## 5 Conclusions  
Until now, many traditional statistical methods have been used for accident prediction and analytical model design. Although these techniques were easy to interpret and showed performance above a certain level, there were limitations that their performance could be decreased in exploring high-dimension feature space and big-data analysis. In order to overcome these shortcomings, this paper attempted to analyze data using not only traditional statistical methods but also machine learning methods. In addition, interpretable machine learning methods were used to interpret the results in more detail so that they can be expressed in quantitative values. In particular, SHAP finds risk factors in the dataset and numerically expresses the degree of accident occurrence possibility by calculating the contribution of variables to the model. Therefore, it is expected that the results can help the road transport department and engineers in the road design policy decision-making process. Furthermore, it could be helpful in establishing public policies such as driver safety management and driver education programs.  
Although this article developed traffic accident modeling to alleviate dilemmas in modeling and associated safety measures, there are some limitations that need to be addressed in future studies. First, there were limitations within the data. In the case of explanatory variables, it is expected that analysis from more diverse aspects would have been possible if there were variables that could represent more diverse road conditions. Also, if the dependent variable was a multiple classification variable according to the severity of the accident rather than a binary classification, a more detailed explanation would be possible depending on the severity of each accident [54]. Therefore, for future work, add the driver's status or the vehicle's status (e.g., vehicle speed) as data [52,56], or include various external variables such as the weather conditions ([PERSON] and [PERSON], 2021) [5]might make the research more abundantly. Second, various methods such as resampling and hyperparameter optimization were used to improve the model's performance, but the development model in this paper did not show overwhelming high performance. So, trying to use a machine learning model (e.g., LightGBM) that has not been used in this analysis or adding a neural network model to data analysis will greatly help future research [20; 57].  
Conceptualization, J.L. and S.K.; methodology, J.L., T.-Y.H. and D.L.; formal analysis, J.L., S.K. and T.-Y.H.; investigation, S.K. and D.L.; resources, S.K.; Data curation, J.L.; writing--original draft, T.-Y.H. and D.L.; writing--review and editing, J.L., S.K., T.-Y.H. and D.L.; visualization, T.-Y.H.; supervision, J.L.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Institute of Environmental Research (NIER) and the Ministry of Environment (MOE) (grant number NIER-2018-01-01-072).  
Anyone with minimal experience in statistical modeling should be able to use the ML models used in this study easily by using this Python 3.10 or most other libraries available in Python and in other computer languages (e.g., R). The Python codes developed for this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.