# Weight Feedback-Based Harmonic MDG-Ensemble Model for Prediction of Traffic Accident Severity
## 3 Weight Feedback Based Harmonic MDG-Ensemble Model for Predicting Traffic Accident Severity
### Pre-Processing Traffic Accident Data  
There are various types of traffic accidents, such as vehicle-to-vehicle, vehicle-to-person, and vehicle-to-bicycle accidents. In addition, the degree of injury, number of deaths, and costs of accident handling are different. Accordingly, the level of traffic accident severity is subjectively predicted based on the severity of a driver's injuries, the number of deaths, or the extent of vehicle damage. The severity attributed by a subjective opinion lacks supporting evidence and has low reliability. Thus, objective criteria must be set for determining severity according to the types of traffic accidents. To eliminate unnecessary data from the analysis and to increase efficiency, pre-processing is applied. The traffic accident information data used in this study is based on the 2012 to 2018 serious traffic accident information provided by the Korean Road Traffic Authority [24]. This includes twenty-seven variables (e.g., date and day of occurrence, number of deaths, and number of injuries) in 30,913 transactions. However, no variables related to traffic accident severity were found. Therefore, following the accident severity calculation method used by the Korean Road Traffic Authority [24], traffic accident severity variables are generated and categorized. This is calculated by multiplying the number of deaths and serious injuries by 70%, and the number of casualties by 30%. Accordingly, if the value of a traffic accident's severity is below 2, the severity level is categorized as '2'; if between 2 and 3, it is categorized as '2'; if more than 3, it is categorized as '3'. The data consists of numerical and text data and must be converted into numerical and categorical data through pre-processing. Data with duplicate meanings or the data that are unconvertible to numerical data like 'occurrence place type (city or county)' are eliminated. Table 1 shows the pre-processing results of traffic accident information data, consisting of a variable name, data type, category, and meaning.  
As shown in Table 1, 13 variables and 18,700 transactions were extracted through pre-processing. Under 'Traffic Accident Severity', '1' means 'Slight-moderate'; '2' means 'Serious'; '3' means'very serious'. 'Year' is categorized according to the year of occurrence. 'Violation_law' a large category of law violations is categorized between '0' and '2': '0' means a pedestrian's negligence; '1' means a driver's violation; '2' means poor maintenance of the vehicle. 'Road_type' a large category of road type is categorized between '0' and '4': 0' means crossroad; '1' means others/undefined; '2' means one way only; '3' means parking lot; '4' means railroad crossing. However, based on the pre-processing results, it is difficult to identify the factors that influence severity. For this reason, the important variables that influence severity are extracted. therefore, to prevent over-fitting of data analysis, it is divided into 70% of training data, 20% of test data, and 10% of validation data.  
Figure 4: Weight feedback based harmonic MDG-ensemble model process for prediction of traffic accident severity.