An artificial intelligence-based approach for avoiding traffic congestion in connected autonomous vehicles

Abstract

The Internet of Vehicles (IoV) has led to the emergence of sustainable smart roads. Recent advancements in this field have focused on improving traffic flow and reducing congestion using intelligent systems. In this paper, we propose a novel approach called the 'Traffic Congestion Avoidance Approach (TCAA)'. Our approach leverages IoV technologies and deep learning algorithms to create a more responsive and efficient traffic management system. The IoV model facilitates communication between autonomous vehicles, allowing them to coordinate movements and optimise traffic flow seamlessly. Additionally, deep learning algorithms analyse real-time data, to predict and mitigate congestion dynamically. The performance evaluation of TCAA demonstrates the potential of intelligent traffic regulation systems. The union of IoV and deep learning technologies provides a robust solution to contemporary traffic challenges, paving the way for smarter, more sustainable urban mobility. This research underscores the transformative potential of AI-powered IoV systems in creating the smart roads, ultimately enhancing the quality of life in smart cities.