## 4 Analysis and Results
### Statistical Analysis of Public Questionnaire Data
There is a significant difference in terms of education level and the three variables, i.e., attitudes toward red-light running behavior of e-bikers (\(\chi^{2}(3)\) = 114.730, \(p\) < 0.001), application effect of FRT (\(\chi^{2}(3)\) = 103.534, \(p\) < 0.001), whether FRT violates privacy (\(\chi^{2}(3)\) = 90.292, \(p\) < 0.001). Then, we use Multiple Correspondence Analysis (MCA) to study the correspondence between the public's characteristics and the three variables. In order to meet MCA's data requirements, the scope of variables (A, B, and C), values, and the classification values are shown in Table 5. Figure 4 is the joint plot of the category points. Table 6 and Figure 5 present the discrimination measures of the variables. The MCA transforms all variables of the original data through the optimal scale transformation to obtain two dimensions (Dimension 1 and Dimension 2). \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Variable** & **Item** & **M** & **S.D.** & **Variable Average** \\ \hline \multirow{6}{*}{B: Application effect of FRT} & B1: FRT can significantly reduce the red-light running behavior of e-bikers. & 3.516 & 0.852 \\ \cline{2-4}  & B2: The application of FRT helps to strengthen personal traffic safety awareness. & 3.012 & 0.766 \\ \cline{2-4}  & B3: Need to take specific measures to punish the identified behavior. & 3.287 & 0.848 \\ \cline{2-4}  & B4: The information of violators shown on the screen has a deterrent effect on the public. & 3.360 & 0.798 \\ \cline{2-4}  & B5: Most people will support and actively obey the application of FRT in traffic management. & 3.264 & 0.856 \\ \cline{2-4}  & B6: FRT is progress of technology and is worth promoting. & 3.229 & 0.836 \\ \hline \multirow{6}{*}{C: Whether FRT violates privacy protected and will not be leaked.} & C1: It is not a violation of personal privacy to show red-light running behavior on the screen. & 3.376 & 0.947 \\ \cline{2-4}  & C2: The following information published on the screen is appropriate: the offender’s image running a red light (the face is covered), the middle part of the name is concealed, and the ID number is concealed in the middle digits. & 3.384 & 0.853 \\ \cline{2-4}  & C3: Personal information identified by FRT will be strictly protected and will not be leaked. & 3.349 & 0.901 \\ \cline{2-4}  & C4: The application of FRT also needs to be regulated by improving relevant laws. & 3.357 & 0.907 \\ \cline{2-4}  & C5: When using FRT, the public’s right to know needs to be guaranteed. & 3.019 & 0.996 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._From Figures 4 and 5, and Table 6, we could observe the correspondence between the public's characteristics and the three variables. In Figure 5 and Table 6, the public's education level (x = 0.509, y = 0.186) and whether holding a driving license (x = 0.630, y = 0.030) are related to the value of variable A, and the two characteristics also possess greater explanatory power to variable B (x = 0.533, y = 0.289; x = 0.541, y = 0.009). However, variable C only related to the public's education level (x = 0.787, y = 0.269). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Scope of Variable Value** & **Classification Values** \\ \hline \multirow{4}{*}{A, B, C} & (0, 1) & 1 \\ \cline{2-3}  & (1, 2) & 2 \\ \cline{2-3}  & (2, 3) & 3 \\ \cline{2-3}  & (3, 4) & 4 \\ \cline{2-3}  & (4, 5) & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The scores of variables (A, B, and C) and the values after classification. Figure 4: Joint plot of the category points.