Highway smart transport in vehicle network based traffic management and behavioral analysis by machine learning models

The intelligent transport system (ITS), which gets beyond the drawbacks of the conventional transport system, has become a crucial element and is frequently used in smart cities. This study suggests a revolutionary approach to managing traffic with intelligent highway vehicle behaviour analysis utilising machine learning techniques. Here, the multiagent reinforcement markov Bayesian Gaussian model (MRMBG) monitoring system is used to regulate traffic for highway transportation. Then, the edge cloud -based fuzzy gradient propagation regressive model (FGPRM) is used to conduct the behavioural analysis. For different vehicle -based network analyses, experimental analysis is done in terms of mean squared error (MSE), average accuracy, efficiency, and traffic congestion rate. the proposed technique attained Efficiency of 96 %, average accuracy 99 %, mean squared error (MSE) of 50 %, traffic congestion rate of 97 %.