Quality of Service Aware Secure Caching using Intelligent Prediction of Traffic Congestion in Intelligent Transportation System

Abstract

Vehicular cyber-physical systems constitute indispensable elements within modern newlinetransportation frameworks, facilitating seamless data transmission across diverse newlineapplications, including safety, mobility, and sustainability domains. Despite notable strides in enhancing traffic flow prediction through machine learning algorithms, standalone models often encounter challenges in attaining optimal accuracy levels due to the inherent complexity of vehicular networks. This dissertation presents a pioneering hybrid learning algorithm, BAT-LSTM, integrating BAT Optimization with Long Short-Term Memory (LSTM) networks to elevate traffic flow prediction accuracy. The BAT Algorithm meticulously refines hyperparameters for LSTM predictors, yielding substantial improvements in prediction accuracy by harnessing the temporal dependencies and non- linear dynamics inherent in traffic data. Exhaustive evaluation on real-time traffic datasets, newlineenriched with features such as traffic density, vehicle speed, and road conditions, newlinedemonstrates the superior performance of the BAT-LSTM approach across an array of newlinemetrics, including accuracy, sensitivity, and selectivity, outperforming conventional newlinemethods, and showcasing its efficacy in predicting traffic congestion with unparalleled precision. newline