A Study of the Effectiveness of Epidemic Prevention Policies on Public Transit Usage Based on the Theory of Planned Behaviors
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摘要: 疫策略对居民公交出行决策行为和出行偏好有着关键作用, 直接关系到公交优先策略的长期实施效果。从居民出行行为的角度, 在计划行为理论的框架下, 基于调查数据研究了居民公交出行影响因素及作用路径, 在此基础上对公共交通防疫策略进行了分析。研究发现了疫情期间公交出行行为影响因素中的1条显著作用路径, 即“风险感知、防疫策略→出行态度→出行意向→出行行为”, 验证了疫情风险感知和各项防疫管控策略对居民出行方式选择行为和出行偏好上有着深刻的长期影响, 需要更加慎重的使用停运等严格策略。通过进一步地观测变量分析, 驾驶员和车内环境消杀等信息对相应潜变量的路径系数最高, 均达到0.9以上, 说明已实施的防疫策略的信息公开对乘客出行态度极其重要, 这在当前是普遍被忽略的。在分析结果的基础上提出了信息公开、分散就坐等具体策略建议。Abstract: Epidemic prevention strategies play a key role in the decision behavior and travel preference. Thus they are related to the long-term effects of the"public transport priority"strategy. From the perspective of residents' travel behaviors, the influencing factors and travel paths on transit-trip behaviors are studied using the questionnaire and the theory of planned behaviors. Furthermore, epidemic prevention strategies of public transit are analyzed. A significant path of the influencing factors on transit-trip behaviors is found, namely"risk perception and epidemic prevention strategies → travel attitude → travel intention → travel behaviors". Thus, the perceived risk and prevention strategies of the epidemic have profound and long-term effects on choosing traffic modes as well as travel preference. Therefore, strict strategies, such as the shutdown approach, should be used more carefully. Further, by analyzing the observation variables, the path coefficients to the corresponding latent variables of the driver's information and the disinfection of the internal environment are maximum, more than 0.9. These indicate that releasing the implemented epidemic prevention strategies is vital to residents' traveling attitudes. However, it is generally ignored at present. Finally, based on the analysis results, some specific strategic suggestions are put forward, such as information disclosure and decentralized sitting.
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图 2 居民公交出行结构方程全模型示意图
Figure 2. Sketch map of the structural equation model for transit-trip
表 1 调查数据结果
Table 1. Contents and data of the questionnaire
单位: % 变量名称 观测变量 变量编号 “非常不同意”结果占比 “不同意”结果占比 “不确定”结果占比 “同意”结果占比 “非常同意”结果占比 风险感知 病毒对生命安全带来的威胁很严重 RC1 4.14 3.91 11.95 48.97 31.03 病毒对日常工作生活影响很严重 RC2 4.14 1.61 7.36 50.8 36.09 病毒对经济上带来的损失很严重 RC3 3.91 1.84 12.18 48.05 34.02 病毒对心理上的负担很严重 RC4 3.68 2.53 11.26 47.82 34.71 公交防疫策略 与同车乘客间距不会影响出行意向 S1 10.8 35.4 32.41 13.79 7.59 是否知道公交工作人员防疫培训、宣传情况不会影响出行意向 S2 11.26 39.31 27.59 14.71 7.13 乘车环境的消杀通风的程度不会影响出行意向 S3 10.11 37.93 27.59 16.55 7.82 是否知道司机以及乘客身体状况和防护程度不会影响出行意向 S4 10.34 39.54 27.82 14.48 7.82 出行态度 车厢其他乘客不会将病毒传染给我 AT1 39.77 40.46 14.25 1.61 3.91 公交司机不会将病毒传染给我 AT2 35.86 40.23 16.78 2.76 4.37 乘车环境的消毒情况不必忧虑 AT3 36.78 