考虑行人心理潜变量的地铁应急疏散仿真研究*

doi:10.16265/j.cnki.issn1003-3033.2022.08.0307

摘要/Abstract

摘要：

为加深对地铁乘客疏散出口选择行为内在机制的认识,综合考虑恐慌、安全意识、从众心理等影响疏散者出口决策的心理潜变量,构建混合选择模型(HCM),对比其与传统模型疏散效率差异,并通过变更各心理潜变量权重系数,深入探究不同心理潜变量对疏散行为及疏散效率的影响。结果表明:一定程度的恐慌和从众,有助于行人选择更优的路径促进疏散,但过度恐慌及盲从将降低疏散效率;环境熟悉度和安全意识越高越有利于疏散,但随着环境熟悉度和安全意识系数值变大,其对疏散时间影响越小。考虑疏散者心理潜变量的HCM更符合现实中行人的出口选择行为,能更好地揭示真实场景下疏散者的应急疏散行为决策本质。

关键词: 地铁应急疏散, 人群疏散仿真, 心理潜变量, 混合选择模型(HCM), 出口选择行为

Abstract:

In order to deepen understanding on internal mechanism of decision makers' exit selection behavior, a HCM was constructed by comprehensively considering psychological latent variables that affected pedestrian's exit decision-making, such as panic, safety awareness, and herd mentality. Then, it was compared with traditional model in terms of evacuation efficiency, and influence of different latent variables on the decision-making by changing weight coefficients of the variables. The results show that a certain degree of panic and herd mentality can help pedestrians choose a better route to evacuate, but excessive panic and blind obedience will reduce evacuation efficiency. Moreover, the higher environmental familiarity and safety awareness, the better evacuation there will be. However, along withe the increase of their coefficients, their influence on evacuation time will be reduced. A HCM that considers pedestrians' psychological latent variables can be more consistent with pedestrian exit selection behavior in reality, and can better reveal the nature of their decision-making in real scenes.

Key words: subway emergency evacuation, crowd evacuation simulation, psychological latent variable, hybrid choice model(HCM), exit selection behavior

王立晓, 郝闵熙, 孙小慧. 考虑行人心理潜变量的地铁应急疏散仿真研究*[J]. 中国安全科学学报, 2022, 32(8): 67-75.

WANG Lixiao, HAO Minxi, SUN Xiaohui. Simulation study of subway emergency evacuation considering psychological latent variables of pedestrians[J]. China Safety Science Journal, 2022, 32(8): 67-75.

