Simulation model of self-organizing pedestrian movement considering following behavior

  • Zhilu Yuan
  • Hongfei Jia
  • Mingjun Liao
  • Linfeng Zhang
  • Yixiong Feng
  • Guangdong Tian


A new force is introduced in the social force model (SFM) for computing following behavior in pedestrian counterflow, whereby an individual tries to approach others in the same direction to avoid conflicts with pedestrians from the opposite direction. The force, like a kind of gravitation, is modeled based on the movement state and visual field of the pedestrian, and is added to the classical SFM. The modified model is presented to study the impact of following behavior on the process of lane formation, the conflict, the number of lanes formed, and the traffic efficiency in the simulations. Simulation results show that the following behavior has a significant effect on the phenomenon of lane formation and the traffic efficiency.

Key words

Gravitation Pedestrian counterflow Social force model (SFM) Lane formation Self-organizing 

CLC number

TP391.9 U698.2 



We would like to thank Dr. Lei BIAN and Dr. Yongjun MA for the stimulating discussions.


  1. Chen, M.J., Bärwolff, G., Schwandt, H., 2009. A derived grid-based model for simulation of pedestrian flow. J. Zhejiang Univ.-Sci. A, 10(2): 209–220. Scholar
  2. Fujiyama, T., Tyler, N., 2009. Bidirectional collisionavoidance behaviour of pedestrians on stairs. Environ. Plan. B, 36(1): 128–148. Scholar
  3. Guo, R.Y., 2014. Simulation of spatial and temporal separation of pedestrian counter flow through a bottleneck. Phys. A, 415: 428–439. Scholar
  4. Helbing, D., 1996. A stochastic behavioral model and a ‘microscopic’ foundation of evolutionary game theory. Theory Dec., 40(2): 149–179. Scholar
  5. Helbing, D., 2001. Traffic and related self-driven manyparticle systems. Rev. Mod. Phys., 73(4): 1067. Scholar
  6. Helbing, D., Molnár, P., 1995. Social force model for pedestrian dynamics. Phys. Rev. E, 51(5): 4282. Scholar
  7. Helbing, D., Farkas, I., Vicsek, T., 2000. Simulating dynamical features of escape panic. Nature, 407(6803): 487–490. Scholar
  8. Helbing, D., Farkas, I.J., Molnár, P., et al., 2002. Simulation of pedestrian crowds in normal and evacuation situations. Proc. 1st Int. Conf. on Pedestrian and Evacuation Dynamics, p.21–58.Google Scholar
  9. Helbing, D., Buzna, L., Johansson, A., et al., 2005. Selforganized pedestrian crowd dynamics: experiments, simulations, and design solutions. Transp. Sci., 39(1): 1–24. Scholar
  10. Heliövaara, S., Korhonen, T., Hostikka, S., et al., 2012. Counterflow model for agent-based simulation of crowd dynamics. Build. Environ., 48: 89–100. Scholar
  11. Iryo-Asano, M., Alhajyaseen, W.K.M., Nakamura, H., 2015. Analysis and modeling of pedestrian crossing behavior during the pedestrian flashing green interval. IEEE Trans. Intell. Transp. Syst., 16(2): 958–969. Scholar
  12. Jia, H.F., Li, Y.X., Yang, L.L., et al., 2016. Modeling the separating pedestrian flow in T-shaped passage based on guide sign. Discr. Dynam. Nat. Soc., 2016: 5625286. Scholar
  13. Kuang, H., Li, X.L., Wei, Y.F., et al., 2010. Effect of following strength on pedestrian counter flow. Chin. Phys. B, 19(7): 070517. Scholar
  14. Lakoba, T.I., Kaup, D.J., Finkelstein, N.M., 2005. Modifications of the Helbing-Molnár-Farkas-Vicsek social force model for pedestrian evolution. Simulation, 81(5): 339–352. Scholar
  15. Lam, W.H.K., Lee, J.Y.S., Cheung, C.Y., 2002. A study of the bi-directional pedestrian flow characteristics at Hong Kong signalized crosswalk facilities. Transportation, 29(2): 169–192. Scholar
