## 7 Conclusions  
In this study, random forest, ANN, and decision tree algorithms were applied to road accident data. The random forest model yielded the most accurate predictions, while the ANN algorithm overestimated the AL compared to the other models. In the model evaluation using the MSE and RMSE, the ANN model produced the highest values due to AL overestimation. After the random forest model was verified to be superior for prediction, variables having strong correlations with traffic accidents were analyzed and an accident forecast model was built using the random forest algorithm. The model was developed to estimate accident severity according to road surface conditions using traffic accident data. Three data sets containing road geometry data, precipitation data, and traffic accident data for the Naebu Expressway in Seoul, Korea over the course of nine years were used. The data were analyzed using the random forest algorithm as a machine learning tool because this algorithm can handle complex relationships without any assumptions and reduces the risk of over-fitting, which is the main disadvantage of tree algorithms; furthermore, it is possible to evaluate various traffic accident characteristics. In the model evaluation, the low mean error of the OOB and low values of the MSE and RMSE demonstrate the accuracy of the proposed model. From the variable importance plot, the RI, CL, and Dg are the top featured factors, which can be considered to be important parameters affecting the number of accidents based on the road surface conditions for the Naebu Expressway. This finding supports previous literature reports, where the main factor affecting traffic injury severity was determined to be the designed road geometry [60; 61] and one of the factors that was significantly related to severity was road wetness [62; 63]. In addition, it has been found that the traffic accident severity correlates with Dg and that male drivers show increased severity [64]. This work will improve the ability of decision-makers such as policy-makers and transportation safety designers to take proper actions for traffic safety control of Seoul under various weather conditions. Furthermore, this study will enable researchers adopting the random forest model for traffic accident forecasts to obtain more accurate results. Even though the accident data covered a nine-year period from 2007 to 2015 for the Naebu Expressway, the low quantity of data for particular road surface conditions did not allow an analysis of wet road conditions. In further research, larger data sets will allow estimation for the various types of road conditions, which is expected to improve the prediction performance. A limitation of the current study is that the analysis does not consider about the road construction situation, which causes the same amount of rain to have different consequences, but the assumption that the same amount of rain has the same consequences. \begin{table}
\begin{tabular}{c c} \hline \hline
**Feature Variable** & **Mean Decrease in Accuracy** \\ \hline RI (rainfall intensity) & 1.6878244076 \\ CL (curve length) & 1.1212874347 \\ Dg (driver gender) & 0.4364175560 \\ HA & 0.3302372701 \\ (horizontal alignment) & 0.330237201 \\ RC & 0.3283706054 \\ (road surface condition) & 0.2036621645 \\  RT & 0.1976457683 \\ (road geometry type) & 0.1974057994 \\ VT (vehicle type) & \(-1.2282141894\) \\ AT (accident time) & \(-2.9598124259\) \\ Se (super-elevation) & \(-3.8938194205\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Importance values (mean decrease in accuracy). **Author Contributions:** Conceptualization, [PERSON] ([PERSON]). and T.Y. ; methodology, T.Y. ; software, [PERSON] ([PERSON]); validation, S.K. ; formal analysis, [PERSON] ([PERSON]); writing--original draft preparation, [PERSON] ([PERSON]); writing--review and editing, T.Y. ; visualization, S.K. ; supervision, [PERSON] ([PERSON]), All authors have read and agreed to the published version of the manuscript.