图/表 10

表1

图1

表2

图2

图3

图4

图5

图6

图7

图8

参考文献 23

[1]	王付宇, 王骏. 突发事件情景下地铁站人员应急疏散问题综述[J]. 计算机应用研究, 2018, 35(10):2888-2893.
	WANG Fuyu, WANG Jun. Summary of emergency evacuation of subway station personnel in emergency situations[J]. Application Research of Computers, 2018, 35(10):2888-2893.
[2]	LU Lili, CHAN Chingyao, WANG Jian, et al. A study of pedestrian group behaviors in crowd evacuation based on an extended floor field cellular automaton model[J]. Transportation Research Part C: Emerging Technologies, 2016, 81:317-329. doi: 10.1016/j.trc.2016.08.018
[3]	HELBING D, FARKAS I J, VICSEK T, et al. Simulating dynamical features of escape panic[J]. Nature, 2000, 407: 487-490. doi: 10.1038/35035023
[4]	赵雪, 胡玉玲. 被疏散者行为的模糊建模与仿真研究[J]. 中国安全科学学报, 2015, 25(8):56-61.
	ZHAO Xue, HU Yuling. Research on fuzzy modeling and simulation of evacuees' behavior[J]. China Safety Science Journal, 2015, 25(8):56-61.
[5]	何梦男, 付瑜玲, 陈诚, 等. 基于元胞自动机的应急疏散最短路径优化算法[J]. 中国安全科学学报, 2019, 29(4):51-57. doi: 10.16265/j.cnki.issn1003-3033.2019.04.009
	HE Mengnan, FU Yuling, CHEN Cheng, et al. Shortest path optimal algorithm for emergency evacuation based on cellular automata[J]. China Safety Science Journal, 2019, 29(4):51-57. doi: 10.16265/j.cnki.issn1003-3033.2019.04.009
[6]	魏娟, 郭阳勇, 游磊, 等. 一种基于多出口环境的人群疏散改进模型[J]. 中国安全科学学报, 2020, 30(7):139-145. doi: 10.16265/j.cnki.issn1003-3033.2020.07.021
	WEI Juan, GUO Yangyong, YOU Lei, et al. An improved crowd evacuation model for a multi-exit environment[J]. China Safety Science Journal, 2020, 30(7):139-145. doi: 10.16265/j.cnki.issn1003-3033.2020.07.021
[7]	刘杨. 基于Anylogic的地铁站应急疏散仿真研究[D]. 兰州: 兰州交通大学, 2016.
	LIU Yang. Simulation research on emergency evacuation of subway station based on Anylogic[D]. Lanzhou: Lanzhou Jiaotong University, 2016.
[8]	ZHENG Xiaoping, ZHONG Tingkuan, LIU Mengting, et al. Modeling crowd evacuation of a building based on seven methodological approaches[J]. Building and Environment, 2009, 44(3): 437-445. doi: 10.1016/j.buildenv.2008.04.002
[9]	罗凌燕, 沈斐敏, 刘毅, 等. 地铁站突发事件疏散路径选择影响因素研究[J]. 中国安全科学学报, 2016, 26(7):169-174.
	LUO Lingyan, SHEN Feimin, LIU Yi, et al. Research on factors influencing selection of evacuation route in an emergency in subway station[J]. China Safety Science Journal, 2016, 26(7):169-174.
[10]	HAGHANI M, SARVI M. Heterogeneity of decision strategy in collective escape of human crowds: on identifying the optimum composition[J]. International journal of disaster risk reduction, 2019, 35,DOI: 10.1016/j.ijdrr.2019.101064. doi: 10.1016/j.ijdrr.2019.101064
[11]	ANTONINI G, BIERLAIRE M, WEBER M. Discrete choice models of pedestrian walking behavior[J]. Transportation Research Part B, 2006, 40(8):667-687. doi: 10.1016/j.trb.2005.09.006
[12]	HAGHANI M, SARVI M. Crowd behaviour and motion: empirical methods[J]. Transportation Research Part B Methodological, 2017, 107: 253-294. doi: 10.1016/j.trb.2017.06.017
[13]	LOVREGLIO R, FONZONE A, DELLOLIO L, et al. A mixed logit model for predicting exit choice during building evacuations[J]. Transportation Research Part A-policy and Practice, 2016, 92: 59-75. doi: 10.1016/j.tra.2016.06.018
[14]	WANG Lei, ZHENG Jiehui, ZHANG Xiaoshuang, et al. Pedestrians' behavior in emergency evacuation: modeling and simulation[J]. Chinese Physics B, 2016, 25(11):689-698.
[15]	SHIELDS T J, PROULX G. The science of human behaviour: past research endeavours, current developments and fashioning a research agenda[J]. Fire Safety Science, 2000, 6: DOI: 10.3801/IAFSS.FSS.6-95. doi: 10.3801/IAFSS.FSS.6-95
[16]	BEN-AKIVA M E, MCFADDEN D, TRAIN K W, et al. Hybrid choice models: progress and challenges[J]. Marketing Letters, 2002, 13(3):163-175. doi: 10.1023/A:1020254301302
[17]	JOHANSSON M V, HELDT T, JOHANSSON P. The effects of attitudes and personality traits on mode choice[J]. Transportation Research Part A: Policy and Practice, 2006, 40(6):507-525. doi: 10.1016/j.tra.2005.09.001
[18]	PAULSSEN M, TEMME D, VIJ A, et al. Values, attitudes and travel behavior: a hierarchical latent variable mixed logit model of travel mode choice[J]. Transportation, 2014, 41(4):873-888. doi: 10.1007/s11116-013-9504-3
[19]	景鹏, 隽志才, 查奇芬. 考虑心理潜变量的出行方式选择行为模型[J]. 中国公路学报, 2014, 27(11):84-92,108.
	JING Peng, JUAN Zhicai, ZHA Qifen. Incorporating psychological latent variables into travel mode choice model[J]. China Journal of Highway and Transport, 2014, 27 (11): 84-92,108.
[20]	PRATO C G, BEKHOR S, PRONELLO C. Latent variables and route choice behavior[J]. Transportation, 2012, 39:299-319. doi: 10.1007/s11116-011-9344-y
[21]	DE LUCA S, PACE R D, BRUNO F, et al. Accounting for attitudes and perceptions influencing users' willingness to purchase electric vehicles through a Hybrid Choice Modeling approach based on analytic hierarchy process[J]. Transportation Research Procedia, 2020, 45: 467-474. doi: 10.1016/j.trpro.2020.03.040
[22]	付兴胜. 考虑泊位共享的居住区停车选择行为研究[D]. 哈尔滨: 哈尔滨工业大学, 2018.
	FU Xingsheng. Study on parking choice behavior of residential areas considering shared parking[D]. Harbin: Harbin Institute of Technology, 2018.
[23]	HAGHANI M, EJTEMAI O, SARVI M, et al. Random utility models of pedestrian crowd exit selection based on SP-off-RP experiments[J]. Transportation Research Procedia, 2014, 2:524-532. doi: 10.1016/j.trpro.2014.09.070

