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A Personality-based Model of Emotional Contagion and Control in Crowd Queuing Simulations

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Published:28 February 2023Publication History
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Abstract

Queuing is a frequent daily activity. However, long waiting lines equate to frustration and potential safety hazards. We present a novel, personality-based model of emotional contagion and control for simulating crowd queuing. Our model integrates the influence of individual personalities and interpersonal relationships. Through the interaction between the agents and the external environment parameters, the emotional contagion model based on well-known theories in psychology is used to complete the agents’ behavior planning and path planning function. We combine the epidemiological SIR model with the cellular automaton model to capture various emotional modelling for multi-agent simulations. The overall formulation involves different emotional parameters, such as patience, urgency, and friendliness, closely related to crowd queuing. In addition, to manage the order of the queue, governing agents are added to prevent the emotional outbreak. We perform qualitative and quantitative comparisons between our simulation results and real-world observations on various scenarios. Numerous experiments show that reasonably increasing the queue channel and adding governing agents can effectively improve the quality of queues.

REFERENCES

  1. [1] Barrett Lisa Feldman, Gross James, Christensen Tamlin Conner, and Benvenuto Michael. 2001. Knowing what you’re feeling and knowing what to do about it: Mapping the relation between emotion differentiation and emotion regulation. Cog. Emot. 15, 6 (Nov.2001), 713724. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  2. [2] Başak Ahmet Eren, Gúdükbay Uğur, and Durupınar Funda. 2018. Using real life incidents for creating realistic virtual crowds with data-driven emotion contagion. Comput. Graph. 72 (May2018), 7081. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  3. [3] Bode Nikolai. 2020. Parameter calibration in crowd simulation models using approximate Bayesian computation. Collect. Dynam. 5 (Mar.2020), 340347. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  4. [4] Bosse T., Duell R., Memon Z. A., Treur J., Wal C. N. van der, Otamendi J., Bargiela A., Montes J. L., and Pedrera L. M. D.. 2009. A multi-agent model for mutual absorption of emotions. In Proceedings of the 23rd European Conference on Modelling and Simulation (ECMS’09). [J]: European Council on Modeling and Simulation, 212218.Google ScholarGoogle ScholarCross RefCross Ref
  5. [5] Bosse Tibor, Duell Rob, Memon Zulfiqar A., Treur Jan, and Wal C. Natalie van der. 2015. Agent-based modeling of emotion contagion in groups. Cog. Computat. 7, 1 (2015), 111136. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  6. [6] Brogan David C. and Hodgins Jessica K.. 1997. Group behaviors for systems with significant dynamics. Auton. Robots 4, 1 (1997), 137153. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  7. [7] Brown Ben. 2015. Cops and chaos: A historical examination of the police role in riot control. J. Appl. Secur. Res. 10, 4 (2015), 427465. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  8. [8] Burstedde Carsten, Kirchner Ansgar, Klauck Kai, Schadschneider Andreas, and Zittartz Johannes. 2001. Cellular automaton approach to pedestrian dynamics-applications. In Pedestrian and Evacuation Dynamics. Springer, 8798.Google ScholarGoogle Scholar
  9. [9] Cao Mengxiao, Zhang Guijuan, Wang Mengsi, Lu Dianjie, and Liu Hong. 2017. A method of emotion contagion for crowd evacuation. Phys. A: Statist. Mech. Applic. 483 (2017), 250258. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  10. [10] Cattell Raymond B. and Kline Paul Ed. 1977. The Scientific Analysis of Personality and Motivation.Academic Press.Google ScholarGoogle Scholar
