A Framework for Analyzing Road Accidents Using Machine Learning Paradigms
Shweta, Jagdish Yadav, Khushi Batra, Amit Kumar Goel
Abstract
Abstract Road Safety is a matter of great concern throughout the world. As number of casualties is increasing more than 4% annually in all age groups. It has been predicted that due to road accidents causality rate will grow around 8% till 2030. It’s entirely admissible and saddening to let citizens get killed in road accidents. As a result, to handle this sort of situation, an in-depth analysis is required. The Data of Road accidents are very heterogeneous in nature so analysis of such type of data is tricky. Segmentation is the main task for analyzing such data. So, K-means clustering method is mainly used for it as proposed in the research work. Second task of this model is to extract the data, images and hidden patterns by using Supervised Machine Learning algorithm that will help to form the policies for the prevention from road accidents. The combination of segmentation machine learning algorithm produces meaning full information.