Logo PTI
Polish Information Processing Society
Logo FedCSIS

Annals of Computer Science and Information Systems, Volume 18

Proceedings of the 2019 Federated Conference on Computer Science and Information Systems

A Social Bonds Integration Approach for Crowd Panic Simulation

,

DOI: http://dx.doi.org/10.15439/2019F35

Citation: Proceedings of the 2019 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 18, pages 303308 ()

Full text

Abstract. Crowd panic has incurred massive injuries and deaths throughout history; thus understanding it is particularly important in order to save human lives. Recently, numerous simulation methods have been contributed in order to provide insight into the design of evacuation planning strategies. In this paper, we integrate a social structure to the crowd mobility model for the purpose of investigating the influence of social bonds on collective behavior during panic. A macroscopic crowd panic model based on social science theories was integrated as an internal module to the microscopic mobility model. The resulting framework is tunable and permits the implementation of several panic scenarios. It is also designed to run in different situations for a better comprehension of panic-related phenomena. The results demonstrate the smoothness of our crowd flow model and the realism of evacuation during panic.

References

  1. Anthony R Mawson. “Understanding mass panic and other collective responses to threat and disaster”. In: Psychiatry: Interpersonal and biological processes 68.2 (2005), pp. 95–113.
  2. Tibor Bosse et al. “Modelling collective decision making in groups and crowds: Integrating social contagion and interacting emotions, beliefs and intentions”. In: AUTON AGENT MULTI-AG 27.1 (2013), pp. 52–84.
  3. Haifa Abdelhak. “Modélisation des phénomènes de panique dans le cadre de la gestion de crise”. PhD thesis. Université du Havre, 2013.
  4. Zhilu Yuan et al. “Simulation model of self-organizing pedestrian movement considering following behavior”. In: Frontiers of Information Technology & Electronic Engineering 18.8 (2017), pp. 1142–1150.
  5. Peng Lin, Jian Ma, and Siuming Lo. “Discrete element crowd model for pedestrian evacuation through an exit”. In: Chinese Physics B 25.3 (2016), p. 034501.
  6. Mei Ling Chu et al. “Modeling social behaviors in an evacuation simulator”. In: C. Animation and Virtual Worlds 25.3-4 (2014), pp. 373–382.
  7. Jinhuan Wang et al. “Modeling and simulating for congestion pedestrian evacuation with panic”. In: Physica A: Statistical Mechanics and its Applications 428 (2015), pp. 396–409.
  8. Russell Hardin. Collective action. RFF Press, 2015.
  9. Lu Tan, Mingyuan Hu, and Hui Lin. “Agent-based simulation of building evacuation: Combining human behavior with predictable spatial accessibility in a fire emergency”. In: Inf. Sciences 295 (2015), pp. 53–66.
  10. Yan Li et al. “A grouping method based on grid density and relationship for crowd evacuation simulation”. In: Physica A Stat. Mech. Appl. 473 (2017), pp. 319–336.
  11. Dirk Helbing and Peter Molnar. “Social force model for pedestrian dynamics”. In: Physical review E 51.5 (1995), p. 4282.
  12. Farid Kadri, Babiga Birregah, and Eric Châtelet. “The impact of natural disasters on critical infrastructures: A domino effect-based study”. In: J. of Homeland Security and Emergency Management 11.2 (2014), pp. 217–241.
  13. Fatima Zohra Younsi. “Mise ne palce d’un système d’aide à la décision pour le suivi et la prévention des épidémies”. PhD thesis. Université d’Oran, 2016.
  14. Armin Seyfried et al. “New insights into pedestrian flow through bottlenecks”. In: Transportation Science 43.3 (2009), pp. 395–406.
  15. Robson dos Santos França, Maria das Graças Bruno Marietto, and Margarethe Born Steinberger. “A Multi-agent Model for Panic Behavior in Crowds”. In: (2009).
  16. Bachar Kabalan. “Crowd dynamics: modeling pedestrian movement and associated generated forces”. PhD thesis. Université Paris-Est, 2016.