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CLC number: TP391.9; U698.2

On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 2017-08-19

Cited: 0

Clicked: 19470

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Zhilu Yuan

http://orcid.org/0000-0002-7431-6599

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

http://doi.org/10.1631/FITEE.1601592


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.

@article{title="Simulation model of self-organizing pedestrian movement considering following behavior",
author="Zhilu Yuan, Hongfei Jia, Mingjun Liao, Linfeng Zhang, Yixiong Feng, Guangdong Tian",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="18",
number="8",
pages="1142-1150",
year="2017",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1601592"
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%0 Journal Article
%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
%V 18
%N 8
%P 1142-1150
%@ 2095-9184
%D 2017
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1601592

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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
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SP - 1142
EP - 1150
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Y1 - 2017
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1601592


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.

考虑跟随行为的行人自组织运动仿真模型

概要:在本文中一种新的力学模型被引入到社会力模型中,用来仿真相向行人流中的跟随行为。这种跟随行为指的是行人通过接近同向行人以避免与反向行人冲突的行为。新的力学模型类似于一种引力模型,在建模过程中考虑了行人的视野范围、自身的运动状态、被跟随行人的运动状态等因素。我们利用新的力学模型对相向行人流进行了仿真,研究了跟随行为对渠化现象、行人间冲突以及双向通道通行效率的影响。仿真结果表明:跟随行为能促进渠化现象形成,并能起到缓解相向行人流拥堵的作用;跟随行为具有降低相向行人流冲突次数的作用,这种作用在入口流量较低时并不明显,但随着行人流量的升高而增强。跟随行为能够提高双向通道的通行效率,并且跟随行为的强度参数越大通道的通行效率越高。

关键词:引力模型;相向行人流;社会力模型;渠化现象;自组织行为

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

Reference

[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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