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Journal of ZheJiang University (Engineering Science)  2020, Vol. 54 Issue (6): 1185-1193    DOI: 10.3785/j.issn.1008-973X.2020.06.016
Traf fic Engineering     
Evacuation behaviour modelling and simulation of pedestrian counter flow considering influence of visual field
Da-zhi HE(),Xiao-ke LI,Ming-ming LI
School of Civil Engineering and Communication, North China of Water Resource and Electric Power, Zhengzhou 450045, China
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Abstract  

Pedestrian visual radius data was obtained through experiments. A decision algorithm for pedestrian micro behaviors, including turning, following, decelerating, stopping, and normal movement, was established based on collision detection results at different sight radiuses. An overlap elimination algorithm was designed for the problem of personnel overlap caused by spatial continuity. A pedestrian counter flow simulation experiment was designed. The simulation process show that the model can achieve the automatic channelization well; while, the problem of overlapping pedestrian is also eliminated. In the case of high pedestrian flow density, the numbers of pedestrian lanes formed by the simulation and actual experiment are basically the same, and the evacuation durations are close. In the case of low pedestrian flow density, the number of pedestrian lanes formed by the simulation experiment is consistent with the calculation results. The fitting curve of pedestrian flow-density data in the simulation experiment agrees well with the fitting curve of the survey data.



Key wordspedestrian evacuation      spatial continuity model      Agent-based modeling      visual field      behavior decision-making      lane formation     
Received: 21 June 2019      Published: 06 July 2020
CLC:  U 121  
Cite this article:

Da-zhi HE,Xiao-ke LI,Ming-ming LI. Evacuation behaviour modelling and simulation of pedestrian counter flow considering influence of visual field. Journal of ZheJiang University (Engineering Science), 2020, 54(6): 1185-1193.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2020.06.016     OR     http://www.zjujournals.com/eng/Y2020/V54/I6/1185


考虑视域影响的疏散行为建模及双向行人流仿真

通过实验获取行人视域半径数据,根据不同视域半径下的碰撞检测结果,建立包括转向、跟随、减速、停止和正常移动等行人微观行为的决策算法. 针对空间连续性导致的人员重叠的问题,设计消除行人位置重叠的算法. 设计通道双向行人流仿真实验,仿真过程显示,模型能较好地实现自动渠化现象,并消除行人位置重叠的问题. 在行人流密度较高的情况下,对比实际实验与仿真实验的自动渠化过程,发现两者形成的行人通道行数基本一致,疏散时长接近. 在行人流密度较低的情况下,仿真实验得到的行人通道行数与计算结果一致. 仿真实验的行人流量?密度数据拟合曲线与调研数据拟合曲线吻合度较高.


