基于结构方程模型的地铁突发事件下弱势群体帮扶意愿影响分析

黄利华; 陈霄彤; 吕淑然; 刘旭腾; 陈康; 王晨硕

doi:10.3963/j.jssn.1674-4861.2024.03.016

基于结构方程模型的地铁突发事件下弱势群体帮扶意愿影响分析

doi: 10.3963/j.jssn.1674-4861.2024.03.016

首都经济贸易大学管理工程学院北京 100079

基金项目:

国家自然科学基金面上项目 51474151

北京市教委科技一般项目 KM202410038001

详细信息

作者简介:
黄利华(1990—)，博士，讲师. 研究方向：交通安全. E-mail: hpuhuanglihua@163.com

通讯作者:
陈霄彤（2000—），博士. 研究方向：交通安全. E-mail: xiaotongchen2000@126.com

中图分类号: U492.1
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出版历程
- 收稿日期: 2023-12-29
- 网络出版日期: 2024-10-21

An Impact Analysis of Willingness to Help Vulnerable Groups under Subway Emergencies Based on SEM Model

School of Management Engineering, Capital University of Economics and Trade, Beijing 100079, China

摘要

摘要: 针对地铁突发事件下弱势群体帮扶意愿影响机理不清楚等问题，研究开展问卷调查精细化分析了实际地铁应急疏散状态下乘客疏散条件、自身状况、帮扶对象等多因素对帮扶意愿的影响及各因素间的结构关系。研究发现：地铁应急疏散过程中，影响疏散效率最高的为老年群体（62.92%），其次为幼小群体（25.83%）；而受应急疏散负面影响最大的则为幼小群体（51.25%），其次是老年群体（39.58%）。借助方差分析发现地铁乘客应急疏散过程中的帮扶意愿受到自身状况、疏散条件及帮扶对象的显著性影响（p < 0.05）。利用信效度检验对帮扶意愿影响因素进行筛减优化，搭建结构方程模型（structural equation modeling，SEM）精准获取弱势群体应急疏散帮扶行为关键影响因子，量化各因素间的关联关系。结果显示不利的疏散条件对帮扶行为有直接负面影响（－0.162），帮扶对象自身状况对帮扶行为有直接正面影响（0.151），不利疏散条件对帮扶对象自身状况有直接正面影响（0.652）。结果表明，乘客的疏散条件不利时将降低其帮扶意愿；帮扶对象情况不乐观时将提高乘客的帮扶意愿；帮扶行为有奖励时，能提高乘客帮扶意愿；同时，即使乘客处于不利的疏散条件，当遇到情况不乐观的帮扶对象时，仍有较高的帮扶意愿。
- 轨道交通 /
- 地铁弱势群体 /
- 帮扶意愿 /
- 结构方程模型 /
- 应急疏散
Abstract: To address the issue of unclear mechanisms influencing the willingness to help vulnerable groups under subway emergencies, a questionnaire survey is conducted. It aims to refine the analysis of the impact of multiple factors, such as passenger evacuation conditions, their own status, and the target of assistance, on the willingness to assist during actual subway emergency evacuations, as well as the structural relationships among these factors. The study finds that elderly group are the most likely to hinder evacuation efficiency (62.92%), followed by young children (25.83%). Among these vulnerable groups, young children are most negatively affected by emergency evacuation (51.25%), followed by elderly group (39.58%). Through variance analysis, it reveals that passengers'willingness to help during evacuation of subway emergencies is significantly influenced by their own conditions, evacuation conditions, and the characteristics of those in need of assistance (p < 0.05). To refine and optimize the influencing factors of assistance willingness, the study conducts reliability and validity tests, and constructs the Structural Equation Modeling (SEM) is used to accurately identify the key influencing factors of assistance behaviors towards vulnerable groups under subway emergencies, while clarifying the quantitative relationships among these factors. The results indicate that unfavorable evacuation conditions have a direct negative impact on assistance behaviors (－0.162), whereas the conditions of those in need have a direct positive influence (0.151). Additionally, unfavorable evacuation conditions have a direct positive impact on the conditions of those in need (0.652). These findings suggest that poor evacuation conditions reduce passengers'willingness to help while unfavorable conditions of those in need increase it. The promise of rewards for assistance behaviors also enhances passengers'willingness to help. Notably, even under unfavorable evacuation conditions, passengers still exhibit a high willingness to assist when encountering those in dire need.
- rail transit /
- vulnerable groups in the subway /
- assistance willingness /
- SEM model /
- emergency evacuation

