ADVANCED RIDGE REGRESSION USING IMFT MODEL FOR RSU DESIGN IN VEHICULAR NETWORKS

In the realm of Vehicular Ad Hoc Networks (VANETs), Roadside Units (RSUs) play a pivotal role in enhancing communication, data processing, and predictive analytics. This paper introduces a novel hybrid design that integrates Ridge Regression and XG-Boost algorithms to optimize the data processing and prediction capabilities of RSUs, aimed at improving traffic management and safety applications. The hybrid framework with IMFT (inter-intra mod filter) algorithm employs Ridge Regression for robust initial data processing, minimizing overfitting and ensuring reliability in the noisy, dynamic environment of vehicular data. Feature extraction with IMFT is utilized to encompass the relevant features before utilizing Ridge-Model for high-accuracy predictions, leveraging its gradient boosting capabilities to facilitate timely interventions and optimize traffic flow. Furthermore, the architecture of the RSU is expanded to include essential units such as communication modules, data storage, and user interface components, all functioning cohesively to create a comprehensive system. With the proposed IMFT design we have incorporated extensive simulations with K-fold loss to demonstrate that the proposed IMFT with Ridge Model design significantly enhances prediction accuracy and processing efficiency compared to traditional methods (Elastic Net.) with more than 98% of improved R2-score. By optimizing the operational capabilities of RSUs in VANETs, this work contributes to the development of smarter and safer urban mobility solutions, paving the way for more effective traffic management and improved vehicular safety.