Skip to main content
SpringerLink
Log in
Book cover

International Conference in Swarm Intelligence

ICSI 2014: Advances in Swarm Intelligence pp 9–18Cite as

PHuNAC Model: Emergence of Crowd’s Swarm Behavior

  • Conference paper

  • 2696 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8794))

Abstract

The swarm behavior of pedestrians in a crowd, generally, causes a global pattern to emerge. A pedestrian crowd simulation system must have this emergence in order to prove its effectiveness. For this reason, the aim of our work is to demonstrate the effectiveness of our model PHuNAC (Personalities’ Human’s Nature of Autonomous Crowds) and also prove that the swarm behavior of pedestrians’ agents in our model allows the emergence of these global patterns. In order to validate our approach, we compared our system with real data. The conducted experiments show that the model is consistent with the various emergent behaviors and thus it provides realistic simulated pedestrian’s behavior.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Durupinar, F., Allbeck, J.M., Pelechano, N., Badler, N.I.: Creating crowd variation with the ocean personality model. In: Padgham, L., Parkes, D.C., Mller, J. (eds.) AAMAS (3), pp. 1217–1220. IFAAMAS (2008)

    Google Scholar 

  2. Moussaïd, M., Perozo, N., Garnier, S., Helbing, D., Theraulaz, G.: The Walking Behaviour of Pedestrian Social Groups and Its Impact on Crowd Dynamics. PLoS ONE 5(4), e10047+ (2010)

    Google Scholar 

  3. Fridman, N., Kaminka, G.A.: Towards a cognitive model of crowd behavior based on social comparison theory. In: AAAI, pp. 731–737 (2007)

    Google Scholar 

  4. Beltaief, O., Hadouaj, S., Ghedira, K.: Multi-agent simulation model of pedestrians crowd based on psychological theories. In: 4th International Conference on Logistics, LOGISTIQUA (2011)

    Google Scholar 

  5. Beltaief, O., et al.: PHuNAC Model: Creating Crowd Variation with the MBTI Personality Model. In: Corchado, J.M., et al. (eds.) PAAMS 2014. CCIS, vol. 430, pp. 179–190. Springer, Heidelberg (2014)

    Google Scholar 

  6. Helbing, D.: A fluid-dynamic model for the movement of pedestrians. Complex Systems 6, 391–415 (1992)

    MATH  MathSciNet  Google Scholar 

  7. Smith, R.A.: Density, velocity and flow relationships for closely packed crowds. Safety Science 18, 321–327 (1995)

    Article  Google Scholar 

  8. Cusack, M.A.: Modelling aggregate-level entities as a continuum. Mathematical and Computer Modelling of Dynamical Systems 8, 33–48 (2002)

    Article  MATH  Google Scholar 

  9. Reynolds, C.W.: Flocks, herds, and schools: A distributed behavioral model. In: SIGGRAPH 1987: Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques, New York, NY, USA, pp. 25–34 (1987)

    Google Scholar 

  10. Xiong, M., Lees, M., Cai, W., Zhou, S., Low, M.Y.-H.: A Rule-Based Motion Planning for Crowd Simulation. In: Ugail, H., Qahwaji, R., Earnshaw, R.A., Willis, P.J. (eds.) 2009 International Conference on Cyber Worlds, Bradford, West Yorkshire, UK, September 7-11, pp. 88–95. IEEE Computer Society (2009)

    Google Scholar 

  11. Klupfel, H.: The simulation of crowds at very large events. In: Schadschneider, A., Khne, R., Pschel, T., Schreckenberg, M., Wolf, D.E. (eds.) Trafic and Granular Flow 2005, Berlin (2006)

    Google Scholar 

  12. Weifeng, Y., Hai, T.K.: A novel algorithm of simulating multi-velocity evacuation based on cellular automata modeling and tenability condition. Physica A: Statistical Mechanics and its Applications 379(1), 250–262 (2007)

    Article  Google Scholar 

  13. Cherif, F., Chighoub, R.: Crowd simulation influenced by agent’s socio-psychological state (2010)

    Google Scholar 

  14. Musse, S.R., Thalmann, D.: A model of human crowd behavior: Group interrelationship and collision detection analysis. In: Workshop Computer Animation and Simulation of Eurographics, pp. 39–52 (1997)

    Google Scholar 

  15. Helbing, D., Farkas, I., Moln‘ar, P., Vicsek, T.: Simulation of pedestrian crowds in normal and evacuation situations. In: Schreckenberg, M., Sharma, S.D. (eds.) Pedestrian and Evacuation Dynamics, Berlin, pp. 21–58 (2002)

    Google Scholar 

  16. Still, G.: Crowd Dynamics, pp. 640–655 (2000)

    Google Scholar 

  17. Challenger, R., Clegg, C.W., Robinson, M.A.: Understanding Crowd Behaviours, Cabinet Office Emergency Planning College (2009)

