big data” toward crisis management [J]. Physics of Life Reviews, 2016, 18: 1–21.

    Article  Google Scholar 

  44. [44]

    CHRAIBI M, TORDEUX A, SCHADSCHNEIDER A, et al. Modelling of pedestrian and evacuation dynamics [M]//Encyclopedia of complexity and systems science. Berlin: Springer, 2018: 1–22.

    Google Scholar 

  45. [45]

    SHI J, REN A, CHEN C. Agent-based evacuation model of large public buildings under fire conditions [J]. Automation in Construction, 2009, 18(3): 338–347.

    Article  Google Scholar 

  46. [46]

    ZARBOUTIS N, MARMARAS N. Searching efficient plans for emergency rescue through simulation: The case of a metro fire [J]. Cognition, Technology & Work, 2004, 6(2): 117–126.

    Article  Google Scholar 

  47. [47]

    TOYAMA M C, BAZZAN A L C, DA SILVA R. An agent-based simulation of pedestrian dynamics: From lane formation to auditorium evacuation [C]//Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems. Hakodate, Japan: ACM, 2006: 108–110.

    Chapter  Google Scholar 

  48. [48]

    KAN Z Q, YU C C, TAN L, et al. Simulation of evacuation based on multi-agent and cellular automaton [C]//2011 International Conference on Mechatronic Science, Electric Engineering and Computer (MEC). Jilin: IEEE, 2011: 550–553.

    Google Scholar 

  49. [49]

    TAN L, HU M, LIN H. Agent-based simulation of building evacuation: Combining human behavior with predictable spatial accessibility in a fire emergency [J]. Information Sciences, 2015, 295: 53–66.

    MathSciNet  Article  Google Scholar 

  50. [50]

    LIU Z, JACQUES C C, SZYNISZEWSKI S, et al. Agent-based simulation of building evacuation after an earthquake: Coupling human behavior with structural response [J]. Natural Hazards Review, 2016, 17(1): 04015019.

    Article  Google Scholar 

  51. [51]

    LUJAK M, GIORDANI S. Centrality measures for evacuation: Finding agile evacuation routes [J]. Future Generation Computer Systems, 2018, 83: 401–412.

    Article  Google Scholar 

  52. [52]

    KIM H, HAN S. Crowd evacuation simulation using active route choice model based on human characteristics [J]. Simulation Modelling Practice and Theory, 2018, 87: 369–378.

    Article  Google Scholar 

  53. [53]

    WILLIAMS R A. Lessons learned on development and application of agent-based models of complex dynamical systems [J]. Simulation Modelling Practice and Theory, 2018, 83: 201–212.

    Article  Google Scholar 

  54. [54]

    CIMELLARO G P, MAHIN S, DOMANESCHI M. Integrating a human behavior model within an agent-based approach for blasting evacuation [J]. Computer-Aided Civil and Infrastructure Engineering, 2019, 34(1): 3–20.

    Article  Google Scholar 

  55. [55]

    GUTIERREZ-MILLA A, BORGES F, SUPPI R, et al. Crowd evacuations SaaS: An ABM approach [J]. Procedia Computer Science, 2015, 51: 473–482.

    Article  Google Scholar 

  56. [56]

    BURSTEDDE C, KLAUCK K, SCHADSCHNEIDER A, et al. Simulation of pedestrian dynamics using a two-dimensional cellular automaton [J]. Physica A: Statistical Mechanics and Its Applications, 2001, 295(3/4): 507–525.

    MATH  Article  Google Scholar 

  57. [57]

    SIEBERS P O, MACAL C M, GARNETT J, et al. Discrete-event simulation is dead, long live agent-based simulation! [J]. Journal of Simulation, 2010, 4(3): 204–210.

    Article  Google Scholar 

  58. [58]

    KAJI M, INOHARA T. Cellular automaton simulation of unidirectional pedestrians flow in a corridor to reproduce the unique velocity profile of Hagen-Poiseuille flow [J]. Physica A: Statistical Mechanics and Its Applications, 2017, 467: 85–95.

    MATH  Article  Google Scholar 

  59. [59]

    GRIMM V, REVILLA E, BERGER U, et al. Pattern-oriented modeling of agent-based complex systems: Lessons from ecology [J]. Science, 2005, 310(5750): 987–991.

    Article  Google Scholar 

  60. [60]

    ZHAO L, YANG G, WANG W, et al. Herd behavior in a complex adaptive system [J]. PNAS, 2011, 108(37): 15058–15063.

    Article  Google Scholar 

  61. [61]

    JONES J E. On the determination of molecular fields: I. From the variation of the viscosity of a gas with temperature [J]. Proceedings of the Royal Society of London, Series A, 1924, 106(738): 441–462.

    Google Scholar 

  62. [62]

    WAGNER N, AGRAWAL V. An agent-based simulation system for concert venue crowd evacuation modeling in the presence of a fire disaster [J]. Expert Systems With Applications, 2014, 41(6): 2807–2815.

    Article  Google Scholar 

  63. [63]

    LUBAŚ R, MILLER J, MYCEK M, et al. Three different approaches in pedestrian dynamics Modeling: A case study [M]//New results in dependability and computer systems. Heidelberg: Springer, 2013: 285–294.

    Chapter  Google Scholar 

  64. [64]

    TO K, LAI P Y, PAK H K. Jamming of granular flow in a two-dimensional hopper [J]. Physical Review Letters, 2001, 86(1): 71–74.

    Article  Google Scholar 

  65. [65]

    TO K. Jamming transition in two-dimensional hoppers and silos [J]. Physical Review E, 2005, 71: 060301.

    Article  Google Scholar 

  66. [66]

    TANG J, SAGDIPHOUR S, BEHRINGER R P. Jamming and flow in 2D hoppers [J]. AIP Conference Proceedings, 2009, 1145: 515–518.

    Article  Google Scholar 

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Corresponding author

Correspondence to Feier Chen.

Additional information

Foundation item: the Natural Science Foundation of Shanghai (No. 18ZR1420200), the National Natural Science Foundation of China (No. 61603253), and the China Postdoctoral Science Foundation Funded Project (No. 2016M601598)

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Chen, F., Zhao, Q., Cao, M. et al. Adaptive Agent-Based Modeling Framework for Collective Decision-Making in Crowd Building Evacuation. J. Shanghai Jiaotong Univ. (Sci.) 26, 522–533 (2021).

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Key words

  • adaptive agent-based model
  • evacuation simulation
  • emergency
  • interaction
  • heterogeneity

CLC number

  • TP 301.6

Document code

  • A