## 3 Case Analysis and Discussion
### Cluster Results Verification  
In the previous section, the threshold result for each risk indicator was determined, which was then used to test the final clustering results. The worse the risk measurement of driving behavior is, the higher the percentage of the risk indicators for drivers' various driving behaviors exceeding the threshold should be. The integration results of the data are shown in Tables 7 and 8. Figure 8: Clustering results of the driving behavior risk measurement. Figure 9: MOR clustering for a single driver. Tables 7 and 8 show that the average value of each coefficient is higher when the risk measurement result of the driving behavior is worse. For example, the average value of the lateral displacement change indicator for the dangerous type was 2.526, whereas that for the conservative type was only 0.540. The maximum value of the longitudinal stability indicator for the dangerous type was 6.053, whereas that for the conservative type was only 1.752. At the same time, for the dangerous and the aggressive driving behaviors, the percentage of each coefficient exceeding the threshold was much higher than that for the safe type and the conservative type. The coefficients for the dangerous type and radical type were also different, which showed that the safety of a dangerous driving behavior is poorer than that of an aggressive driving behavior. For the safe and conservative driving behaviors, some of the values of the risk measurement indicators also exceeded the threshold, but the proportion in excess was very small. By comparing the thresholds of each coefficient with the clustering results, the test results showed that clustering the driving behavior evaluation results into dangerous, aggressive, safe, and conservative is correct and feasible to distinguish the driving behaviors of drivers. In order to further validate the effectiveness of the selected indices, an experimental comparison was conducted on the driving behavior risk measurement models composed of different indicators. Utilizing the CRITIC weighting method, three new driving behavior risk measurement models were established, with the composition of each model indicator shown in Table 9.  
In Table 9, one or two indicators were removed from the driving behavior risk measurement model, and the weight values of the indicators were recalculated. These three kinds of driving behavior risk measurement models were used to calculate the driving behavior risk measurement values, and clustering was completed through the K-means clustering algorithm. We selected 20 vehicles among the 600 sample vehicles for the test. By playing back the video, three experts in the field of traffic safety evaluated the risk of the vehicle's driving behaviors at each moment. In the end, the category of driving behavior risk at each second for each vehicle was determined through discussion among the three experts. A total of 1207 driving behavior risk measurement results evaluated by the experts were collected in the experiment. The comparison between the expert evaluation results and the model measurement results is shown in Table 10. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Cluster**} & \multicolumn{4}{c}{\(R_{1}\)} & \multicolumn{2}{c}{\(R_{2}\)} \\ \cline{2-7}  & **Mean** & **Range** & **Percent Over Threshold** & **Mean** & **Range** & **Percent Over Threshold** \\ \hline Dangerous & 2.526 & [0.355, 6.245] & 46.10\% & 2.152 & [0.000, 6.053] & 51.12\% \\ \hline Aggressive & 1.669 & [0.039, 3.603] & 3.65\% & 1.470 & [0.000, 3.638] & 13.58\% \\ \hline Safe & 1.060 & [0.005, 0.245] & 0\% & 1.189 & [0.000, 2.588] & 0.35\% \\ \hline Conservative & 0.540 & [0.004, 1.624] & 0\% & 0.817 & [0.000, 1.752] & 0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Cluster results of the driving stability indicator.