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图 1 国内地铁事故类型统计

Figure 1. Statistics of metro accident types in china

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图 2 受负面影响较大群体选择比例

Figure 2. Proportion of groups that have greater negative impact

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图 3 最容易影响疏散效率群体选择比例

Figure 3. Proportion of groups most likely to affect evacuation efficiency

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图 4 老年群体影响效率原因选择比例

Figure 4. Proportion of the causes of elderly groups affecting efficiency

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图 5 幼小群体影响效率原因选择比例

Figure 5. Proportion of the causes of young children and primary school student groups affecting efficiency

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图 6 不同自身状况下乘客的帮扶态度

Figure 6. Passengers' assistance attitude under different personal conditions

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图 7 不同疏散因素下乘客的帮扶态度

Figure 7. Passengers' assistance attitude under different evacuation factors

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图 8 帮扶对象不同状态时乘客的帮扶态度

Figure 8. Passengers' assistance attitude towards different states of the assisted objects

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图 9 不同激励策略下帮扶意愿

Figure 9. Assistance willingness under different incentive strategies

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图 10 地铁应急疏散帮扶行为SEM模型

Figure 10. The SEM model of subway emergency evacuation helping behavior

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表 1 问卷设计内容

Table 1. Questionnaire content

4个部分内容	子内容	题目数量
基本信息		6
群体对应急疏散的影响		4
	自身状况	9
帮扶选择态度	疏散条件	8
	帮扶对象	9
帮扶激励策略		1

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表 2 基本信息汇总

Table 2. Summary of basic information

题目	选项	人数	占比/%
年龄/岁	≤18	12	5
	＞18~39	214	89.17
	＞39~59	11	4.58
	＞59~75	2	0.83
	> 75	1	0.42
性别	男	86	36
性别	女	154	64
文化程度	小学	4	2
	初中	4	2
	高中	24	10
	专科	8	3
	本科	158	66
	硕士及以上	42	17
职业	政府部门及事业单位	22	9
	自主经营者	2	1
	学生	192	80
	企业	16	7
	工人	4	1
	农民	0	0
	其他	4	2
1周乘坐地铁次数	不乘坐地铁	12	5.00
	很少乘坐地铁	37	15.42
	1~2	119	49.58
	3~6	55	22.92
	每天乘坐1~2	16	6.67
	每天乘坐3次以上	1	0.42
是否经历过人员密集场所	是	28	12
拥挤踩踏突发事件	否	212	88

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表 3 自身状况差异显著性

Table 3. The difference of self-condition is significant

4因素		F	显著性p
自身状况	恐慌因素	309.31	0.00
	出行目的	56.58	0.00
	逃生时间	228.39	0.00
	健康状况	343.35	0.00
注：p＜0.05表示结果存在显著性差异。

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表 4 疏散条件的差异显著性

Table 4. The difference of evacuation conditions is significant

4因素		F	显著性p
疏散条件	是否结伴	69.97	0.00
	人群密集	253.23	0.00
	工作人员存在与否	10.54	0.00
	路径是否拥堵	154.99	0.00

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表 5 帮扶对象的差异显著性

Table 5. The difference of assisted objects is significant

4因素		F	显著性p
帮扶对象	对象群体类型	89.86	0.00
	对象是否求助	158.81	0.00
	对象是否危急	170.60	0.00
	是否答谢	4.10	0.04

