Fused computational approach used in transportation industry for congestion monitoring

Abstract

The Internet of Things (IoT) is heading to its mature development and continuously evolving itself as an essential component of the fifth-generation (5G) internet. IoT infrastructure requires networks for which 5G network is developed. Indeed global interest and the advancement in 5G networks has transformed the vision of smart cities into reality in terms of smart homes, smart consumer items, driverless cars, and similar daily appliances. Congestion in traffic affected the areas around the world and causing different issues like fuel wastage, anxiety, and delayed conveyances problems. To locate the exact location of congestion is one of the significant problem in the intelligent transportation system. It perceives the variation from the norm of traffic with the assistance of various sensors utilized in the progression of traffic. So far the development of intelligent transportation system given the capacity to researchers to investigate new procedures for finding the congested zones. This paper proposes the novel model for the prediction of traffic congestion in internet of vehicle using information fusion with artificial neural network and support vector machine. The proposed model obtained 98.4% accuracy, which is greater than previously mentioned approaches in the literature.