交通安全意识对非机动车骑行者危险骑行行为的影响研究

裴玉龙; 龙钰; 马丹

doi:10.3963/j.jssn.1674-4861.2024.01.006

交通安全意识对非机动车骑行者危险骑行行为的影响研究

doi: 10.3963/j.jssn.1674-4861.2024.01.006

东北林业大学交通研究中心哈尔滨 150040

基金项目:

国家重点研发计划项目 2017YFC0803901

黑龙江省重点研发计划项目 JD22A014

详细信息

通讯作者:
裴玉龙（1961—），博士，教授. 研究方向：道路交通安全、交通规划和交通管理.E-mail：peiyulong@nefu.edu.cn

中图分类号: U491
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出版历程
- 收稿日期: 2023-09-04
- 网络出版日期: 2024-05-31

Impacts of Traffic Safety Awareness on Risky Riding Behaviors among Non-Motorized Cyclists

Transportation Research Center, Northeast Forestry University, Harbin 150040, China

摘要

摘要: 安全意识在促进安全行为方面发挥着重要作用，但由于安全意识具有多维性和复杂性，难以直接测量。为探究交通安全意识对危险骑行行为的影响，通过云模型选取安全态度、危险认知、安全素质和外界环境这4个潜变量，作为影响交通安全意识的结构要素，并基于调查问卷数据开展实证研究。运用Mplus 8.0软件构建“交通安全意识-危险骑行行为”结构方程模型，量化交通安全意识各要素作用于危险骑行行为的因果链路。采用Bootstrap法检验安全素质、危险认知和安全态度的中介作用，梳理外界环境对危险骑行行为的直接和间接关系；再利用分层回归模型，验证交通安全知识在交通安全意识与危险骑行行为间的调节效应。研究结果表明：①结构方程模型拟合良好，交通安全意识的4个要素分别与危险骑行行为呈显著的负相关，其中，危险认知对无意行为的影响最大（-0.331），安全态度对有意行为的影响最大（-0.332）；②中介效应显示外界环境作为外生变量可直接作用于行为，也可通过安全素质、危险认知和安全态度对骑行者的行为产生影响；③交通安全知识的调节作用显著（∆R² = 0.017，P＜0.05），该变量强化了交通安全意识与危险骑行行为的负向影响关系，其简单斜率关系表明，当骑行者交通安全知识水平较高时，交通安全意识对危险骑行行为的作用效果更强。
- 交通安全 /
- 安全意识 /
- 因果链路 /
- 结构方程模型 /
- 危险骑行行为
Abstract: Traffic safety awareness (TSA) of cyclists plays a crucial role in promoting safe behavior, but it is difficult to directly measure due to its multidimensionality and complexity. To investigate the impact of TSA on risky riding behavior (RRB), safety attitude, risk cognition, safety quality, and external environment are selected as the structural elements (or TSA elements) by using the cloud model, and the empirical research is carried out based on questionnaire data. The"TSA-RRB"structural equation model is developed, and the causal pathway from each TSA element to RRB is quantified by Mplus 8.0. The Bootstrap method is applied to verify the mediating roles of safety quality, risk cognition, and safety attitude, and the direct and indirect relationships between the external environment and RRB are sorted; subsequently, a hierarchical regression model is employed to validate the moderating effect of traffic safety knowledge (TSK) between TSA and RRB. The findings of this research are concluded as follows. ① the proposed structural equation model fits well with questionnaire data, and four TSA elements all have significant negative correlations with RRB. Specifically, risk cognition has the most considerable impact on unintentional behavior (-0.331), while safety attitude displays the greatest influence on intentional behavior (-0.332). ② Mediating effects show that the external environment, as an exogenous variable, could either directly act on riding behavior or indirectly affect the behavior through other TSA elements such as safety quality, risk cognition, and safety attitude. ③ The moderation effect of TSK is significant (∆R² = 0.017, P < 0.05), enhancing the negative correlations between TSA and RRB, and the simple slope relationship between TSA and RRB implies that the effect of TSA on RRB is strengthened when the level of TSK is high.
- traffic safety /
- safety awareness /
- causal pathways /
- structural equation model /
- risky riding behavior