    Google Scholar 

  18. Bonabeau, E.: Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences of the United States of America 99(Suppl. 3), 7280–7287 (2002)

    Article  Google Scholar 

  19. Furnham, A.: The big five versus the big four: The relationship between the Myers-Briggs Type Indicator (MBTI) and NEO-PI five factor model of personality. Personality and Individual Differences (1996)

    Google Scholar 

  20. Johnsson, F.: Personality measures under focus: The NEO-PI-R and the MBTI. Griffith University Undergraduate Student Journal 1, 32–42 (2009)

    Google Scholar 

  21. Wurster, C.D. : Myers-Briggs Type Indicator: A cultural and ethical evaluation. Executive Research Project. National Defense University, Washington D.C. (1993) (retrieved May 5, 2008)

    Google Scholar 

  22. Ando, K., Oto, H., Aoki, T.: Forecasting the Flow of People. Railway Research Review 45(8), 8–13 (1988) (in Japanese)

    Google Scholar 

  23. Howard, P.J.: The Mbti And The Five Factor Model. Center for Applied Cognitive Studies (CentACS), Charlotte (2006)

    Google Scholar 

  24. Helbing, D., Molnar, P., Farkas, I.J., Bolay, K.: Self-organizing pedestrian movement. Environment and Planning B: Planning and Design 28(3), 361–383 (2001)

    Article  Google Scholar 

  25. JMH Consultancy, Introducing the Myers Briggs Type Indicator (MBTI) (2006)

    Google Scholar 

  26. Oosterhout, D.: Automatic Modeling of Personality Types. at the Universiteit van Amsterdam (2009)

    Google Scholar 

  27. Zevenbergen, R.: Automatic modeling of personality types: the 2-qubit model. Universiteit van Amsterdam (2010)

    Google Scholar 

  28. Wilde, D.J.: Jung’s Personality Theory Quantified (2011), ISBN: 978-0-85729-099-1 (Print) 978-0-85729-100-4

    Google Scholar 

  29. Hoogendoorn, S.P.: Pedestrian Flow Modeling by Adaptive Control. In: Transportation Research Board Annual Meeting (2004a)

    Google Scholar 

  30. Burden, A.M.: New york city pedestrian level of service study phase i. Technical report, City of New York (2006)

    Google Scholar 

  31. Fruin, J.J.: Pedestrian Planning and Design. Metropolitan association of urban designers and environmental, New York (1971)

    Google Scholar 

  32. Shao, W., Terzopoulos, D.: Autonomous pedestrians. Graphical Models 69(5-6), 246–274 (2007)

    Article  Google Scholar 

  33. Treuille, A., Cooper, S., Popovi´c, Z.: Continuum crowds. In: SIG-GRAPH 2006: ACM SIGGRAPH 2006 Papers, New York, NY, USA, pp. 1160–1168 (2006)

    Google Scholar 

  34. Pelechano, N., Allbeck, J.M., Badler, N.I.: Controlling individual agents in high-density crowd simulation. In: SCA 2007: Proceedings of the 2007 ACM SIG- GRAPH/Eurographics Symposium on Computer Animation, vol. 16, pp. 99–108. Eurographics Association, Aire-la-Ville (2007)

    Google Scholar 

  35. Tasse, F.P.: Crowd simulation of pedestrians in a virtual city. Technical Report Honours Project Report, Virtual Reality Special Interest Group, Computer Science Department, Rhodes University, Grahamstown, South Africa (November 2008)

    Google Scholar 

  36. Zanlungo, F.: Microscopic dynamics of articial life systems. Springer (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratory SOIE, ISG of Tunis, Tunisia

    Olfa Beltaief & Khaled Ghedira

  2. Laboratory SOIE, FSEG Nabeul, Tunisia

    Sameh El Hadouaj

Authors
  1. Olfa Beltaief
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sameh El Hadouaj
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Khaled Ghedira
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Key Laboratory of Machine Perception (MOE), Peking University, 100871, Beijing, China

    Ying Tan

  2. Department of Electrical & Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  3. Computer Science Department, CINVESTAV-IPN, Mexico

    Carlos A. Coello Coello

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Beltaief, O., El Hadouaj, S., Ghedira, K. (2014). PHuNAC Model: Emergence of Crowd’s Swarm Behavior. In: Tan, Y., Shi, Y., Coello, C.A.C. (eds) Advances in Swarm Intelligence. ICSI 2014. Lecture Notes in Computer Science, vol 8794. Springer, Cham. https://doi.org/10.1007/978-3-319-11857-4_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-11857-4_2

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11856-7

  • Online ISBN: 978-3-319-11857-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation