Conceptual Framework for the Application of the ANN Model in Accident Prediction: A Study of Central Kolkata

View Sample PDF

Author(s): Amrita Sarkar (Birla Institute of Technology, Mesra, India)and Satyaki Sarkar (Birla Institute of Technology, Mesra, India)
Copyright: 2024
Pages: 27
Source title: Theoretical and Conceptual Frameworks in ICT Research
Source Author(s)/Editor(s): Agripah Kandiero (Insituto Superior Mutasa (ISMU), Mozambique & Mozambique Institute of Technology, Mozambique & Africa University, Zimbabwe), Stanislas Bigirimana (Africa University, Zimbabwe)and Sabelo Chizwina (University of South Africa, South Africa)
DOI: 10.4018/978-1-7998-9687-6.ch003

Keywords: Information Science Reference / Library & Information Science / Library Information Systems / Research Methods

Purchase

View Conceptual Framework for the Application of the ANN Model in Accident Prediction: A Study of Central Kolkata on the publisher's website for pricing and purchasing information.

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.

The IRMA Community

Research IRM

Conceptual Framework for the Application of the ANN Model in Accident Prediction: A Study of Central Kolkata

Purchase

Abstract

Related Content

IRMA Sponsors