Accident black spot identification based on classical and computational intelligence methods.

Accident prediction and Black Spot identification have a significant role in improving traffic safety and urban traffic management. This paper deals with developing the model to identify black spot locations using the Classical approach that relies on historical accident occurrence and the Computational Intelligence approach. A hybrid approach with a combination of the best fit Artificial Neural Network (ANN) model and classical Accident frequency method results in 97.72% accurate prediction of the black spots. Combining the best fit ANN model and the Accident severity method results in 93.18% accuracy in predicting black spots. It concluded that combining Computational Intelligence and a Classical approach ensures better accuracy in identifying black spots. The uniqueness of the applied model in this research also includes combining Classical methods and Fuzzy C-means clustering to identify the road segments recognized as black spots. It also identifies the road segments categorized in the medium possibility range expected to be promoted to highly vulnerable groups. [ABSTRACT FROM AUTHOR]