关键词: 行人疏散,  空间连续模型,  Agent建模,  视域,  行为决策,  渠化现象 
Fig.1 Framework of evacuation behavior Agent modelling method
Fig.2 Horizontal visual field of pedestrian
Fig.3 Pedestrian steering distance measurement in counter flow experiment
序号 l / m 实验组别 序号 l / m 实验组别
1 3.6 1 10 1.2 3
2 1.2 1 11 1.2 3
3 1.5 1 12 1.5 3
4 1.5 1 13 2.4 3
5 1.8 2 14 0.9 3
6 1.5 2 15 0.9 4
7 1.5 2 16 1.2 4
8 0.9 2 17 0.9 4
9 6.0 2 18 1.2 4
Tab.1 Experiment data of pedestrian steering distance
Fig.4 Illustration of detecting blocker、
Fig.5 Influence of locker information on pedestrian’s decision
Fig.6 Diagram for condition with pedestrian’s decision of keeping original direction
Fig.7 Diagram for condition with pedestrian’s decision of steering and avoiding
Fig.8 Restrictions of opposite pedestrian flow on steering and avoiding decision
Fig.9 Diagram for condition with pedestrian’s decision of following
Fig.10 Influence of changing visual field radius on behavior decision
Fig.11 Experiment and simulation of lane formation in pedestrian counter flow
Fig.12 Channel number of pedestrian counter flow in walkway with width of 5 m
Fig.13 Comparison of pedestrian flow density-flow curve between simulation data and survey data
Fig.14 Residual plot of simulation data fitting curve
[1]   BURSTEDDE C, KLAUCK K, SCHADSCHNEIDER A, et al Simulation of pedestrian dynamics using a two-dimensional cellular automation[J]. Physica A: Statistical Mechanics and its Applications, 2001, 295 (3/4): 507- 525
[2]   XUEMEI Z, JINGJIE H, XIONGZIYAN X Cellular automaton simulation of pedestrian flow considering vision and multi-velocity[J]. Physica A: Statistical Mechanics and its Applications, 2019, 514: 982- 992
doi: 10.1016/j.physa.2018.09.041
[3]   ALIZADEH R A dynamic cellular automaton model for evacuation process with obstacles[J]. Safety Science, 2011, 49 (2): 315- 323
doi: 10.1016/j.ssci.2010.09.006
[4]   THOMPSON P A, MARCHANT E W Simulex: developing new computer modelling techniques for evaluation[J]. Fire Safety Science, 1994, 4: 613- 624
doi: 10.3801/IAFSS.FSS.4-613
[5]   TAO Y Z, DONG L Y A cellular automaton model for pedestrian counterflow with swapping[J]. Physica A: Statistical Mechanics and its Applications, 2017, 475: 155- 168
doi: 10.1016/j.physa.2017.02.008
[6]   宋卫国, 于彦飞, 范维澄, 等 一种考虑摩擦与排斥的人员疏散元胞自动机模型[J]. 中国科学E辑: 工程科学 材料科学, 2005, 35 (7): 725- 736
SONG Wei-Guo, YU Yan-Fei, FAN Wei-Cheng A cellular automaton model for personnel evacuation considering friction and repulsion[J]. Science in China Ser. E: Engineering and Materials Science, 2005, 35 (7): 725- 736
[7]   杨立中, 方伟峰, 黄锐, 等 基于元胞自动机的火灾中人员逃生的模型[J]. 科学通报, 2002, 47 (12): 896- 901
YANG Li-Zhong, FANG Wei-Feng, HUANG Rui Model of personnel escape in fire based on cellular automata[J]. Chinese Science Bulletin, 2002, 47 (12): 896- 901
doi: 10.3321/j.issn:0023-074X.2002.12.003
[8]   岳昊, 邵春福, 关宏志, 等 基于元胞自动机的行人视线受影响的疏散流仿真研究[J]. 物理学报, 2010, 59 (7): 4499- 4507
YUE Hao, SHAO Chun-fu, GUAN Hong-zhi, et al Simulation of pedestrian evacuation flow with affected visual field using cellular automata[J]. Acta Physica Sinica, 2010, 59 (7): 4499- 4507
doi: 10.7498/aps.59.4499
[9]   DIRK HELBING, ILLéS FARKAS, TAMáS VICSEK Simulating dynamical features of escape panic[J]. Nature, 2000, 407 (6803): 487- 490
doi: 10.1038/35035023
[10]   HELBING D, MOLNAR P Social force model for pedestrian dynamics[J]. Physical Review E, 1995, 51 (5): 4282- 4286
doi: 10.1103/PhysRevE.51.4282
[11]   LAKOBA T I, KAUP D J, FINKELSTEIN N M Modifications of the Helbing Molnár Farkas Vicsek social force model for pedestrian evolution[J]. Simulation, 2005, 81 (5): 339- 352
doi: 10.1177/0037549705052772
