## 4 Case Study
### Analysis of Influencing Factors
The results of the KMO Bartlett test are shown in Table 2. The KMO value is 0.625 and the Bartlett sphericity test statistic is 153, with a corresponding sig of 0.000, indicating a significant difference between the correlation coefficient matrix and the unit matrix. The data set is suitable for principal component analysis, according to [PERSON]'s KMO measurement standard. Based on the results of PCA, the comprehensive score coefficient table of the filtered principal components was obtained and are shown in Table 4. The factor load matrix of the associated standardized variable \(X_{j}\) (\(j\) = 1, 2, 3,..., 18) for the principal component \(F_{i}\) (\(i\) = 1, 2, 3,..., 11) is represented by the coefficient \(\alpha_{j}\) (\(j\) = 1, 2, 3,..., 18) in the table. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**KMO Bartlett Test**} \\ Kaiser–Meyer–Olkin measure of sampling adequacy & 0.625 \\ Approximate chi square & 3285.915 \\ [PERSON]’s sphericity test & df & 153 \\ Sig. & 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The results of the KMO Bartlett test. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{3}{c}{**Composition**} & \multicolumn{1}{c}{**Characteristic Value**} & \multicolumn{1}{c}{**Variance**} & \multicolumn{1}{c}{**Cumulative**} \\  & & **Contribution Rate** & **Contribution Rate** \\ \hline
1 & 4.023 & 22.353 & 22.353 \\
2 & 2.341 & 13.007 & 35.359 \\
3 & 1.495 & 8.303 & 43.662 \\
4 & 1.296 & 7.199 & 50.862 \\
5 & 1.159 & 6.436 & 57.298 \\
6 &  
\(F_{1}\) can be interpreted as the speed index. The load proportion of the main variables \(X_{1}\), \(X_{6}\), \(X_{11}\), \(X_{17}\), and \(X_{18}\) are the largest ones, which represent the speed of the lane-changing vehicle in the preparation, execution, and adjustment stages, and the rear vehicle speed in the target lane and the final speed in the lane-changing process, as can be seen from the expression of the first principal component \(F_{1}\). It shows that the speed of vehicle changing lanes and the rear vehicle has a great impact during the process of lane changing. \(F_{2}\) can be understood as the vehicle stability index for the entire lane-changing process. In the second principal component, \(X_{2}\), \(X_{7}\), and \(X_{12}\) have the largest load proportion. The average acceleration of lane-changing vehicles during the preparation, execution, and adjustment stages is represented by these three variables. \(F_{3}\) represents the flow index of the buffer zone in the lane change preparation stage, where variables \(X_{3}\) and \(X_{4}\) account for the largest load proportion. Similarly, for duration indexes, the fourth principal component, \(F_{4}\), represents the duration index of the previous two stages during the lane-changing procedure, \(F_{8}\) represents the duration index of the last two stages, and \(F_{11}\) is the duration index of the preparation and adjustment stages. Moreover, \(F_{5}\) indicates the duration index of the adjustment stage and \(F_{7}\) represents the duration index of the execution stage. As for flow indexes, \(F_{6}\) is the dedicated lane flow index of the adjustment stage and \(F_{10}\) represents the dedicated lane flow index of the execution stage. In addition, \(F_{9}\) is the stability index of the preparation and adjustment stages.