Efficient Analysis of Traffic Accident Using Data Mining Techniques

Data Mining is the process of extracting patterns from data. Machine Learning is a scientific discipline that is concerned with the design and development of algorithms that allow computers to learn based on data, such as from sensor data or databases. A major focus of machine learning research is automatically learn to recognize complex patterns and make intelligent decisions based on data. Engineers and researchers in the automobile industry have tried to design and build safer automobiles, but traffic accidents are unavoidable. Patterns involved in dangerous crashes could be detected if we develop a prediction model that automatically classifies the type of injury severity of various traffic accidents. These behavioral and roadway patterns are useful in the development of traffic safety control policy. We believe that to obtain the greatest possible accident reduction effects with limited budgetary resources, it is important that measures be based on scientific and objective surveys of the causes of accidents and severity of injuries. This paper deals about some classification models to predict the severity of injury that occurred during traffic accidents using two machine-learning approaches. We compared Naive Bayesian classifier and J48 decision tree Classifier for classifying the type of injury severity of various traffic accidents and the result shows that J48 outperforms Naive Bayesian.