Skip to main content
SpringerLink
Log in

A Relationship-Based Pedestrian Social Groups Model

  • Conference paper
  • First Online:
Book cover Computer Supported Cooperative Work and Social Computing (ChineseCSCW 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 917))

  • 857 Accesses

Abstract

In the crowd evacuation process, pedestrians will produce self-organization phenomenon. Family members, friends and other closely related people will be formed group according to the degree of intimacy. The closer the relationship between people, the higher their aggregation in the group. Although the original social force model can simulate arching in the portals, “faster-is slower” and other phenomena. But in the process of movement, pedestrians are an isolated individuals, who have no association with the rest of surrounding people. This cannot truly reflect the group characteristics during crowd movement process. To simulate this pedestrian behavior in the process of movement, a social groups force model is proposed. However, the social groups model does not take the influence of strength of group membership on the group behavior into account. In view of the above shortcomings, this paper proposes a pedestrian social groups force model based on relationship strength. To consider the influence of the relationship strength on the group attribution force in the model, the model can reflect the features of pedestrian behaviors in the process of movement, and achieve a more efficient and more realistic crowd movement.

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

Access this chapter

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 EPUB and 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

Institutional subscriptions

References

  1. Porzycki, J., Mycek, M., Lubaś, R., Wąs, J.: Pedestrian spatial self-organization according to its nearest neighbor position. Transp. Res. Procedia 2(2), 201–206 (2014)

    Article  Google Scholar 

  2. Miguel, A.F.: The emergence of design in pedestrian dynamics: locomotion, self-organization, walking paths and constructal law. Phys. Life Rev. 10(2), 168–190 (2013)

    Article  Google Scholar 

  3. Deb, S., Strawderman, L., Dubien, J., Smith, B., Carruth, D.W., Garrison, T.M.: Evaluating pedestrian behavior at crosswalks: validation of a pedestrian behavior questionnaire for the U.S. population. Accid. Anal. Prev. 106, 191–201 (2017)

    Article  Google Scholar 

  4. Sakuma, T., Mukai, T., Kuriyama, S.: Psychological model for animating crowded pedestrians. Comput. Animat. Virtual Worlds 16(3–4), 343–351 (2005)

    Article  Google Scholar 

  5. EXODU. http://fseg.gre.ac.uk. Accessed 28 Dec 2016

  6. Myriad. http://www.crowddynamics.com. Accessed 2 Mar 2017

  7. Pathfinder. http://www.thunderheadeng.com/pathfinder. Accessed 10 May 2018

  8. Wang, Z., Mao, T., Jiang, H., Xia, S.: Guarder: virtual drilling system for crowd evacuation under emergency scheme. J. Comput. Res. Dev. 47(6), 969–978 (2010)

    Google Scholar 

  9. Gomez-Sanz, J., Pax, R., Arroyo, M., Cárdenas-Bonett, M.: Requirement engineering activities in smart environments for large facilities. Comput. Sci. Inf. Syst. 14, 3 (2017)

    Article  Google Scholar 

  10. Henderson, L.F.: On the fluid mechanics of human crowd motion. Transp. Res. 8(6), 509–515 (1974)

    Article  Google Scholar 

  11. Wen, J., Tian, H.H., Xue, Y.: Lattice hydrodynamic model for pedestrian traffic with the next-nearest-neighbor pedestrian. Acta Phys. Sin. 59(6), 3817–3823 (2010)

    MATH  Google Scholar 

  12. Helbing, D., Johansson, A., Al-Abideen, H.Z.: Dynamics of crowd disasters: an empirical study. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 75(2), 046109 (2007)

    Article  Google Scholar 

  13. Helbing, D., Johansson, A.: Pedestrian, crowd, and evacuation dynamics. Encycl. Complex. Syst. Sci. 16, 697–716 (2012)

    Google Scholar 

  14. Von Neumann, J.: The general and logical theory of automata. In: Papers of John Von Neumann on Computing & Computer Theory, vol. 1, no. 41, pp. 1–2 (1951)

    Google Scholar 

  15. Chen, C.K., Li, J., Zhang, D.: Study on evacuation behaviors at a t-shaped intersection by a force-driving cellular automata model. Physica A 391(7), 2408–2420 (2012)

    Article  Google Scholar 

  16. Johansson, A., Helbing, D., Shukla, P.K.: Specification of the social force pedestrian model by evolutionary adjustment to video tracking data. Adv. Complex Syst. 10(Supp 02), 271–288 (2007)

    Article  MathSciNet  Google Scholar 

  17. Zeng, W., Nakamura, H., Chen, P.: A modified social force model for pedestrian behavior simulation at signalized crosswalks. Procedia Soc. Behav. Sci. 138, 521–530 (2014)

    Article  Google Scholar 

  18. Han, Y., Liu, H.: Modified social force model based on information transmission toward crowd evacuation simulation. Physica A 469, 499–509 (2016)

    Article  Google Scholar 

  19. Hou, L., Liu, J.G., Pan, X., Wang, B.H.: A social force evacuation model with the leadership effect. Physica A 400(2), 93–99 (2014)

