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Simulation model of self-organizing pedestrian movement considering following behavior

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

Abstract

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 

Notes

Acknowledgements

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

References

  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. https://doi.org/10.1631/jzus.A0820049CrossRefGoogle Scholar
  2. Fujiyama, T., Tyler, N., 2009. Bidirectional collisionavoidance behaviour of pedestrians on stairs. Environ. Plan. B, 36(1): 128–148. https://doi.org/10.1068/b33123CrossRefGoogle Scholar
  3. Guo, R.Y., 2014. Simulation of spatial and temporal separation of pedestrian counter flow through a bottleneck. Phys. A, 415: 428–439. https://doi.org/10.1016/j.physa.2014.08.036MathSciNetCrossRefGoogle Scholar
  4. Helbing, D., 1996. A stochastic behavioral model and a ‘microscopic’ foundation of evolutionary game theory. Theory Dec., 40(2): 149–179. https://doi.org/10.1007/BF00133171MathSciNetCrossRefGoogle Scholar
  5. Helbing, D., 2001. Traffic and related self-driven manyparticle systems. Rev. Mod. Phys., 73(4): 1067. https://doi.org/10.1103/RevModPhys.73.1067CrossRefGoogle Scholar
  6. Helbing, D., Molnár, P., 1995. Social force model for pedestrian dynamics. Phys. Rev. E, 51(5): 4282. https://doi.org/10.1103/PhysRevE.51.4282CrossRefGoogle Scholar
  7. Helbing, D., Farkas, I., Vicsek, T., 2000. Simulating dynamical features of escape panic. Nature, 407(6803): 487–490. https://doi.org/10.1038/35035023CrossRefGoogle 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. https://doi.org/10.1287/trsc.1040.0108CrossRefGoogle 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. https://doi.org/10.1016/j.buildenv.2011.08.020CrossRefGoogle 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. https://doi.org/10.1109/TITS.2014.2346154Google 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. https://doi.org/10.1155/2016/5625286Google 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. https://doi.org/10.1088/1674-1056/19/7/070517CrossRefGoogle 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. https://doi.org/10.1177/0037549705052772CrossRefGoogle 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. https://doi.org/10.1023/A:1014226416702CrossRefGoogle Scholar
  16. Li, J., Yang, L., Zhao, D., 2005. Simulation of bi-direction pedestrian movement in corridor. Phys. A, 354: 619–628. https://doi.org/10.1016/j.physa.2005.03.007CrossRefGoogle 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. https://doi.org/10.1155/2015/645989Google 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. https://doi.org/10.1142/S0129183117500255CrossRefGoogle 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. https://doi.org/10.3141/2534-13CrossRefGoogle Scholar
  20. Löhner, R., 2010. On the modeling of pedestrian motion. Appl. Math. Model., 34(2): 366–382. https://doi.org/10.1016/j.apm.2009.04.017MathSciNetCrossRefGoogle 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. https://doi.org/10.1016/j.physa.2010.01.014CrossRefGoogle 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. https://doi.org/10.2312/SCA/SCA07/099-108Google 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. https://doi.org/10.1073/pnas.2031912100CrossRefGoogle 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. https://doi.org/10.1088/1742-5468/2005/10/P10002CrossRefGoogle 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. https://doi.org/10.1016/j.ssci.2008.05.006CrossRefGoogle Scholar
  27. Tajima, Y., Takimoto, K., Nagatani, T., 2002. Pattern formation and jamming transition in pedestrian counter flow. Phys. A, 313(3): 709–723. https://doi.org/10.1016/S0378-4371(02)00965-2CrossRefGoogle 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. https://doi.org/10.1016/j.physa.2016.10.008CrossRefGoogle 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. https://doi.org/10.1088/1751-8113/45/30/305004CrossRefGoogle Scholar
  30. Weidmann, U., 1993. Transporttechnik der Fussgänger: Transporttechnische Eigenschaften des Fussgängerverkehrs (Literaturauswertung). ETH Zürich (in German). https://doi.org/10.3929/ethz-a-000687810Google 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. https://doi.org/10.1103/PhysRevE.74.036102CrossRefGoogle 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. https://doi.org/10.1016/j.physa.2008.01.107CrossRefGoogle 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. https://doi.org/10.1016/j.physa.2009.09.035CrossRefGoogle 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. https://doi.org/10.1631/jzus.2004.0835CrossRefGoogle 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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