Smart Traffic Management Analyzing Network Congestion Mitigation Techniques in Vehicular AD-HOC Networks

Abstract

Vehicle Ad Hoc Networks (VANETs) play a vital role in developing intelligent transportation systems, enhancing traffic efficiency and safety. However, network congestion poses a significant challenge, impacting network performance and reliability. This paper presents a novel algorithm designed to mitigate network congestion in VANETs by leveraging adaptive processing capabilities, context awareness, and real-time processing. The proposed algorithm's performance is evaluated against existing techniques (D-FPAV, VMaSC, and CARPOOL) using key indicators: data throughput, latency, and packet delivery ratio (PDR). Our findings indicate that the proposed algorithm outperforms these solutions, achieving: Data throughput of 850 kbps, Latency of 120ms, and PDR of 95%. This performance is attributed to the algorithm's dynamic adjustment mechanism, multi-metric optimization, and hybrid communication strategies. These improvements ensure efficient data transmission, enhance reliability, and responsiveness of vehicle communications. The practical implications of this research are profound, offering significant enhancements in traffic flow, safety, and scalability for intelligent traffic management systems. Future research directions include: Real-world implementation, enhancing energy efficiency and Integration with emerging technologies (5G and edge computing), Paving the way for a smarter, more adaptable transportation network.