Computational Framework for Analytical Operation in Intelligent Transportation System using Big Data

Intelligent Transportation System (ITS) is the future of current transport scheme. It meant to incorporate an intelligent traffic management operation offer vehicles more safety and valuable traffic-related information. A review existing approaches showcases implementation varied scattered schemes where analytical mainly emphasized. However, some significant shortcomings are witnessed in efficiently managing complex data. Therefore, proposed system introduces a novel computational framework with joint toward processing using big data targeting manage raw efficiently. As feature, model manager who can stream, followed by decentralized management, that identify eliminate artefacts statistical correlation. Finally, predictive modelling incorporated knowledge discovery highest accuracy. The simulation outcome shows Random Forest excels 99% accuracy, which all machine learning approaches, along accomplishment 11.77% reduced overhead, 1.3% delay, 67.47% time compared approaches.