  11. [11] Chen Yameng, Wang Chen, Li Heng, Yap Jeffrey Boon Hui, Tang Rui, and Xu Bin. 2020. Cellular automaton model for social forces interaction in building evacuation for sustainable society. Sustain. Cities Societ. 53 (Feb.2020), 101913. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  12. [12] Chenney Stephen. 2004. Flow tiles. In Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation. Eurographics Association, 233242. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. [13] Connor Stephen. 2020. Omnithermal perfect simulation for multi-server queues. ACM Trans. Model. Comput. Simul. 30, 1 (Feb.2020), 115. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. [14] Crippa Monica, Guizzardi Diego, Pisoni Enrico, Solazzo Efisio, Guion Antoine, Muntean Marilena, Florczyk Aneta, Schiavina Marcello, Melchiorri Michele, and Hutfilter Andres Fuentes. 2021. Global anthropogenic emissions in urban areas: Patterns, trends, and challenges. Environ. Res. Lett. 16, 7 (July2021), 074033. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  15. [15] Dickinson Patrick, Gerling Kathrin, Hicks Kieran, Murray John, Shearer John, and Greenwood Jacob. 2018. Virtual reality crowd simulation: Effects of agent density on user experience and behaviour. Virt. Real. 23, 1 (Sep.2018), 1932. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. [16] Durupınar Funda, Güdükbay Uğur, Aman Aytek, and Badler Norman I.. 2016. Psychological parameters for crowd simulation: From audiences to mobs. IEEE Trans. Visualiz. Comput. Graph. 22, 9 (2016), 21452159. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. [17] Durupinar Funda, Pelechano Nuria, Allbeck Jan, Gudukbay Ugur, and Badler Norman I.. 2011. How the ocean personality model affects the perception of crowds. IEEE Comput. Graph. Applic. 31, 3 (2011), 2231. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. [18] Eysenck H. J.. 1973. Chapter 6–personality and the law of effect. In Proceeding of the Pleasure, Reward, Preference, Academic Press, 133–166. Google ScholarGoogle ScholarCross RefCross Ref
  19. [19] Fraichard Thierry and Levesy Valentin. 2020. From crowd simulation to robot navigation in crowds. IEEE Robot. Autom. Lett. 5, 2 (Apr.2020), 729735. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  20. [20] Friend Kenneth E.. 1982. Stress and performance: Effects of subjective work load and time urgency. Personn. Psychol. 35, 3 (1982), 623633. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  21. [21] Fu Libi, Song Weiguo, Lv Wei, and Lo Siuming. 2014. Simulation of emotional contagion using modified SIR model: A cellular automaton approach. Phys. A: Statist. Mech. Applic. 405 (2014), 380391. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  22. [22] Funge John, Tu Xiaoyuan, and Terzopoulos Demetri. 1999. Cognitive modeling: Knowledge, reasoning and planning for intelligent characters. In Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques. ACM, 2938. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. [23] Giddings Franklin H.. 1897. The crowd. A study of the popular mind. By Gustave Le Bon. The Macmillan Co. Science 5, 123 (May1897), 734735. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  24. [24] Goldberg Lewis R.. 1992. The development of markers for the Big-Five factor structure. Psychol. Assess. 4, 1 (1992), 2642. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  25. [25] Green Linda. 2011. Queueing theory and modeling. In Handbook of Healthcare Delivery Systems. Taylor & Francis.Google ScholarGoogle Scholar
  26. [26] Guy Stephen J., Chhugani Jatin, Kim Changkyu, Satish Nadathur, Lin Ming, Manocha Dinesh, and Dubey Pradeep. 2009. ClearPath: Highly parallel collision avoidance for multi-agent simulation. In Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation. ACM, 177187. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. [27] Helbing Dirk, Farkas Illés, and Vicsek Tamas. 2000. Simulating dynamical features of escape panic. Nature 407, 6803 (2000), 487. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  28. [28] Hesham Omar and Wainer Gabriel. 2021. Explicit modeling of personal space for improved local dynamics in simulated crowds. ACM Trans. Model. Comput. Simul. 31, 4 (Oct.2021), 129. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. [29] Hong Xiao, Zhang Guijuan, Lu Dianjie, Liu Hong, Zhu Lei, and Xu Mingliang. 2020. Personalized crowd emotional contagion coupling the virtual and physical cyberspace. IEEE Trans. Syst. Man Cyber.: Syst. (2020), 115. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  30. [30] Karamouzas Ioannis, Geraerts Roland, and Overmars Mark. 2009. Indicative routes for path planning and crowd simulation. In Proceedings of the 4th International Conference on Foundations of Digital Games. ACM, 113120. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. [31] Kermack William Ogilvy and McKendrick Anderson G.. 1927. A contribution to the mathematical theory of epidemics. Proc. Roy. Societ. Lond. Series A, Contain. Papers Math. Phys. Charact. 