影响因素		变量命名	备注
疏散出口属性	路径长度	DIST	1:最短;2:较短;3:较长; 4:最长
	拥挤程度	CONG	1:不拥挤;2:一般拥挤; 3:非常拥挤
	疏散时间	TIME	1:较短;2:一般;3:较长
社会经济属性	性别	SEX	0:男;1:女
	年龄/ 岁	AGE	1:18~25;2:26~40; 3:41~60;4:60以上
	职业	OCC	1:事业单位人员;2:企业单位人员;3:公务员4:教师; 5:学生;6:其他
	交通工具	CMT	1:地铁;2:公交;3:私家车; 4:出租车;5:步行;6:自行车; 7:单位用车
	教育水平	EDU	1:高中(含中专)及以下; 2:大专及本科;3:硕士及以上
	家庭年收入/万元	INC	1:0~8;2:8~15; 3:15~25;4:25以上
	乘坐地铁频率/ (次·周^-1)	FRE	1:少于1;2:1~2; 3:3~5;4:7
	疏散演习次数	NOD	1:0;2:1;3:2~3; 4:3以上
心理潜变量	恐慌	PAN	采用Likert五点量表,每个属性由多个问题进行测量
	信息依赖度	ID
	环境熟悉度	EF
	安全意识	SF
	服从性	SA
	从众心理	GP

模型参数		似然比检验
		MNL模型			HCM模型
		最短路径	较短路径	较长路径	最短路径	较短路径	较长路径
p_i	DIST	2.356^***	1.465^***	1.296^**	7.282^***	3.088^***	3.937^***
	CONG	-1.015^***	-0.799^***	0.063	-1.033^***	-0.809^***	0.057
	TIME	-0.721^***	-0.120^***	-0.305^***	-0.620^***	-0.097^**	-0.268^***
q_i	SEX₁	0.168	0.160	0.125	-0.016	0.075	0.043
	AEG₁	0.292	0.168	0.143	0.257	0.160	0.158
	AEG₂	0.400^**	0.253^*	0.228	0.289	0.212	0.184
	AEG₃	0.455^**	0.361^**	0.218	0.399^**	0.357^**	0.189
	OCC₁	-0.801^***	-0.977^***	-0.512^***	-0.789^***	-1.000^***	-0.491^*
	OCC₂	-0.791^***	-0.305^**	-0.088	-0.681^***	-0.270^*	-0.036
	OCC₃	-0.875^***	-0.083	0.181	-0.790^**	0.028	0.257
	OCC₄	-1.076^***	-0.268	-0.161	-0.977^**	-0.222	-0.101
	OCC₅	0.638^*	-0.207	0.283	0.622^*	-0.206	0.246
	CMT₁	-0.456	-0.241	-0.607^*	-0.999^**	-0.440	-0.858^**
	CMT₂	-0.478	0.342	-0.324	-0.828^**	0.209	-0.403
	CMT₃	-0.234	0.047	-0.611^**	-0.519	-0.074	-0.717^**
	CMT₄	0.487	0.132	-0.232	0.099	-0.029	-0.441
	CMT₅	-0.090	0.211	-0.504	-0.755	-0.103	-0.697
	CMT₆	0.164	0.357	0.016	-0.481	0.187	-0.187
	EDU	0.161	0.117	-0.019	0.110	0.078	-0.033
	INC	0.367^***	0.216^**	0.143^*	0.302^***	0.203^**	0.151^*
	FRE	0.518^***	0.278^***	0.136^*	0.587^***	0.285^***	0.171^**
	NOD	0.030	-0.033	-0.050	0.069	-0.018	-0.043
γ_i	PAN	—	—	—	-0.432^***	-0.211^***	-0.261^***
	ID	—	—	—	0.055	0.094	0.059
	EF	—	—	—	0.026	0.045	-0.036^**
	SF	—	—	—	-0.299^***	-0.053	-0.170^**
	SA	—	—	—	-0.011	0.019	0.076
	GP	—	—	—	-0.022^**	-0.097	-0.046^*
样本量		3 285			3 285
拟合优度R²		0.091			0.105