Optimizing Road Transportation Big Data: A Novel Approach for Feature Selection through Optimization Techniques

Abstract

Road traffic accidents are very essential for common people, consequential an estimated 1.2 million deaths and 50 million injuries all over the world every year. In this emerging world, the road accidents are among the principal reason of fatality and injury. The concern of traffic safety has heaved immense alarms across the manageable enhancement of contemporary traffic and transportation. The analysis on road traffic accident grounds can detect the major aspects quickly, professionally and afford instructional techniques to the prevention of traffic accidents and reduction of road traffic accident, which might significantly decrease personal victim by means of road traffic accidents. Data Mining techniques are used in the process of knowledge discovery for many domains’ problems. Feature Selection plays a vital role for a large number of datasets. In this research work, the model involves two main phases (i) Feature Selection and (ii) Classification. Since the length of feature vector tends to high, optimal feature selection technology is included, from which the most relevant features are selected by the Lion-based Firefly Algorithm which is referred as Optimization based Feature Selection Method (OFSM). The main objective of this paper is projected on minimizing the correlation between the selected features, which results in providing diverse information regarding the different classes of data. Once, the optimal features are selected, the classification algorithm called Neural Network (NN) is adopted, which can classify the data in an effective manner with the selected features.