A Novel Approach On PPG RAP Kernel For Symbiotic Pavement Calamity Forecast Using Optimized Machine Learning Techniques

Abstract

Rapid growth of population coupled with increased economic activities has newlinefavored in tremendous growth of motor vehicles. This is one of the primary newlinefactors responsible for road accidents. Metro cities provide the efficient mobility newlineand accessibility function. The increasing road accidents have created social newlineproblems due to loss of lives and human miseries. Road accidents are newlineessentially caused by interactions of the vehicles, road users and roadway conditions. newlineEach of these basic elements comprises a number of sub elements like pavement newlinecharacteristics, geo newlinecharacteristics and environmental aspects. This research proposes the PPG-RAP newlineclassification algorithm in order to find out the relevant patterns and to predict the newlineaccident zones and its severity based on the various traffic accidents with the help of newlineinfluential environmental features of road accidents. To clean and preprocess the newlinedata, transformation and arithmetic mean computation is used. The optimum features newlineare selected from the dataset using Binary particle swarm optimization (BPSO) newlinebased feature selection method. newlineThe factors like lighting condition, Road surface, temperature, visibility and newlinevehicle are related to traffic accidents, to determine accident zone some of these newlinefactors are most important. This research proposes the PPG-RAP classification newlinealgorithm in order to find out the relevant patterns and to predict the accident newlinezones and its severity based on the various traffic accidents with the help of newlineinfluential environmental features of road accidents. The real time data also used newlineto evaluate the efficiency of PPG-RAP classifier. To clean and preprocess the newlinedata, transformation and arithmetic mean computation is used. The optimum newlinefeatures are selected from the dataset using Binary particle swarm optimization newline(BPSO) based feature selection method.