## 5 Results  
### Driving-Style Classification  
Based on the four derivative driving features, this study used the K-means cluster method to classify the three driving types--aggressive, normal, and cautious--and define the thresholds among them.  
The cluster steps are as follows:  
1. Select mean acceleration (\(|\overline{a}|_{mean}\)), standard deviation of acceleration (\(|\overline{a}|_{std}\)), mean jerk (\(|\overline{f}|_{mean}\)), and standard deviation of jerk (\(|J|_{std}\)) as the components of clustering factor data.
2. Determine the number of clusters to be 3; that is, define \(k=3\). Each step of the clustering analysis redefines the center of the class.
3. Run K-means clustering analysis function in STATA. Output clustering results.  
STATA classified the data into three categories: cautious, normal, and aggressive. After sorting and processing the data, the parameter ranges of the three types of driving behavior are shown in Table 4.  
The data in each time window includes four variables, and the driving style of each time window is defined according to the clustering results. The output results show there were 15,544 time windows seen as being of a cautious driving style, 3666 time windows defined as being of a normal driving style, and 193 time windows defined as being of aggressive driving style. Due to the use of four variables, traditional two-dimensional images could not be used for the cluster display. Thus, the cluster method can be used to detect the thresholds between a normal driving state and the two abnormal driving states. As shown in Table 4, the result of \(K\)-means clustering defines the thresholds of mean acceleration and jerk of a driving style. The other two parameters are used to make certain that acceleration and jerk changes are in line with the characteristics of the driving style within the current time window.  
By determining the threshold, it is possible to divide the acceleration and jerk when a heavy truck passed through the intersection and accurately identify the abnormal state. Figure 8 shows the data from 5 intersections as an example. The passing time of each intersection is from 16 s to 40 s, and the average passing time is 29.6 s. The average passing velocity is 24.32 km/h, which meets the operating conditions of heavy trucks in the actual environment.  
As seen in Figure 8, driving styles at intersection 1-5 are \"Normal\", \"Normal\", \"Normal\", \"Cautious\", and \"Normal\", respectively. Jerk in Figure 9 proves the accuracy of classification. Within the thresholds, the area is divided into three categories, and all driving types can be classified within the area.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable Name** & **Cautious** & **Normal** & **Aggressive** \\ \hline \(|\overline{a}|_{mean}\) (m/s\({}^{2}\)) & [0, 0.36) & [0.36, 0.99] & (0.99, 3.92) \\ \(|\overline{a}|_{std}\) (m/s\({}^{2}\)) & [0, 0.10) & [0.10, 0.41] & (0.41, \(\infty\)) \\ \(|\overline{f}|_{mean}\) & [0, 0.005) & [0.005, 0.019] & (0.019, \(\infty\)) \\ \(|J|_{std}\) & [0, 0.04) & [0.04, 0.14] & (0.14, \(\infty\)) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of \(K\)-means clustering.  
were determined as aggressive, the driving style of the driver on this intersection was viewed as aggressive. The results are shown in Table 5.