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表 6 KMO和巴特利特检验结果

Table 6. Rrsult of KMO and Bartlett test

KMO取样适切性量数	巴特利特球形度检验
KMO取样适切性量数	近似卡方	自由度	显著性
0.844	19 417.582	5 460	0.000

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表 7 问卷信效度检验

Table 7. Questionnaire reliability and validity test

潜变量	观察题项	观察题项量表	观察变量	标准负荷	α	CR	AVE
不利疏散条件	当您自己1个人时您选择	A.不帮助 B.可能不帮助 C.可能帮助 D.帮助	自己1个人	0.553	0.890	0.844	0.585
	当您身边人群过于密集时您选择		人群过于密集	0.911
	当附近没有工作人员疏导时您选择		没有工作人员疏导	0.640
	当您的疏散路径非常拥堵时您选择		疏散路径非常拥堵	0.892
帮扶对象自身状况	当乘客是老人时您选择		乘客是老人	0.796	0.915	0.915	0.730
	当乘客是儿童时您选择		乘客是儿童	0.872
	当该乘客求助时您选择		乘客求助	0.870
	当该乘客情况危急时您选择		乘客危急机时	0.876
帮扶行为	当给予物质奖励时您选择		物质奖励	0.827	0.751	0.836	0.631
	当给予金钱奖励时时您选择		金钱奖励	0.805
	当给予荣誉奖励时时您选择		荣誉奖励	0.748

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表 8 结构方程模型的适配度指标值

Table 8. Fitness index values of structural equation models

适配指标	推荐值	拟合值
Χ²	越小越好	206.514
Χ² /df	< 3.0	2.037
GFI	> 0.9	0.957
AGFI	> 0.8	0.810
RMSEA	< 0.08	0.07
NFI	> 0.9	0.926
IFI	> 0.9	0.918
CFI	> 0.9	0.957

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表 9 结构方程模型分析结果

Table 9. Results of structural equation model analysis

假设	标准化路径系数	平均误差	载荷系数	显著性p
不利疏散条件→帮扶行为	－0.162	0.044	－3.641	0.000
帮扶对象自身状况→帮扶行为	0.151	0.047	3.234	0.001
不利疏散条件帮扶→对象自身状况	0.652	0.065	10.005	0.000

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参考文献(25)

