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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