Description

Transportation has literally converted impossible ideas into amazing realities. Transportation, however, comes with accidents that create mild to fatal injuries and reducing the fatalities and injuries of road traffic accidents, have long been a concern for governments and the general public. In most of the existing studies of risk factors, the data are categorized according to the accident severity level, for instance, fatal, severe, slight injury, and property damage only. The other data fields can be used to model or estimate the severity level, and the modeling or estimation allows a common and effective way of evaluating risk factors. In order to overcome the limitations of regression models, classification models of data -mining approaches have been applied to the risk factor analysis problem. A classifier is a function that classifies th e class variable given a set of input variables which are called feature or attribute variables . Typically, severity level is set as a class variable and risk factors are se t as feature variables. Transportation continues to be an integral part of modern life, and the importance of road traffic safety cannot be overstated. Consequently, recent road traffic safety studies have focused on analysis of risk factors that impact fatality and injury level (severity) of traffic accidents. While some of the risk factors, such as drug use and drinking, are widely known to affect severity, an accurate modeling of their influences is still an open research topic. Furthermore, there are innumerable risk factors that are waiting to be discovered or analyzed