ARTIFICIAL INTELLIGENCE ALGORITHMS FOR REAL-TIME TRAFFIC MANAGEMENT AND OPTIMIZATION IN AUTONOMOUS VEHICLES

Abstract

This study explores the application of artificial intelligence (AI) algorithms for real-time traffic management and optimization in autonomous vehicles (AVs), focusing on their impact on traffic flow, safety, and energy efficiency. AI-driven systems, incorporating machine learning and reinforcement learning, were deployed in various traffic scenarios, including peak hour traffic, accident conditions, and road closures. The results demonstrated a significant reduction in congestion, with an average decrease of 32.5% during peak hours, along with a 60% reduction in accidents across all scenarios. Additionally, the implementation of AI algorithms led to a 7.5% reduction in energy consumption, highlighting the potential of these systems to contribute to environmental sustainability. Real-time traffic data analysis ability of AI enables smarter decisions during decision-making processes while improving coordination between autonomous vehicles and vehicles with human operators. The promising results of the study encountered obstacles in data privacy, cybersecurity and integrating systems with existing infrastructure. AI-based traffic management systems deliver significant benefits to efficiency and safety but proper solution of encountered issues remains essential for general adoption. The research demonstrates how artificial intelligence brings revolutionary possibilities to urban traffic management which enables next-generation transportation solutions.