## 5 Discussion
### Application of Transition Probabilities  
The previous studies selected the statistical indexes (e.g., mean and standard deviations, maximum, and minimum) of velocity, acceleration, and THW as input features for driving style recognition [41], which are not significant under highway driving conditions, and may often result in low classification accuracy. This paper takes the driving behavior transition probabilities as the input features to establish the driving style evaluation model. Then, the results of the proposed model are compared with the results of the classification model using statistical indexes. In order to ensure the singleness of variables, the number \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Score** \\ **Transition** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Behavi** \\ **Transition** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Aggressive** \\ **Mean** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Normal** \\ **Mean** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Cautious** \\ **Std** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Significance** \\ **Level** \\ \end{tabular} } \\ \cline{3-4} \cline{6-9}  & & **Mean** & & & & & & & \\ \hline
6–7 & MFND–MFNA & **0.6281** & 0.1917 & **0.6453** & 0.1395 & **0.2787** & 0.2346 & 0 \textless{} 0.05 \\
7–8 & NFAD–NFND & **0.7382** & 0.3201 & **0.4494** & 0.2271 & **0.0444** & 0.1721 & 0 \textless{} 0.05 \\
7–9 & NFAD–NFNA & **0.4408** & 0.2353 & **0.3369** & 0.2812 & **0.1111** & 0.2999 & 0.0016 \textless{} 0.05 \\
8–7 & NFND–NFAD & 0.2492 & 0.1625 & 0.1119 & 0.1396 & 0 & 0 & 0 \textless{} 0.05 \\
4–5 & FFND–FFNA & 0.3347 & 0.3499 & 0.5005 & 0.2803 & 0.8106 & 0.0941 & 0 \textless{} 0.05 \\
5–4 & FRNA–FFND & 0.3871 & 0.2168 & 0.5751 & 0.2287 & 0.7363 & 0.1842 & 0 \textless{} 0.05 \\
7–8 & MENA–NFND & **0.1051** & 0.0873 & **0.0575** & 0.0581 & **0.0321** & 0.0649 & 0.0021 \textless{} 0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Significance analysis of driving behavior transition probability of different driving styles. Figure 17: Transition probabilities between the behaviors involved in the selected seven features for (**a**) aggressive, (**b**) normal, and (**c**) cautious driving styles, respectively. of statistical indexes is the same as the number of transition probabilities. In total, seven statistical indexes were selected, namely, the mean and standard deviation of velocity, the mean and standard deviation of acceleration, and the mean and standard deviation of relative distance and the mean of THW. The random forest (RF), support vector machines (SVM), and K-nearest neighbor (KNN) were developed based on the selected seven transition probabilities. The radial basis function (RBF) was adopted in SVM, the neighborhood number was set as 5, and the decision tree of random forest was set as 25. The leave-one-out method was applied for cross validation. Table 5 lists the classification accuracy of these three classification algorithms under different feature inputs. Based on Table 5, the recognition accuracy for the aggressive driving style and cautious driving style was better than that for the normal driving style irrespective whether the transition probability or statistical features were used as the input features. The RF classifier had the highest classification accuracy. The classification accuracy of the classifier with transition probability as the input feature was higher than that of the classifier with traditional statistical features. The recognition rate of the RF classifier for aggressive and cautious driving styles was 91.35% and 92.22%, respectively, and the average recognition rate was higher than 90%. Since the classifiers have the same design, the differences in the values must be caused by different input characteristic parameters. Correlation analysis between the two types of features was carried out.