115, 772 (1927), 700721. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  32. [32] Kim Jooyoung, Ahn Chiwon, and Lee Seungjae. 2018. Modeling handicapped pedestrians considering physical characteristics using cellular automaton. Phys. A: Statist. Mech. Applic. 510 (Nov.2018), 507517. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  33. [33] Kim Sujeong, Guy Stephen J., Manocha Dinesh, and Lin Ming C.. 2012. Interactive simulation of dynamic crowd behaviors using general adaptation syndrome theory. In Proceedings of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games. ACM, 5562. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. [34] Kirchner A., Namazi A., Nishinari Katsuhiro, and Schadschneider A.. 2003. Role of conflicts in the floor field cellular automaton model for pedestrian dynamics. In Proceedings of the 2nd International Conference on Pedestrians and Evacuation Dynamics. 5162.Google ScholarGoogle Scholar
  35. [35] Kluge Boris and Prassler Erwin. 2004. Reflective navigation: Individual behaviors and group behaviors. In Proceedings of the IEEE International Conference on Robotics and Automation. IEEE, 41724177. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  36. [36] Bon Gustave Le. 2002. The Crowd: A Study of the Popular Mind. Foreign Language Teaching and Research Press.Google ScholarGoogle Scholar
  37. [37] Li Chaochao, Lv Pei, Manocha Dinesh, Wang Hua, Li Yafei, Zhou Bing, and Xu Mingliang. 2019. ACSEE: Antagonistic crowd simulation model with emotional contagion and evolutionary game theory. IEEE Trans. Affect. Comput. (2019), 11. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  38. [38] Li D. and Zhong J.. 2020. Dimensionally aware multi-objective genetic programming for automatic crowd behavior modeling. ACM Trans. Model. Comput. Simul. 30, 3 (July2020), 124. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. [39] Liang Chih-Chin. 2017. Enjoyable queuing and waiting time. Time Societ. 28, 2 (May2017), 543566. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  40. [40] Lim C. K., Tan K. L., Zaidan A. A., and Zaidan B. B.. 2019. A proposed methodology of bringing past life in digital cultural heritage through crowd simulation: A case study in George Town, Malaysia. Multim. Tools Applic. 79, 5-6 (July2019), 33873423. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  41. [41] Mandelbaum Avi and Zeltyn Sergey. 2004. The impact of customers’ patience on delay and abandonment: Some empirically-driven experiments with the M/M/n+ G queue. OR Spectrum 26, 3 (2004), 377411. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  42. [42] Miller Rupert G.. 1960. Priority queues. Ann. Math. Statist. 31, 1 (Mar.1960), 86103. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  43. [43] Musse Soraia Raupp and Thalmann Daniel. 2001. Hierarchical model for real time simulation of virtual human crowds. IEEE Trans. Visualiz. Comput. Graph. 7, 2 (2001), 152164. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. [44] Ondřej Jan, Pettré Julien, Olivier Anne-Hélène, and Donikian Stéphane. 2010. A synthetic-vision based steering approach for crowd simulation. ACM Trans. Graph. 29, 4 (2010), 19. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. [45] Ono K. and Suzuki R. O.. 1998. Thermoelectric power generation: Converting low-grade heat into electricity. JOM 50, 12 (1998), 4951. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  46. [46] Paris Sébastien and Donikian Stéphane. 2009. Activity-driven populace: A cognitive approach to crowd simulation. IEEE Comput. Graph. Applic. 29, 4 (2009), 3443. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  47. [47] Rafaeli Anat, Barron Greg, and Haber Keren. 2002. The effects of queue structure on attitudes. J. Serv. Res. 5, 2 (Nov.2002), 125139. DOI:Google ScholarGoogle Scholar
  48. [48] Sandman Peter. 2003. Beyond panic prevention: Addressing emotion in emergency communication. Emergency Risk Communication CDCynergy (CD-ROM), Centers for Disease Control and Prevention, Atlanta.Google ScholarGoogle Scholar