    Article  Google Scholar 

  20. Wang, L., Cai, Y., Xu, Q.: Modifications to social force model. J. Nanjing Univ. Sci. Technol. 35(1), 144–149 (2011)

    Google Scholar 

  21. Shang, R., Zhang, P., Zhong, M.: Investigation and analysis on evacuation behavior of large scale population in campus ☆. Procedia Eng. 52(7), 302–308 (2013)

    Article  Google Scholar 

  22. Yang, X., Dong, H., Wang, Q., Chen, Y., Hu, X.: Guided crowd dynamics via modified social force model. Physica A 411(10), 63–73 (2014)

    Article  MathSciNet  Google Scholar 

  23. Xi, H., Son, Y.J., Lee, S.: An integrated pedestrian behavior model based on extended decision field theory and social force model. In: Rothrock, L., Narayanan, S. (eds.) Human-in-the-Loop Simulation: Methods and Practice, pp. 824–836. Springer, London (2010). https://doi.org/10.1007/978-0-85729-883-6_4

    Chapter  Google Scholar 

  24. Lu, L., Chan, C.Y., Wang, J., Wang, W.: A study of pedestrian group behaviors in crowd evacuation based on an extended floor field cellular automaton model. Transp. Res. C Emerg. Technol. 81, 317–329 (2016)

    Article  Google Scholar 

  25. Li, Y., Liu, H., Liu, G.P., Li, L., Moore, P., Hu, B.: A grouping method based on grid density and relationship for crowd evacuation simulation. Physica A 473, 319–336 (2017)

    Article  Google Scholar 

  26. Julien, P., Cretual, A.: Experiment-based modeling, simulation and validation of interactions between virtual walkers. In: ACM Siggraph/Eurographics Symposium on Computer Animation, pp. 189–198. ACM, New Orleans (2009)

    Google Scholar 

  27. Gonzalez-Pardo, A., Rosa, A., Camacho, D.: Behaviour-based identification of student communities in virtual worlds. Comput. Sci. Inf. Syst. 11(1), 195–213 (2014)

    Article  Google Scholar 

  28. Kang, H.L., Choi, M.G., Hong, Q., Lee, J.: Group behavior from video: a data-driven approach to crowd simulation. In: ACM Siggraph/Eurographics Symposium on Computer Animation, vol. 2007, pp. 109–118. ACM, San Diego (2007)

    Google Scholar 

  29. Musse, S.R., Thalmann, D.: A model of human crowd behavior: group inter-relationship and collision detection analysis. In: Thalmann, D. (ed.) Computer Animation and Simulation 1997. EUROGRAPH, pp. 39–51. Springer, Vienna (1997). https://doi.org/10.1007/978-3-7091-6874-5_3

    Chapter  Google Scholar 

  30. Vizzari, G., Manenti, L., Crociani, L.: Adaptive pedestrian behaviour for the preservation of group cohesion. Complex Adapt. Syst. Model. 1(7), 1–29 (2013)

    Google Scholar 

  31. Qiu, F., Hu, X.: Modeling group structures in pedestrian crowd simulation. Simul. Model. Pract. Theory 18(2), 190–205 (2010)

    Article  MathSciNet  Google Scholar 

  32. 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)

    Article  Google Scholar 

  33. Liu, B., Liu, H., Zhang, H., Qin, X.: A social force evacuation model driven by video data. Simul. Model. Pract. Theory 84, 190–203 (2018)

    Article  Google Scholar 

  34. Liu, H., Liu, B., Zhang, H., Li, L., Qin, X., Zhang, G.: Crowd evacuation simulation approach based on navigation knowledge and two-layer control mechanism. Inf. Sci. 436–437, 247–267 (2018)

    Article  MathSciNet  Google Scholar 

  35. Qin, X., Liu, H., Zhang, H., Liu, B.: A collective motion model based on two-layer relationship mechanism for bi-direction pedestrian flow simulation. Simul. Model. Practice Theory 84, 268–285 (2018)

    Article  Google Scholar 

Download references

Acknowledgments

This research is supported by the National Natural Science Foundation of China (61472232, 61572299, 61373149 and 61402270) and by the Project of Taishan scholarship.

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Shandong Normal University, Jinan, 250014, China

    Guang-peng Liu, Hong Liu & Liang Li

  2. Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, Jinan, 250358, China

    Guang-peng Liu, Hong Liu & Liang Li

  3. State Grid of China Technology College, Jinan, 250002, China

    Guang-peng Liu

Authors
  1. Guang-peng Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Liang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hong Liu .

Editor information

Editors and Affiliations

  1. Shandong University, Jinan, China

    Yuqing Sun

  2. Fudan University, Shanghai, China

    Tun Lu

  3. Guilin University of Technology, Guilin, China

    Xiaolan Xie

  4. University of Shanghai for Science and Technology, Shanghai , China

    Liping Gao

  5. Tongji University, Shanghai, China

    Hongfei Fan

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Liu, Gp., Liu, H., Li, L. (2019). A Relationship-Based Pedestrian Social Groups Model. In: Sun, Y., Lu, T., Xie, X., Gao, L., Fan, H. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2018. Communications in Computer and Information Science, vol 917. Springer, Singapore. https://doi.org/10.1007/978-981-13-3044-5_19

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-3044-5_19

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-3043-8

  • Online ISBN: 978-981-13-3044-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation