# Weight Feedback-Based Harmonic MDG-Ensemble Model for Prediction of Traffic Accident Severity
## 1 Introduction  
Most traffic accidents are caused by vehicles, and thus severe injuries and deaths outnumber slight-moderate injuries. According to the World Health Organization (WHO), globally about 1,350,000 people die due to traffic accidents every year, and the annual number of deaths is on the rise [1]. The influences on traffic accidents include human factors, such as a driver's physical defects, driving habits, and safety consciousness material factors including poor vehicle maintenance and deterioration, environmental factors, including weather, time, and traffic enforcement; and physical factors, including the road type and condition [2, 3]. Therefore, it is necessary to deal with traffic accidents by considering their causes and influencing factors. Indicators of traffic accidents' severity, such as the number of vehicles involved and the number of deaths and injuries, can be found at an accident site. The severity of a traffic accident is decided after expert analysis based on diverse factors. If a traffic accident's severity is assessed immediately, the scale of support from emergency vehicles such as ambulances and others can be arranged efficiently. Therefore, a method for deriving traffic accident severity objectively and immediately must be developed. With the number of car drivers increasing, there have been more demands for analyzing the actual extent of individual traffic accidents and the related potential risk factors. In addition, the development of IoT (Internet of Things) leads to a massive amount of data being generated and collected in real-time. Accordingly, big data processing technologies have been developed to find significant information among road traffic data. A typical technology for big data processing is machine learning, which is classified as supervised or unsupervised learning. Machine learning models include a support vector machine,decision tree, and k-nearest neighbor's algorithm. In these models, new knowledge and information are obtained from pre-processed data through classification, regression, correlation analysis, and other methods. In a single machine learning model, it is difficult to reflect a variety of data features, such as data distributions and patterns. Con-sequently, overfitting occurs because the training data is a subset of the actual data in some cases [4, 5]. Therefore, we proposed a traffic accident prediction model using [6] Gaussian-kernel support vector machines (SVMs), such as used by [PERSON] et.al. This solves the problem of data properties reflecting the characteristics of the data used through a Gaussian kernel SVM. However, this has problems that are limited to the data used in the study. In addition, [PERSON] et al. [7] proposed a ContextDNN (Context Deep Neural Network) model using multiple regression for risk prediction. The proposed method divides the data into a certain training, testing, and validation ratio to solve the over-fitting problem. Because this divides limited data, it is difficult to use when the amount of data is insufficient. It also predicts risk by discovering causal relationships through regression analysis. However, if data are of a categorical type, it is difficult to find the rate of change of independent variables to dependent variables. Therefore, it is necessary to conduct an efficient analysis with a small amount of data and to develop a method for finding the causal relationships of categorical data according to the rate of change of independent variables to dependent variables. [PERSON] et al. [8] proposed a method for analyzing the severity of traffic accidents at intersections using a logistic regression model. The proposed method uses nine independent variables, including the month of the accident, the period of the accident, the weather condition and condition, the road type, the road surface condition, and the traffic control type.