  49. [49] Satsuma Junkichi, Willox R., Ramani A., Grammaticos B., and Carstea A. S.. 2004. Extending the SIR epidemic model. Phys. A: Statist. Mech. Applic. 336, 3-4 (2004), 369375. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  50. [50] Silverman Barry G., Johns Michael, Cornwell Jason, and O’Brien Kevin. 2006. Human behavior models for agents in simulators and games: Part I: Enabling science with PMFserv. Pres.: Teleop. Virt. Environ. 15, 2 (2006), 139162. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. [51] Smits Gerard J. and Cherhoniak Irene M.. 1976. Physical attractiveness and friendliness in interpersonal attraction. Psychol. Rep. 39, 1 (1976), 171174. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  52. [52] Sun Yutong and Liu Hong. 2021. Crowd evacuation simulation method combining the density field and social force model. Phys. A: Statist. Mech. Applic. 566 (Mar.2021), 125652. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  53. [53] Treuille Adrien, Cooper Seth, and Popović Zoran. 2006. Continuum crowds. ACM Trans. Graph. 25, 3 (2006), 11601168. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. [54] Toll Wouter van, Grzeskowiak Fabien, Gandía Axel López, Amirian Javad, Berton Florian, Bruneau Julien, Daniel Beatriz Cabrero, Jovane Alberto, and Pettré Julien. 2020. Generalized microscropic crowd simulation using costs in velocity space. In Proceedings of the Symposium on Interactive 3D Graphics and Games. ACM. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. [55] Wagner Neal and Agrawal Vikas. 2014. An agent-based simulation system for concert venue crowd evacuation modeling in the presence of a fire disaster. Expert Syst. Applic. 41, 6 (2014), 28072815. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  56. [56] Wolinski D., Guy S. J., Olivier A.-H., Lin M., Manocha D., and Pettré J.. 2014. Parameter estimation and comparative evaluation of crowd simulations. Comput. Graph. Forum 33, 2 (May2014), 303312. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  57. [57] Xu Mingliang, Li Chunxu, Lv Pei, Lin Nie, Hou Rui, and Zhou Bing. 2018. An efficient method of crowd aggregation computation in public areas. IEEE Trans. Circ. Syst. Vid. Technol. 28, 10 (2018), 28142825. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  58. [58] Xu Mingliang, Wu Yunpeng, Ye Yangdong, Farkas Illes, Jiang Hao, and Deng Zhigang. 2015. Collective crowd formation transform with mutual information–based runtime feedback. In Computer Graphics Forum, Vol. 34. Wiley Online Library, 6073. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  59. [59] Xu Mingliang, Xie Xiaozheng, Lv Pei, Niu Jianwei, Wang Hua, Li Chaochao, Zhu Ruijie, Deng Zhigang, and Zhou Bing. 2019. Crowd behavior simulation with emotional contagion in unexpected multihazard situations. IEEE Trans. Syst. Man Cyber.: Syst. (2019), 115. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  60. [60] Xue Junxiao, Yin Hui, Lv Pei, Xu Mingliang, and Li Yafei. 2019. Crowd queuing simulation with an improved emotional contagion model. Sci. China Inf. Sci. 62, 4 (2019), 44101. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  61. [61] Yang Shanwen, Li Tianrui, Gong Xun, Peng Bo, and Hu Jie. 2020. A review on crowd simulation and modeling. Graphic. Models 111 (Sep.2020), 101081. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  62. [62] Yao Chengwei and Chen Gencai. 2001. A emotion development agent model based on OCC model and operant conditioning. In Proceedings of the International Conferences on Info-Tech and Info-Net. IEEE, 246250. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  63. [63] Yao Zhenzhen, Zhang Guijuan, Lu Dianjie, and Liu Hong. 2020. Learning crowd behavior from real data: A residual network method for crowd simulation. Neurocomputing 404 (Sep.2020), 173185. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  64. [64] Zhen Liu, Wei Jin, and Peng Huang. 2013. An emotion contagion simulation model for crowd events. J. Comput. Res. Devel. 50, 12 (2013), 25782589.Google ScholarGoogle Scholar

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      cover image ACM Transactions on Modeling and Computer Simulation
      ACM Transactions on Modeling and Computer Simulation  Volume 33, Issue 1-2
      April 2023
      159 pages
      ISSN:1049-3301
      EISSN:1558-1195
      DOI:10.1145/3572857
      Issue’s Table of Contents

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      Publication History

      • Published: 28 February 2023
      • Online AM: 20 December 2022
      • Accepted: 9 December 2022
      • Revised: 12 November 2022
      • Received: 25 October 2021
      Published in tomacs Volume 33, Issue 1-2

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