43.45 12.87 2.76 4.14 主观规范 家人、朋友、同事希望我乘公交出行 SN1 12.64 41.84 17.93 14.71 12.87 电视、网络新闻等媒体倡导公交出行 SN2 13.56 43.22 20.69 11.03 11.49 当地政府倡导公交出行 SN3 17.93 40.46 21.15 8.97 11.49 知觉行为控制 乘坐公交完全取决于自己的意志 PBC1 7.82 46.44 18.62 13.1 14.02 若对防护的必要知识了解，愿意出行 PBC2 10.11 36.78 25.98 20.0 7.13 若应对疫情有丰富的经验，愿意出行 PBC3 9.2 44.37 23.45 14.25 8.74 出行意向 经常选择公交出行 BI1 54.48 21.84 10.80 4.83 8.46 优先选择公交出行 BI2 24.37 44.60 18.62 8.28 4.14 鼓励身边人公交出行 BI3 18.62 28.05 28.05 20.46 4.83 出行行为 工作日通勤，主要选择公交出行 B1 68.26 4.60 5.52 19.78 1.84 休息日出行，主要选择公交出行 B2 72.01 13.28 5.29 6.44 2.99 公交出行频率会因疫情发生改变 B3 34.25 49.66 7.82 5.98 1.61 
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表 2 信度和效度分析结果
Table 2. Results of the reliability and validity analysis
潜变量 测量题项 信度系数 整体信度系数 KMO抽样适合性 巴特利特球形度检验 近似卡方 自由度 显著性 出行态度 AT1、AT2、AT3 0.952 0.773 1 320.679 3 < 0.001 主观规范 SN1、SN2、SN3 0.904 0.810 0.701 1 241.255 3 < 0.001 知觉行为控制 PBC1、PBC2、PBC3 0.726 0.619 469.981 3 < 0.001 风险感知 RC1、RC2、RC3、RC4 0.950 0.866 1 771.110 6 < 0.001 防疫策略 S1、S2、S3、S4 0.956 0.872 1 920.338 6 < 0.001 出行意向 BI1、BI2、BI3 0.778 0.693 349.885 3 < 0.001 出行行为 B1、B2、B3 0.850 0.721 606.559 3 < 0.001
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表 3 参数估计值及显著性水平
Table 3. Parameter estimates and significance level of the model
路径关系 Estimate S.E. C.R. P 是否通过检验 风险感知←防疫策略 -0.590 0.49 -12.099 *** 是 出行态度←风险感知 -0.538 0.037 -14.606 *** 是 出行态度←防疫策略 0.370 0.037 9.895 *** 是 出行意向←出行态度 0.474 0.043 11.048 *** 是 出行意向←主观规范 0.109 0.018 5.937 *** 是 出行意向←知觉行为控制 0.097 0.021 4.511 *** 是 出行意向←防疫策略 0.375 0.031 12.086 *** 是 出行意向←风险感知 -0.015 0.031 -0.478 0.616 否 出行行为←出行意向 0.405 0.051 7.907 *** 是 出行行为←知觉行为控制 0.309 0.043 7.167 *** 是 RC1 ←风险感知 0.982 0.032 28.109 *** 是 RC2 ←风险感知 1.010 0.030 33.389 *** 是 RC3 ←风险感知 0.998 0.032 31.004 *** 是 RC4 ←风险感知 1.000 - - - 是 AT1 ←出行态度 1.000 - - - 是 AT2 ←出行态度 1.075 0.029 37.529 *** 是 AT3 ←出行态度 1.007 0.034 31.726 *** 是 S1 ←防疫策略 0.995 0.025 40.200 *** 是 S2 ←防疫策略 0.959 0.028 33.938 *** 是 S3 ←防疫策略 0.982 0.029 34.157 *** 是 S4 ←防疫策略 1.000 - - - 是 SN1 ←主观规范 0.845 0.042 19.962 *** 是 SN2 ←主观规范 1.073 0.034 31.726 *** 是 SN3 ←主观规范 1.000 - - - 是 PBC1 ←知觉行为控制 0.734 0.064 8.166 *** 是 PBC2 ←知觉行为控制 1.045 0.072 14.446 *** 是 PBC3 ←知觉行为控制 1.000 - - - 是 BI1 ←出行意向 1.000 - - - 是 BI2 ←出行意向 0.981 0.063 15.624 *** 是 BI3 ←出行意向 0.973 0.062 15.614 *** 是 B1 ←出行行为 1.000 - - - 是 B2 ←出行行为 0.901 0.059 15.360 *** 是 B3 ←出行行为 1.106 0.064 17.159 *** 是
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表 4 模型适配度检验结果
Table 4. Results of the fitness test for the model
指标 指标描述 适配指标 检验输出值 是否适配 CMIN/DF 卡方自由度，比值越大，适配度越差 1~3 2.614 适配 GFI 适配度指数，值越大，适配度越好 > 0.9 0.903 适配 NFI 规准适配指数，越接近1，适配程度越佳 > 0.9 0.940 适配 RFI 相对适配指数，越接近1，适配程度越佳 > 0.9 0.930 适配 CFI 比较适配指数，越接近1，适配程度越佳 > 0.9 0.962 适配 RMSEA 渐进残差均方和平方根，值越小，适配程度越好 < 0.08 0.061 适配
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