[12]   JIAN L, LIZHONG Y, DAOLIANG Z Simulation of bi-direction pedestrian movement in corridor[J]. Physica A: Statistical Mechanics and its Applications, 2005, 354: 619- 628
doi: 10.1016/j.physa.2005.03.007
[13]   WENG W G, CHEN T, YUAN H Y, et al Cellular automaton simulation of pedestrian counter flow with different walk velocities[J]. Physical Review E, 2006, 74 (3): 036102
doi: 10.1103/PhysRevE.74.036102
[14]   YU Y F, SONG W G Cellular automaton simulation of pedestrian counter flow considering the surrounding environment[J]. Physical Review E, 2007, 75: 046112
doi: 10.1103/PhysRevE.75.046112
[15]   MA J, SONG W G, ZHANG J, et al K-Nearest-Neighbor interaction induced self-organized pedestrian counter flow[J]. Physica A: Statistical Mechanics and its Applications, 2010, 389 (10): 2101- 2117
doi: 10.1016/j.physa.2010.01.014
[16]   CAO S, SONG W, LV W, et al A multi-grid model for pedestrian evacuation in a room without visibility[J]. Physica A: Statistical Mechanics and its Applications, 2015, 436: 45- 61
doi: 10.1016/j.physa.2015.05.019
[17]   XUE S, JIA B, JIANG R, et al Pedestrian evacuation in view and hearing limited condition: the impact of communication and memory[J]. Physics Letters A, 2016, 380 (38): 3029- 3035
doi: 10.1016/j.physleta.2016.07.030
[18]   LI X L, GUO F, KUANG H, et al An extended cost potential field cellular automaton model for pedestrian evacuation considering the restriction of visual field[J]. Physica A: Statistical Mechanics and its Applications, 2019, 515: 47- 56
doi: 10.1016/j.physa.2018.09.145
[19]   WANG P, CAO S C Simulation of pedestrian evacuation strategies under limited visibility[J]. Physics Letters A, 2019, 383 (9): 825- 832
doi: 10.1016/j.physleta.2018.12.017
[20]   GUO N, HAO Q Y, JIANG R, et al Uni- and bi-directional pedestrian flow in the view-limited condition: experiments and modeling[J]. Transportation Research Part C: Emerging Technologies, 2016, 71: 63- 85
doi: 10.1016/j.trc.2016.07.001
[21]   GEORGEFF M, PELL B, POLLACK M, et al. The belief-desire-intention model of agency [M] // Intelligent Agents V: Agents Theories, Architectures, and Languages. Berlin Heidelberg: Springer, 1998.
[22]   MIHAELAALEXANDRA PUIC?, FLOREA A Emotional belief-desire-intention agent model: previous work and proposed architecture[J]. International Journal of Advanced Research in Artificial Intelligence, 2013, 2 (2): 1- 8
[23]   YUAN Z, JIA H, LIAO M, et al Simulation model of self-organizing pedestrian movement considering following behavior[J]. Frontiers of Information Technology and Electronic Engineering, 2017, 18 (8): 1142- 1150
doi: 10.1631/FITEE.1601592
[24]   国家技术监督局. 中国成年人人体尺寸: GB/T10000-1988 [S]. 北京: 中国标准出版社, 1989: 7-1.
[25]   DIRK HELBING, PéTER MOLNáR, ILLéS J FARKAS, et al Self-organizing pedestrian movement[J]. Environment and Planning B: Planning and Design, 2001, 28 (3): 361- 383
doi: 10.1068/b2697
[26]   王子甲, 陈峰, 施仲恒 基于Agent的社会力模型实现及地铁通道行人仿真[J]. 华南理工大学学报: 自然科学版, 2013, 41 (4): 90- 95
WANG Zi-jia, CHEN Feng, SHI Zhong-hen Agent based realization of social force model and simulation of pedestrians in subway passageway[J]. Journal of South China University of Technology: Natural Science Edition, 2013, 41 (4): 90- 95
[27]   叶建红, 陈小鸿 行人交通流三参数基本关系式适用性研究[J]. 西南交通大学学报, 2016, 51 (1): 138- 144
YE Jian-hong, CHEN Xiao-hong Applicability analysis of triparametric foundational equations for pedestrian traffic flow[J]. Journal of Southwest Jiaotong University, 2016, 51 (1): 138- 144
doi: 10.3969/j.issn.0258-2724.2016.01.020
[28]   李俊梅, 胡成, 李炎锋 不同类型疏散通道人群密度对行走速度的影响研究[J]. 建筑科学, 2014, 30 (8): 122- 129
LI Jun-mei, HU Cheng, LI Yan-feng Influence of crow density on the movement speed on different egress paths[J]. Building Science, 2014, 30 (8): 122- 129
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