An intelligent and resolute Traffic Management System using GRCNet-StMO model for smart vehicular networks

Abstract

One of the key components of a smart city is thought to be the traffic control system. Road traffic congestion is prevalent in big cities due to increasing population density and rising transportation in cities. A smart traffic control system using cutting-edge computational intelligence algorithms has been developed to address numerous challenges related to traffic management on road networks and to assist regulators in making sound decisions. The current endeavor seeks to develop a new type of Smart Traffic Management System (SmartTMS) using state-of-the-art deep learning and optimization methods. The hybrid Gated Recurrent Deep Convoluted Network (GRCNet) approach is applied to accurately forecast the traffic congestion from the smart vehicular networks. In order to improve the classifier's decision-making ability and prediction accuracy, the parameters of the deep learning algorithm are tuned using a revolutionary Starling Murmuration Optimizer (StMO) methodology. Moreover, traffic congestion in vehicle networks can be precisely diagnosed and decreased with a low error rate and high accuracy by using the GRCNet-StMO model combination. The proposed SmartTMS's main benefits are its ease of deployment, quick congestion forecast time, and minimal computing complexity. To evaluate the effectiveness of the suggested model, a comprehensive performance and comparison study is carried out in this work, taking into account a number of factors like error rate, accuracy, miss rate, and journey duration.