Full Text:   <9283>

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CLC number: TP391.9; U698.2

On-line Access: 2017-09-08

Received: 2016-09-28

Revision Accepted: 2016-11-22

Crosschecked: 2017-08-19

Cited: 0

Clicked: 18360

Citations:  Bibtex RefMan EndNote GB/T7714


Zhilu Yuan


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Frontiers of Information Technology & Electronic Engineering  2017 Vol.18 No.8 P.1142-1150


Simulation model of self-organizing pedestrian movement considering following behavior

Author(s):  Zhilu Yuan, Hongfei Jia, Mingjun Liao, Linfeng Zhang, Yixiong Feng, Guangdong Tian

Affiliation(s):  School of Transportation, Jilin University, Changchun 130012, China; more

Corresponding email(s):   jiahf@jlu.edu.cn, tiangd2013@163.com

Key Words:  Gravitation, Pedestrian counterflow, Social force model (SFM), Lane formation, Self-organizing

Zhilu Yuan, Hongfei Jia, Mingjun Liao, Linfeng Zhang, Yixiong Feng, Guangdong Tian. Simulation model of self-organizing pedestrian movement considering following behavior[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(8): 1142-1150.

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author="Zhilu Yuan, Hongfei Jia, Mingjun Liao, Linfeng Zhang, Yixiong Feng, Guangdong Tian",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

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%T Simulation model of self-organizing pedestrian movement considering following behavior
%A Zhilu Yuan
%A Hongfei Jia
%A Mingjun Liao
%A Linfeng Zhang
%A Yixiong Feng
%A Guangdong Tian
%J Frontiers of Information Technology & Electronic Engineering
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%@ 2095-9184
%D 2017
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1601592

T1 - Simulation model of self-organizing pedestrian movement considering following behavior
A1 - Zhilu Yuan
A1 - Hongfei Jia
A1 - Mingjun Liao
A1 - Linfeng Zhang
A1 - Yixiong Feng
A1 - Guangdong Tian
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 18
IS - 8
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EP - 1150
%@ 2095-9184
Y1 - 2017
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1601592

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.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[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.

[2]Fujiyama, T., Tyler, N., 2009. Bidirectional collision-avoidance behaviour of pedestrians on stairs. Environ. Plan. B, 36(1):128-148.

[3]%% Guo, R.Y., 2014. Simulation of spatial and temporal separation of pedestrian counter flow through a bottleneck. Phys. A, 415:428-439.

[4]Helbing, D., 1996. A stochastic behavioral model and a ‘microscopic’ foundation of evolutionary game theory. Theory Dec., 40(2):149-179.

[5]Helbing, D., 2001. Traffic and related self-driven many-particle systems. Rev. Mod. Phys., 73(4):1067.

[6]Helbing, D., Molnár, P., 1995. Social force model for pedestrian dynamics. Phys. Rev. E, 51(5):4282.

[7]Helbing, D., Farkas, I., Vicsek, T., 2000. Simulating dynamical features of escape panic. Nature, 407(6803):487-490.

[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.

[9]Helbing, D., Buzna, L., Johansson, A., et al., 2005. Self-organized pedestrian crowd dynamics: experiments, simulations, and design solutions. Transp. Sci., 39(1):1-24.

[10]Heliövaara, S., Korhonen, T., Hostikka, S., et al., 2012. Counterflow model for agent-based simulation of crowd dynamics. Build. Environ., 48:89-100.

[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.

[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.

[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.

[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.

[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.

[16]Li, J., Yang, L., Zhao, D., 2005. Simulation of bi-direction pedestrian movement in corridor. Phys. A, 354:619-628.

[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.

[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.

[19]Liao, M.J., Liu, G., 2015. Modeling passenger behavior in nonpayment areas at rail transit stations. em Transp. Res. Rec. J. Transp. Res. Board, 2534:101-108.

[20]Löhner, R., 2010. On the modeling of pedestrian motion. Appl. Math. Model., 34(2):366-382.

[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.

[22]Older, S.J., 1968. Movement of pedestrians on footways in shopping streets. Traff. Eng. Contr., 10(4):160-163.

[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.

[24]Saloma, C., Perez, G.J., Tapang, G., et al., 2003. Self-organized queuing and scale-free behavior in real escape panic. PNAS, 100(21):11947-11952.

[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.

[26]Smith, A., James, C., Jones, R., et al., 2009. Modelling contra-flow in crowd dynamics DEM simulation. Safety Sci., 47(3):395-404.

[27]Tajima, Y., Takimoto, K., Nagatani, T., 2002. Pattern formation and jamming transition in pedestrian counter flow. Phys. A, 313(3):709-723.

[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.

[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.

[30]Weidmann, U., 1993. Transporttechnik der Fussgänger: Transporttechnische Eigenschaften des Fussgängerver- kehrs (Literaturauswertung). ETH Zürich (in German).

[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.

[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.

[33]Yang, L., Li, J., Liu, S., 2008. Simulation of pedestrian counter-flow with right-moving preference. Phys. A, 387(13):3281-3289.

[34]Yue, H., Guan, H., Zhang, J., et al., 2010. Study on bi-direction pedestrian flow using cellular automata simulation. Phys. A, 389(3):527-539.

[35]Zhang, J., Wang, H., Li, P., 2004. Cellular automata modeling of pedestrianߣs crossing dynamics. J. Zhejiang Univ.-Sci., 5(7):835-840.

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