Abstract
Conceptual framework for accident prediction is an essential toolkit to curb accidents and fatalities globally. Different statistical methods and soft computing techniques are used to develop accident prediction models. Accident prediction models have been developed using two approaches, i.e., multiple linear regression (MLR) and artificial neural network (ANN). ANN has been applied to predict the frequency of traffic accidents. Adaptive neuro-fuzzy inference system (ANFIS) has been used as the feature selection method. Feature selection using ANFIS gets more accuracy with ANN was considered the most suitable based on prediction accuracy and measuring errors. It gives around 81.81% accuracy. The framework of hybrid model proposed in this chapter concludes that the prediction accuracy is high when ANN is applied for accident prediction, followed by the ANFIS as a feature selection method.
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