Routing Optimization of the Internet of Vehicle with Artificial Intelligence

This electronic document is a “live” template and already defines the components of your paper [title, text, heads, etc.] in its style sheet. The integration of Internet of Vehicle (IoV) technologies with Artificial Intelligence (AI) gave path to Industry 4.0, that revolutionized the industrial in smart transportation and supply chain management. The primary challenge with this integration is the routing optimization by reducing delay, minimize costs, environmental impacts, energy requirements and increase the performance. This paper explores the application of Ai for the routing optimization of IOV to enhance decision-making processes for route selection in dynamic environments. IOV collect real-time data such as traffic details, location accuracy, vehicle performance, velocity, distance and weather information, which is then analyzed by AI based machine learning models such as Random Forest (RF), Logistic Regression (LR), Ada Boost (AB), Support Vector Machine (SVM). The AB provides 99.99% of accuracy, precision, recall followed by the RF with 99.96%t. AI models can continuously provide adaptive routing solutions that enhances the quality of the decision-making. The proposed work highlights the benefits of AI for improved accuracy in route planning, cost-effectiveness, and enhanced operational efficiency, real-time, data-driven decision-making, which can significantly reduce delays, carbon emissions, and overall logistical costs. The proposed work also discusses challenges, including data privacy concerns, system integration issues, and scalability, with the suggestion for the future research directions.