[1]	姚诗忆. 基于模糊贝叶斯网络的综合交通枢纽地铁车站安全风险评价研究[D]. 广州: 华南理工大学, 2022. YAO S Y. Research on safety risk assessment of metro station in integrated transportation hub based on fuzzy Bayesian network[D]. Guangzhou: South China University of Technology, 2022. (in Chinese)
[2]	惠阳, 余强, 王永岗, 等. 地铁站应急疏散效率影响因素分析及优化策略[J]. 长安大学学报(自然科学版), 2024, 44(4): 130-138. HUI Y, YU Q, WANG Y G, et al. Analysis of factors affecting emergency evacuation efficiency in subway stations and optimization strategies[J]. Journal of Chang'an University (Natural Science Edition), 2024, 44(4): 130-138. (in Chinese)
[3]	宗妍, 于露, 牛田元, 等. 高密度建筑火灾下人员多出口应急疏散仿真[J]. 计算机仿真, 2024, 41(5): 543-547. doi: 10.3969/j.issn.1006-9348.2024.05.099 ZONG Y, YU L, NIU T Y, et al. Simulation of emergency evacuation of personnel with multiple exits under high-density building fires[J]. Computer Simulation, 2024, 41(5): 543-547. (in Chinese) doi: 10.3969/j.issn.1006-9348.2024.05.099
[4]	张洪杰, 王慧敏, 聂俊杰, 等. 地铁乘客疏散帮助行为及其影响因素的SEM模型[J]. 中国安全生产科学技术, 2023, 19(4): 147-153. ZHANG H J, WANG H M, NIE J J, et al. Structural equation model(SEM)of passengers helping behavior and its influencing factors in metro evacuation[J]. Journal of Safety Science and Technology, 2023, 19(4): 147-153. (in Chinese)
[5]	王勇, 张锦彬, 朱军等. 基于改进社会力模型的地铁站火灾应急疏散VR模拟方法[J]. 中国安全生产科学技术, 2023, 19(5): 194-201. WANG R, ZHANG J L, ZHU J, et al. VR simulation method for emergency evacuation in subway station fire based on improved social force model[J]. Journal of Safety Science and Technology, 2023, 19(5): 194-201. (in Chinese)
[6]	朱涛, 胡建华, 陆愈实等. 基于Pathfinder的地铁站应急疏散仿真及优化[J]. 地下空间与工程学报, 2023, 19 (S2): 987-996. ZHU T, HU J H, LU Y S, et al. Subway station emergency evacuation simulation and optimization based on pathfinder[J]. Chinese Journal of Underground Space and Engineering, 2023, 19(S2): 987-996. (in Chinese)
[7]	李兴华, 王天佐, 张晓光, 等. 综合客运枢纽疏散流线设计优化方法[J]. 交通信息与安全, 2023, 41(1): 132-139. doi: 10.3963/j.jssn.1674-4861.2023.01.014 LI X H, WANG T Y, ZHANG X G, et al. A method for optimizing the design of evacuation streamline for multimodal passenger transportation hubs[J]. Journal of Transport Information and Safety, 2023, 41(1): 132-139. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2023.01.014
[8]	刘颖. 群体性恐慌心理对地铁乘客不安全疏散行为的影响研究[D]. 西安: 西安科技大学, 2020. LIU Y. Study on the influence of group panic psychology on the unsafe evacuation behavior of subway passengers[D]. Xi' an : Xi'an university of science and technology, 2020. (in Chinese)
[9]	王立晓, 于江波, 孙小慧. 考虑心理异质性的地铁应急疏散行为决策建模[J]. 中国安全科学学报, 2021, 31(10): 119-126. WANG L X, YU J B, SUN X H. Decision-making modeling of metro emergency evacuation behavior considering psychological heterogeneity[J]. China Safety Science Journal, 2021, 31(10): 119-126. (in Chinese)
[10]	陈志洲, 李倩. 基于乘客心理的城市轨道交通大客流应急疏散策略研究[J]. 交通企业管理, 2022, 37(2): 91-93. doi: 10.3963/j.issn.1006-8864.2022.02.030 CHEN Z Z, LI Q. Study on emergency evacuation strategy of mass passenger flow in urban rail transit based on passenger psychology[J]. Transportation Enterprise Management, 2022, 37(2): 91-93. (in Chinese) doi: 10.3963/j.issn.1006-8864.2022.02.030
[11]	温莎莎. 基于小学生心理行为特性的教学楼应急疏散仿真与策略优化研究[D]. 太原: 太原科技大学, 2022. WEN S S. Simulation and strategy optimization of emergency evacuation in teaching building based on pupils' psychological behavior characteristics. [D]. Taiyuan: Taiyuan University of Science and Technology, 2022. (in Chinese)
