## 3 Characteristics of Dataset  
The dataset we used includes 362,298 cases of traffic accidents that occurred between 2010 to 2018 in Seoul, provided by a public data portal [22]. The characteristics of the dataset are summarized in Table 2.  
In the pre-processing step, data with unknown values (i.e., null) were removed. Additionally, extremely low-frequency attributes (i.e., less than 0.01%) were removed because it is challenging to develop a good model if the data distribution is imbalanced. The classification criteria for slight injury and serious injury are as follows: \"slight injury\" implies an injury that requires treatment for more than five days but less than three weeks due to a traffic accident. In contrast, \"serious injury\" implies an injury that requires treatment for at least three weeks due to a traffic accident. Further, \"death\" is considered as death within 30 days from the time of a traffic accident. In this study, serious accidents and deaths were equally treated as serious accidents, including life-threatening cases [23]. Because the data are all categorical data, we used one-hot encoding to transform them into a vector space model to use conventional machine learning algorithms. However, the use of one-hot encoding can dramatically increase the number of variables, resulting in poor classification performance of the algorithm. Therefore, we grouped the attribute values to reduce the number of attributes. For example, days of occurrence were grouped by season, and accident occurrence times were grouped into dawn (0: 00-6:00), day (6:00-18:00), and night (18:00-24:00). Further, the ages were grouped into underage (0-18 years), youth (19-34 years), middle-aged (35-49 years), old-aged (50-64 years), and elderly (\(\geq\)65 years). After pre-processing, the classification criteria and distribution ratios for each variable were \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Factors** & **Variables** & **Data Type** & **Attribute Values** \\ \hline \multirow{6}{*}{Human Factor} & \multirow{6}{*}{Volation of law} & \multirow{6}{*}{Categorical} & Side collision, backup collision, head-on collision, rear-end collision, crossing, passing on driveway, passing on the edge of the road, passing on the sidewalk, other Non-compliance with safe driving obligation, not keeping a safe distance, signal violation, intersection crossing procedure violation, centerline violation, pedestrian protection obligation violation, speeding, other \\  & & & Male, female, unidentified \\  & & & 1-117 years old, unidentified \\  & & & 1-117 years old, unidentified \\  & & & 1-117 years old, unidentified \\ \hline \multirow{6}{*}{Road Factor} & \multirow{6}{*}{Volation of law} & \multirow{6}{*}{Categorical} & Dry, wet, frozen, snow, flooding, unidentified \\  & & & At an intersection, near an intersection, on a crosswalk, near a crosswalk, over the bridge, single road, crosswalk at an intersection, inside a tunnel, over an overpass, in an underpass, railroad crossing, unidentified, other \\  & & & Passenger car, lorry, two-wheeler, van, bicycle, motorized bicycle, heavy equipment, specialty \\  & & & vehicle, unidentified, other \\  & & & Passenger car, lorry, two-wheeler, van, bicycle, \\  & & & Passenger car, lorry, two-wheeler, van, bicycle, \\  & & & motorized bicycle, heavy equipment, specialty \\  & & & vehicle, pedestrian, unidentified, other \\ \hline \multirow{6}{*}{Environmental Factor} & Occurrence day & Categorical & 1 January 2010–31 December 2018 \\  & Occurrence time & Categorical & 00:00–23:00 \\ \cline{1-1}  & Day of the week & Categorical & Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday \\ \cline{1-1}  & Weather & Categorical & Sunny, rainy, cloudy, snowy, foggy, unidentified \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Characteristics of the dataset. presented with a table separated by human factors, road factors, and environmental factors. A vast number of variables could be easily seen (Tables 3-5).