# Weight Feedback-Based Harmonic MDG-Ensemble Model for Prediction of Traffic Accident Severity
## 5 Conclusions  
This study proposed a weight feedback-based harmonic MDG-ensemble model for the prediction of traffic accident severity. The proposed method consists of traffic accident data pre-processing, variable selection according to the MDG coefficient, and the construction of a weight feedback-based harmonic MDG-ensemble model. In the pre-processing stage, unique categories and redundant meanings were removed and integrated, and outliers and missing values were processed among traffic accident data of the Road Traffic Authority. In the variable selection step using the MDG coefficient, the MDG for the variable was extracted and the importance was compared to extract the variable. Accordingly, the efficiency of analysis was improved by removing unnecessary data for predicting the severity of traffic accidents. The weighted feedback-based Harmonic MDG-ensemble model was constructed through three classifiers that can be used for multi-category classification. Accordingly, various characteristics of data were considered and the over-fitting problem was solved. The weight update uses the harmonized average to consider the average rate of change. Through this, errors in the prediction of the constructed model were reduced. For performance evaluation, the proposed ensemble algorithm was compared with single classification algorithms in terms of accuracy and F1-score. Additionally, accuracy was compared according to the presence or absence of MDG-based variable selection. The suitability of the harmonic mean was evaluated and the proposed model was compared with other models in terms of performance. As a result, the proposed method demonstrated superior performance. Therefore, with the proposed method, it is possible reliably, objectively, and rapidly to determine traffic accident severity. In addition, the proposed data-based weight update method applies to the establishment of an ensemble model for use in other domains, as well as traffic data. The method proposed in this study was constructed through three single models to consider the characteristics of the data. In addition, important variables were extracted to predict the severity of accidents by selecting the importance of variables. However, a number of variables with overlapping meanings such as time and date were selected. Accordingly, the future work aims to increase the number of single models used to generate an ensemble model and, design a traffic accident severity prediction model with better performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Accuracy** & **Recall** & **Precision** & **F1-Score** \\ \hline Proposed & 0.9686 & 0.9446 & 0.9089 & 0.9264 \\ [PERSON] et al. [39] & 0.8820 & 0.8345 & 0.8856 & 0.8875 \\ [PERSON] et al. [6] & 0.8606 & 0.7630 & 0.8114 & 0.7865 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of the performance comparison.  
**Author Contributions:** Conceptualization, B.-K.K. and J.-W.B.; methodology, K.-Y.C.; resources, B.-K.K., J.-W.B. and K.-Y.C.; validation, B.-K.K., J.-W.B. and K.-Y.C.; formal analysis, B.-K.K., [PERSON]-W.B. and K.-Y.C.; investigation, B.-K.K.; resources, B.-K.K.; data curation, B.-K.K. and [PERSON]-W.B.; writing--original draft preparation, B.-K.K. and [PERSON]-[PERSON]; writing--review and editing, [PERSON]-W.B. and K.-Y.C.; visualization, B.-K.K. and [PERSON]-W.B.; supervision, K.-Y.C.; project administration, K.-Y.C.; funding acquisition, K.-Y.C. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 2 ICTAP-C157011-02).  
**Data Availability Statement:** Data sharing not applicable.  
The authors declare no conflict of interest.