Dynamic Traffic Optimization System: Leveraging IoT and Fog Computing for Enhanced Urban Mobility with the RAO Algorithm

This paper introduces a cutting-edge method for urban traffic management that harnesses the power of the RAO (named after Professor Ravipudi Venkata Rao) optimization algorithm, enhanced by the integration of the Internet of Things (IoT) and fog computing technologies. By collecting real-time traffic data from IoT devices, such as smart cameras and sensors, our approach provides a detailed and up-to-the-minute view of urban traffic conditions. This data is efficiently processed through fog computing infrastructure, which ensures minimal latency and supports immediate responses to fluctuating traffic patterns. The deployment of the RAO algorithm on this rich dataset stands out as a novel contribution, dynamically optimizing traffic flows and routes based on current conditions without the need for specific map data sources. Our research extends beyond traditional traffic management applications, offering significant benefits in emergencies by facilitating quicker medical and security responses and enhancing the management of military operations with the need for rapid and secure route planning. Through experimental simulations conducted in urban environments, our findings demonstrate substantial improvements in traffic management and congestion alleviation. The results indicate that our integrated strategy offers a viable and adaptable solution for both everyday traffic oversight and exceptional scenario planning. This study lays the groundwork for future advancements in smart city technologies, underscoring the role of sophisticated technological integration in fostering more sustainable, efficient, and secure urban landscapes.