## 4 Analysis and Results
### Statistical Analysis of Public Questionnaire Data
Correspondence between the variables: (**a**) The correspondence between the public’s characteristics and variable A; (**b**) the correspondence between the public’s characteristics and variable B; and (**c**) the correspondence between the public’s characteristics and variable C. The education level in the figures is abbreviated (JSH and below stands for junior high school and below; SHS stands for senior high school; college and HG stands for college and undergraduate; PG and above stands for postgraduate and above). Figure 4 presents the points from the various categories. Different variables that are close to the same direction and the area of the graph may be related. In Figure 4a,b, the category points of the education level and whether holding a driving license are close to the specific scores of the variables A, B, and C. Specifically, the points of higher education level are closer to the higher scores of variables A and B. For example, \"PG and above\" is close to \"A: (4, 5]\" and \"B: (4, 5]\" and \"College and HG\" is close to \"A: (3, 4]\" and \"B: (3, 4]\". \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{2}{c}{**Dimension**} & \multicolumn{2}{c}{**Dimension**} & \multicolumn{2}{c}{**Dimension**} \\ \cline{2-9}  & **1** & **2** & **Variable** & **1** & **2** & **Variable** & **1** & **2** \\ \hline Variable A & 0.889 & 0.017 & Variable B & 0.798 & 0.230 & Variable C & 0.679 & 0.196 \\ \hline Gender & 0.003 & 0.483 & Gender & 0.007 & 0.310 & Gender & 0.011 & 0.362 \\ \hline Age & 0.015 & 0.655 & Age & 0.025 & 0.577 & Age & 0.056 & 0.576 \\ \hline Educ. Level & 0.509 & 0.186 & Educ. Level & 0.533 & 0.289 & Educ. Level & 0.787 & 0.269 \\ \hline Driving License & 0.630 & 0.030 & Driving License & 0.541 & 0.009 & Driving License & 0.120 & 0.015 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Discrimination measures of the variables. Figure 5: Discrimination measures of the variables: (**a**) Correspondence between the public’s characteristics and variable A. (**b**) Correspondence between the public’s characteristics and variable B. (**c**) Correspondence between the public’s characteristics and variable C.  
In Figure 4c, \"C: (1, 2]\" and \"JHS and below\" have a long distance. The relationships between education level and variable C are similar to the situation in Figure 4a,b. In general, there are positive correlations between the driver's education level and the three variables. Besides, the points of whether holding a driving license are close to the points of variables A and B. Specifically, \"Do not have\" is close to \"A: (2, 3]\" and \"B: (2, 3]\" and \"Have\" is close to \"A: (3, 4]\" and \"B: (3, 4]\". However, whether holding a driving license does not have an obvious relationship with the points of variable C. The results indicate that people with a driving license get higher scores in variables A and B than those without a driving license. In short, whether holding a driving license positively affects A and B. Table 7 and Figure 6 show the corresponding results between each of the three variables. In Figure 6b, the position of variable A (x = 0.752, y = 0.464) is close to that of variable B (x = 0.