[12]	谢启苗, 王焘, 王维莉. 考虑恐慌心理的人员疏散模型[J]. 中国安全科学学报, 2022, 32(7): 180-187. XIE Q M, WANG T, WANG W L. An evacuation model considering panic psychology[J]. China Safety Science Journal, 2022, 32(7): 180-187. (in Chinese)
[13]	郑勋. 城市轨道交通车站乘客微观行为分析与建模[D]. 北京: 北京交通大学, 2020. ZHENG X. Microscopic behavior analysis and modeling of passenger in urban rail transit station[D]. Beijing: Beijing Jiaotong University, 2020. (in Chinese)
[14]	黄利华, 王亚飞, 马英楠, 等. 地铁站突发事件下乘客疏散行为认知特性研究[J]. 武汉理工大学学报(信息与管理工程版), 2021, 43(2): 99-105. doi: 10.3963/j.issn.2095-3852.2021.02.001 HUANG L H, WANG Y F, MA Y N, et al. Research on the cognitive characteristics of passengers' evacuation behavior in subway station emergency[J]. Journal of Wuhan University of Technology (Information & Management Engineering), 2021, 43(2): 99-105. (in Chinese) doi: 10.3963/j.issn.2095-3852.2021.02.001
[15]	HAGHANI M. Empirical methods in pedestrian, crowd and evacuation dynamics: Part Ⅰ. Experimental methods and emerging topics[J]. Safety Science, 2020, 129: 104743. doi: 10.1016/j.ssci.2020.104743
[16]	钟茂华, 田向亮, 刘畅, 等. 基于结构方程模型的地铁乘客安全行为影响因素分析[J]. 中国安全生产科学技术, 2018, 14(1): 5-11. ZHONG M H, TIAN X L, LIU C, et al. Analysis on influence factors of safety behavior for metro passengers based on structural equation model[J]. Journal of Safety Science and Technology, 2018, 14(1): 5-11. (in Chinese)
[17]	SINGH S, SABERI M. Experimental study to compare factors influencing exit choice behaviour in emergency evacuation situations using virtual reality techniques[J]. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020(6): 115-122.
[18]	霍非舟, 刘昶, 马亚萍, 等. 考虑主动避让行为的养老场所人员疏散研究[J]. 中国安全科学学报, 2022, 32(1): 142-148. HUO F Z, LIU C, MA Y P, et al. Study on evacuation in elderly care places considering active avoidance behavior[J]. China Safety Science Journal, 2022, 32(1): 142-148. (in Chinese)
[19]	GAIRE N. A study on human evacuation behavior involving individuals with disabilities in a building[M]. Logan: Utah State University, 2017.
[20]	REN X X, HU Y H, LI H L, et al. Simulation of building evacuation with different ratios of the elderly considering the influence of obstacle position[J]. Physica A: Statistical Mechanics and its Applications, 2022, 604: 127833. doi: 10.1016/j.physa.2022.127833
[21]	黄丽蒂, 罗开洲, 刘莹, 等. 老年公寓火灾场景人群疏散模拟[J]. 中国安全科学学报, 2020, 30(3): 137-142. HUANG L D, LUO K Z, LIU Y, et al. Simulation study on fire evacuation of senior apartment[J]. China Safety Science Journal, 2020, 30(3): 137-142. (in Chinese)
[22]	KIM D, JEONG J, PARK S, et al. A study on the application of optimal evacuation route through evacuation simulation system in case of fire[J]. Journal of the Society of Disaster Information, 2020, 16(1): 96-110.
[23]	高国平. 建筑内人员疏散的行为特征与疏散环境研究[D]. 武汉: 武汉理工大学, 2018. GAO G P. Study on behavior characteristics and evacuationEnvironment of evacuation in buildings[D]. Wuhan: Wuhan University of Technology, 2018. (in Chinese)
[24]	方廷勇, 韩婷婷. 人员疏散中小群体的帮扶行为实验研究[J]. 安徽建筑大学学报, 2015, 23(3): 115-120. FANG T Y, HAN T T. Experimental study on helping behavior of subgroup in personnel evacuation[J]. Jouranal of Anhui Jianzhu University, 2015, 23(3): 115-120. (in Chinese)
[25]	曾坤媛, 广晓平. 基于结构方程模型地铁突发事件下乘客风险性疏散行为研究[J]. 综合运输, 2023, 45(8): 81-85. ZENG K Y, GUANG X P. Research on risky evacuation behavior of passengers under subway emergencies based on structural equation model[J]. China Transportation Review, 2023, 45(8): 81-85. (in Chinese)