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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

裴玉龙, 龙钰, 马丹. 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[J]. 交通信息与安全, 2024, 42(1): 49-58. doi: 10.3963/j.jssn.1674-4861.2024.01.006
引用本文: 裴玉龙, 龙钰, 马丹. 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[J]. 交通信息与安全, 2024, 42(1): 49-58. doi: 10.3963/j.jssn.1674-4861.2024.01.006
PEI Yulong, LONG Yu, MA Dan. Impacts of Traffic Safety Awareness on Risky Riding Behaviors among Non-Motorized Cyclists[J]. Journal of Transport Information and Safety, 2024, 42(1): 49-58. doi: 10.3963/j.jssn.1674-4861.2024.01.006
Citation: PEI Yulong, LONG Yu, MA Dan. Impacts of Traffic Safety Awareness on Risky Riding Behaviors among Non-Motorized Cyclists[J]. Journal of Transport Information and Safety, 2024, 42(1): 49-58. 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
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

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

  • 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。

     

  • 图  1  权重评语云

    Figure  1.  Cloud of weighted rubrics

    图  2  隶属度判断云图

    Figure  2.  Cloud diagram of affiliation judgement

    图  3  意识-行为结构方程模型假设

    Figure  3.  Hypothesis of the TSA-RRB SEM

    图  4  不同分组下的箱线图

    Figure  4.  Box line diagram with different groupings

    图  5  交通安全意识4因子模型

    Figure  5.  Four factor model of traffic safety awareness

    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

    图  7  安全知识调节作用的假设

    Figure  7.  Hypothetical of the moderating effect of safety knowledge

    图  8  正态性检验P-P图

    Figure  8.  P-P plot for normality test

    图  9  交通安全知识的调节作用

    Figure  9.  The moderating role of traffic safety knowledge

    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
    下载: 导出CSV

    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
    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 75.7
    24.3
    交通事故 12.8
    87.2
    下载: 导出CSV

    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
    下载: 导出CSV

    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
    下载: 导出CSV

    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
    下载: 导出CSV

    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
    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。
    下载: 导出CSV

    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
    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
    下载: 导出CSV

    表  8  分层回归分析结果

    Table  8.   Hierarchical regression analysis results

    变量 模型1 模型2
    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*
    下载: 导出CSV
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  • 收稿日期:  2023-09-04
  • 网络出版日期:  2024-05-31

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