Abstract
Traffic accidents are emerging as a serious social problem in modern society but if the severity of an accident is quickly grasped, countermeasures can be organized efficiently. To solve this problem, the method proposed in this paper derives the MDG (Mean Decrease Gini) coefficient between variables to assess the severity of traffic accidents. Single models are designed to use coefficient, independent variables to determine and predict accident severity. The generated single models are fused using a weighted-voting-based bagging method ensemble to consider various characteristics and avoid overfitting. The variables used for predicting accidents are classified as dependent or independent and the variables that affect the severity of traffic accidents are predicted using the characteristics of causal relationships. Independent variables are classified as categorical and numerical variables. For this reason, a problem arises when the variation among dependent variables is imbalanced. Therefore, a harmonic average is applied to the weights to maintain the variables’ balance and determine the average rate of change. Through this, it is possible to establish objective criteria for determining the severity of traffic accidents, thereby improving reliability.
Highlights
Most traffic accidents are caused by vehicles, and severe injuries and deaths outnumber slight-moderate injuries
Case of the arithmetic sinceTherefore, the eleventhe variables extractedOperating through the mean decrease in Gini (MDG) coefficurveincluding is used totime, evaluate the proposed classification model according to the change of cient the number of deaths, month, position, type of assailant, year, mithe threshold value
This study proposed a weight feedback-based harmonic MDG-ensemble model for the prediction of traffic accident severity
Summary
Most traffic accidents are caused by vehicles, and 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]. 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. A method for deriving traffic accident severity objectively and immediately must be developed
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