A Novel Approach on PPG-RAP Kernel for Symbiotic Pavement Calamity Forecast using Optimized Machine Learning Techniques
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Abstract
Injuries due to road accidents are one of the most prevalent causes of death apart from health related issues. In the year of 2016, about 1.35 million deaths are occurred because of road traffic, according to the statement of World Health Organization in worldwide which reveals that for every 25 seconds a death occurs. To reduce the death rate due to road accidents, it is necessary to analyze the factors affecting the road conditions and come up with the algorithm to reduce the probability of their occurrence.The factors like lighting condition, Road surface, temperature, visibility and vehicle are related to traffic accidents, to determine accident zone some of these factors are most important.This research proposes the PPG-RAP classification algorithm in order to find out the relevant patterns and to predict the accident zones and its severity based onthe various traffic accidents with the help of influential environmental features of road accidents.The real time data also used to evaluate the efficiency of PPG-RAP classifier. To clean and preprocess the data, transformation and arithmetic mean computation is used. The optimum features are selected from the dataset using Binary particle swarm optimization (BPSO) based feature selection method.
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