## 6 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Correlation** & \begin{tabular}{c} **Ve\_mean** \\ \end{tabular} & \begin{tabular}{c} **Ve\_std** \\ \end{tabular} & \begin{tabular}{c} **De\_mean** \\ \end{tabular} & \begin{tabular}{c} **De\_std** \\ \end{tabular} & \begin{tabular}{c} **Ae\_mean** \\ \end{tabular} & \begin{tabular}{c} **Ae\_std** \\ \end{tabular} & \begin{tabular}{c} **THW\_mean** \\ \end{tabular} \\ \hline \begin{tabular}{c} Ve\_mean \\ \end{tabular} & \(1\) & \(0.6025\) & \(-0.5068\) & \(-0.3547\) & \(0.1605\) & \(0.3657\) & \(-0.6764\) \\ \hline \begin{tabular}{c} Ve\_std \\ \end{tabular} & \(0.6025\) & \(1\) & \(-0.2731\) & \(-0.2084\) & \(0.0464\) & \(0.4818\) & \(-0.3277\) \\ \hline \begin{tabular}{c} **De\_mean** \\ \end{tabular} & \(-0.5068\) & \(-0.2731\) & \(1\) & \(0.8666\) & \(-0.0749\) & \(-0.4843\) & \(\mathbf{0.9674}\) \\ \hline \begin{tabular}{c} **De\_std** \\ \end{tabular} & \(-0.3547\) & \(-0.2084\) & \(0.8666\) & \(1\) & \(0.1078\) & \(-0.4450\) & \(\mathbf{0.8225}\) \\ \hline \begin{tabular}{c} Ae\_mean \\ \end{tabular} & \(0.1605\) & \(0.0464\) & \(-0.0749\) & \(0.1078\) & \(1\) & \(-0.0480\) & \(-0.1429\) \\ \hline \
\begin{tabular}{c} Ae\_std \\ \end{tabular} & \(0.3657\) & \(0.4818\) & \(-0.4843\) & \(-0.4450\) & \(-0.0480\) & \(1\) & \(-0.4115\) \\ \hline
\begin{tabular}{c} THW\_mean \\ \end{tabular} & \(-0.6764\) & \(-0.3277\) & \(\mathbf{0.9674}\) & \(\mathbf{0.8225}\) & \(-0.1429\) & \(-0.4115\) & \(1\) \\ \hline \hline \end{tabular} Note: Significant at the \(0.01\) level.  
\end{table} 
Table 7: Pearson correlation coefficients between the seven traditional statistical features. 3. Additionally, the action behavior of aggressive acceleration (AA) and aggressive deceleration (AD) is lower under highway conditions, which is consistent with the actual situation. 4. Transition probability can reveal the internal relationship among driving behaviors. The joint mutual information maximization (JMIM) algorithm can select an optimized subset effectively by combining feature correlation and redundancy concepts. The seven highest ranking features were selected to evaluate driving styles: (1) MFND-MFNA, (2) NFAD-NFND, (3) NFND-NFNA. (4) NFND-NFAD, (5) FFND-FFNA, (6) MFND-FFNA, and (7) FFNA-FFND. The results showed that the transition probability as classification features can provide rich information about driving style and improve the classification accuracy of the driving style. These efforts extend previous studies that focus in more detail on identifying driving style among categories and provide a novel methodology quantifying personal risk indices and visualizing driving performance simply. The limitation of this study is that the driving data were collected from a simulator platform, which results in the observables being idealistic compared with a real vehicle testing platform. Only 33 drivers were selected, and the sampling framework was biased (23 males and 10 females, average age: 26.21 \(\pm\) 5.06 years). The drivers were too young to represent the characteristics of drivers of other ages. Additionally, only the car-following scenario was designed, and the influence of the traffic flow on driving style was rarely considered. In future work, data collected under more complex natural conditions will be considered, and the proposed approach will be further investigated and verified by naturalistic driving data. X.Q. : Methodology, Investigation, Validation, Formal analysis, Resources, Writing--original draft. L.Z. : Conceptualization, Methodology, Writing--review & editing. Y.L. : Conceptualization, Methodology, Writing--review & editing. Y.R. : Data collection, Software, Validation, Formal analysis. Z.Z. ([PERSON]): Validation, Formal analysis. Z.Z. ([PERSON]): Data collection, Formal analysis. L.Q. : Review & Formal analysis. All authors have read and agreed to the published version of the manuscript.