  16. Li, J., Yang, L., Zhao, D., 2005. Simulation of bi-direction pedestrian movement in corridor. Phys. A, 354: 619–628. Scholar
  17. Li, J., Wang, J., Dong, Y., et al., 2015. Streamline simulation and analysis of pedestrian weaving flow in large passenger terminal. Math. Probl. Eng., 2015: 645989. Scholar
  18. Li, Y.X., Jia, H.F., Zhou, Y.N., et al., 2017. Simulation research on pedestrian counter flow subconscious behavior. Int. J. Mod. Phys. C, 28(2): 1750025. Scholar
  19. Liao, M.J., Liu, G., 2015. Modeling passenger behavior in nonpayment areas at rail transit stations. Transp. Res. Rec. J. Transp. Res. Board, 2534: 101–108. Scholar
  20. Löhner, R., 2010. On the modeling of pedestrian motion. Appl. Math. Model., 34(2): 366–382. Scholar
  21. Ma, J., Song, W.G., Zhang, J., et al., 2010. k-nearest-neighbor interaction induced self-organized pedestrian counter flow. Phys. A, 389(10): 2101–2117. Scholar
  22. Older, S.J., 1968. Movement of pedestrians on footways in shopping streets. Traff. Eng. Contr., 10(4): 160–163.Google Scholar
  23. Pelechano, N., Allbeck, J.M., Badler, N.I., 2007. Controlling individual agents in high-density crowd simulation. Proc. ACM SIGGRAPH/Eurographics Symp. on Computer Animation, p.99–108. Scholar
  24. Saloma, C., Perez, G.J., Tapang, G., et al., 2003. Selforganized queuing and scale-free behavior in real escape panic. PNAS, 100(21): 11947–11952. Scholar
  25. Seyfried, A., Steffen, B., Klingsch, W., et al., 2005. The fundamental diagram of pedestrian movement revisited. J. Stat. Mech. Theory Exp., 2005(10): P10002. Scholar
  26. Smith, A., James, C., Jones, R., et al., 2009. Modelling contra-flow in crowd dynamics DEM simulation. Safety Sci., 47(3): 395–404. Scholar
  27. Tajima, Y., Takimoto, K., Nagatani, T., 2002. Pattern formation and jamming transition in pedestrian counter flow. Phys. A, 313(3): 709–723. Scholar
  28. Tang, T.Q., Shao, Y.X., Chen, L., 2017. Modeling pedestrian movement at the hall of high-speed railway station during the check-in process. Phys. A, 467: 157–166. Scholar
  29. Wang, Z., Ma, J., Zhao, H., et al., 2012. Effect of prediction on the self-organization of pedestrian counter flow. J. Phys. A, 45(30): 305004. Scholar
  30. Weidmann, U., 1993. Transporttechnik der Fussgänger: Transporttechnische Eigenschaften des Fussgängerverkehrs (Literaturauswertung). ETH Zürich (in German). Scholar
  31. Weng, W.G., Chen, T., Yuan, H.Y., et al., 2006. Cellular automaton simulation of pedestrian counter flow with different walk velocities. Phys. Rev. E, 74(3): 036102. Scholar
  32. Werner, T., Helbing, D., 2003. The social force pedestrian model applied to real life scenarios. Proc. 2nd Int. Conf. on Pedestrian and Evacuation Dynamics, p.17–26.Google Scholar
  33. Yang, L., Li, J., Liu, S., 2008. Simulation of pedestrian counter-flow with right-moving preference. Phys. A, 387(13): 3281–3289. Scholar
  34. Yue, H., Guan, H., Zhang, J., et al., 2010. Study on bidirection pedestrian flow using cellular automata simulation. Phys. A, 389(3): 527–539. Scholar
  35. Zhang, J., Wang, H., Li, P., 2004. Cellular automata modeling of pedestrian’s crossing dynamics. J. Zhejiang Univ.-Sci., 5(7): 835–840. Scholar

Copyright information

© Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.School of TransportationJilin UniversityChangchunChina
  2. 2.Department of Civil EngineeringBeihua UniversityJilinChina
  3. 3.The State Key Lab of Fluid Power Transmission and ControlZhejiang UniversityHangzhouChina

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