## 4 Discussion  
While there has been limited research on driving evaluation for two-wheeled vehicles, this study employs the AHP technique to comprehensively assess various driving behaviors of two-wheeled vehicles and derive a quantitative driving evaluation index. The AHP analysis of the survey responses indicated that in terms of categories, traffic violations, pedestrian threats, and reckless driving were ranked in order of importance. Among the 11 evaluation items, helmet violation, speed violation, and pedestrian close driving were ranked highly. The dangerous driving index was calculated considering the individual weights and the number of violations for each evaluation item. Additionally, for the reckless driving category, the risk level was also considered.  
Although the respective weights for the evaluation items were calculated, during the index calculation process, there was a limitation that it was unable to collect trajectory data of the two-wheeled vehicles for all items detected. Also, in this study, the developed index was tested using the trajectory data of 12 drivers, but it is considered that a larger amount of trajectory data from multiple years will be required for more accurate testing.  
Future studies could improve on these limitations, such as collecting accumulated trajectory data over several years or using a two-wheeled vehicle driving simulator to generate data that diversifies the degree of risk in driving and violates all 11 items. It is expected that the dangerous driving index can be further developed by adjusting the weights of each item and improving the index calculation method through further experiments. Also, methods to assess the developed index could be studied to increase the credibility and acceptability of the index.  
Moreover, it will also be possible to develop an integrated system for evaluating and managing two-wheeled vehicle driving using these indicators. It can be also expected that this system could be utilized for usage-based insurance (UBI) or government safety management policies, and finally, contribute to the prevention of traffic accidents.  
Conceptualization, S.L.; methodology, S.L. and D.K.; software, S.L. and D.K.; validation, S.L. and D.K.; formal analysis, S.L.; resources, S.L. and D.K.; writing--original draft preparation, S.L.; writing--review and editing, S.L.; visualization, S.L.; project administration, C.J.; funding acquisition, C.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by grant number (2022-MOIS41-005) of the Citizen-customized Life Safety Technology Development Program funded by the Ministry of the Interior and Safety (MOIS, Korea).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Driver No.** & \begin{tabular}{c} **Travel Distance** \\ **(km)** \\ \end{tabular} & \begin{tabular}{c} **Travel Time** \\ **(h)** \\ \end{tabular} & \
\begin{tabular}{c} **Average Speed** \\ **(km/h)** \\ \end{tabular} &
\begin{tabular}{c} **Final Dangerous** \\ **Driving Index** \\ \end{tabular} \\ \hline
1 & 744.371 & 37.109 & 19.809 & 16.921 \\
2 & 377.354 & 20.134 & 19.148 & 13.845 \\
3 & 1058.966 & 48.713 & 22.031 & 13.639 \\
4 & 488.593 & 25.897 & 19.268 & 19.435 \\
5 & 453.244 & 30.227 & 16.332 & 22.086 \\
6 & 1818.832 & 88.230 & 21.757 & 14.158 \\
7 & 1221.171 & 68.477 & 17.194 & 17.738 \\
8 & 901.395 & 52.112 & 16.678 & 11.654 \\
9 & 527.955 & 31.477 & 16.415 & 22.110 \\
10 & 610.077 & 32.538 & 17.721 & 17.816 \\
11 & 1082.630 & 62.249 & 16.987 & 20.511 \\
12 & 529.615 & 28.570 & 17.668 & 19.611 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Final dangerous driving index by two-wheeled vehicle driver.