Adaptive Sensor Fusion Algorithms for Real-Time Traffic Flow Management Under 5G Connectivity

Abstract

The integration of adaptive sensor fusion algorithms into traffic flow management systems has emerged as a crucial strategy for addressing the increasing demands of urban mobility. With the advent of 5G connectivity, the potential for real-time data acquisition, transmission, and processing has been significantly enhanced, enabling the implementation of highly responsive and scalable solutions. This paper explores the development and application of adaptive sensor fusion algorithms designed to optimize traffic flow by leveraging heterogeneous data sources, including vehicular sensors, roadside units, and drone-based monitoring systems. The proposed framework emphasizes the dynamic weighting of sensor inputs based on reliability, latency, and contextual relevance, thereby improving decision-making accuracy in real-time scenarios. Key performance metrics such as average travel time, congestion levels, and incident detection rates are analyzed to evaluate the efficacy of the system under varying traffic and network conditions. Results indicate that the combination of advanced sensor fusion methodologies and 5G's low-latency communication infrastructure significantly enhances the responsiveness and adaptability of traffic management systems. This research highlights the transformative potential of integrating cutting-edge communication technologies with intelligent data processing algorithms to achieve sustainable and efficient urban mobility. The findings contribute to the development of next-generation traffic systems capable of addressing the complexities of modern transportation networks.