HTML全文

图 1 权重评语云

Figure 1. Cloud of weighted rubrics

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图 2 隶属度判断云图

Figure 2. Cloud diagram of affiliation judgement

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图 3 意识-行为结构方程模型假设

Figure 3. Hypothesis of the TSA-RRB SEM

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图 4 不同分组下的箱线图

Figure 4. Box line diagram with different groupings

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图 5 交通安全意识4因子模型

Figure 5. Four factor model of traffic safety awareness

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图 6 意识-行为结构方程模型和路径系数

Figure 6. TSA-RRB structural equation model and path coefficients

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图 7 安全知识调节作用的假设

Figure 7. Hypothetical of the moderating effect of safety knowledge

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图 8 正态性检验P-P图

Figure 8. P-P plot for normality test

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图 9 交通安全知识的调节作用

Figure 9. The moderating role of traffic safety knowledge

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表 1 潜变量及观测指标变量

Table 1. Potential variables and observation indicator variables

结构要素	测量指标	符号	问卷参考
安全态度（AT）	对交通规则的态度	AT_1	文献[24]，[25]
	对交通安全的责任感	AT_2
	对危险骑行行为的态度	AT_3
危险认知（RC）	骑行使用手机的危险程度	RC_1	文献[17]，[26]
	机动车道骑行的危险程度	RC_2
	逆向骑行的危险程度	RC_3
	并排骑行的危险程度	RC_4
安全素质（SQ）	追逐骑行的危险程度	RC_5	文献[23]
	处理突发状况的能力	SQ_1
	判断周围车辆动向的能力	SQ_2
	危险源辨识	SQ_3
	骑车能力评估	SQ_4
外界环境（EE）	周围交通参与者的影响	EE_1	文献[27]
	基础设施完善程度	EE_2
	交通安全投入	EE_3
	道路交通条件	EE_4
无意行为（UB）	制度与规范	EE_5	文献[9]，[28]
	疲劳骑行	UB_1
	单手骑行	UB_2
	骑行时交谈	UB_3
有意行为（IB）	在机动车道上骑行	IB_1
	看到黄灯加速抢行	IB_2
	逆向骑行	IB_3

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表 2 样本描述性统计

Table 2. Descriptive statistics of sample

项目	类别	比例/%
性别	男性	54.4
性别	女性	45.6
学历	高中（中专）及以下	29.7
	大学本科（含大专）	51.2
	研究生	19.1
非机动车道设置	有	75.7
非机动车道设置	无	24.3
交通事故	是	12.8
交通事故	否	87.2

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表 3 验证性因素分析模型拟合指数

Table 3. Confirmatory factor analysis model fitting index

指数	CMIN/DF	RMSEA	CFI	TLI	SRMR
拟合值	1.712	0.049	0.948	0.937	0.041
参考值	≤3	≤0.08	≥0.9	0.9	≤0.08

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表 4 危险骑行行为量表效度检验结果

Table 4. Validity test results of the risky riding behavior scale

测量变量	因子载荷		AVE	CR
IB_1	0.75
IB_2	0.794		0.608	0.823
IB_3	0.794
UB_1		0.764
UB_2		0.858	0.657	0.852
UB_3		0.807

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表 5 模型检验指标结果

Table 5. Fitness statistics of the confirmatory factor analysis

指数	CMIN/DF	RMSEA	CFI	TLI	SRMR
拟合值	2.804	0.064	0.92	0.904	0.044
参考值	≤3	≤0.08	≥0.9	0.9	≤0.08

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表 6 意识-行为模型标准化路径系数

Table 6. Standardized path coefficients of the TSA-RRB structural equation model

路径	参数结果
路径	参数估计	S.E.	P - value	假设结论
IB←SQ	-0.163	0.058	0.005^**	支持
IB←RC	-0.272	0.062	< 0.001^***	支持
IB←AT	-0.332	0.063	< 0.001^***	支持
IB←EE	-0.236	0.063	< 0.001^***	支持
UB←SQ	-0.154	0.054	0.005^**	支持
UB←RC	-0.331	0.058	< 0.001^***	支持
UB←AT	-0.287	0.059	< 0.001^***	支持
UB←EE	-0.212	0.058	< 0.001^***	支持
SQ←EE	0.285	0.054	< 0.001^***	支持
RC←EE	0.417	0.048	< 0.001^***	支持
AT←EE	0.324	0.056	< 0.001^***	支持
注：^* - P < 0.05显著水平；^ - P < 0.01显著水平；^* - P < 0.001显著水平。

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表 7 中介效应分析结果

Table 7. Results of the mediation effects analysis

路径	效应量	95%置信区间
路径	效应量	Lower	Upper
路径1：IB←SQ←EE	-0.032	-0.06	-0.009
路径2：IB←RC←EE	-0.077	-0.117	-0.041
路径3：IB←AT←EE	-0.071	-0.112	-0.037
总间接效应	-0.18	-0.236	-0.126
路径4：UB←SQ←EE	-0.029	-0.058	-0.007
路径5：UB←RC←EE	-0.073	-0.118	-0.032
路径6：UB←AT←EE	-0.066	-0.107	-0.033
总间接效应	-0.168	-0.224	-0.117

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表 8 分层回归分析结果

Table 8. Hierarchical regression analysis results

变量	模型1		模型2
变量	回归系数	T - value	回归系数	T - value
截距项	2.234		2.244
交通安全意识	-0.614	-19.371^***	-0.616	-20.103^***
交通安全知识	-0.71	-27.292^***	-0.704	-27.967^***
int（交互项）			-0.3	-5.658^***
R²		0.746		0.763
∆R²				0.017^*

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