Energy-Aware Traffic Transmission Control in Smart Transportation Networks Using Metaheuristic Optimization

Abstract

Smart transportation networks have become critical components of intelligent urban infrastructure due to the rapid growth of connected vehicles, autonomous transportation systems, Internet of Things (IoT)-enabled traffic environments, and next-generation wireless communication technologies. Modern transportation systems continuously generate massive traffic data streams requiring efficient communication management, adaptive traffic transmission control, low-latency routing, and energy-efficient network coordination. However, increasing vehicle density, dynamic mobility, communication congestion, limited energy resources, packet transmission delay, and unstable routing conditions create major challenges for traffic transmission reliability and intelligent transportation management. Conventional traffic control mechanisms and traditional routing algorithms often experience high computational complexity, excessive communication overhead, increased latency, inefficient energy utilization, and limited adaptability to highly dynamic smart transportation environments. To address these limitations, this research proposes an Energy-Aware Traffic Transmission Control Framework Using Metaheuristic Optimization for Smart Transportation Networks that integrates adaptive traffic scheduling, energy-efficient routing, metaheuristic optimization, intelligent congestion control, and real-time communication management into a unified transportation communication architecture. The proposed framework utilizes hybrid metaheuristic optimization techniques, intelligent traffic prioritization, dynamic route adaptation, and energy-aware communication coordination to optimize packet transmission efficiency, traffic flow stability, communication reliability, and network scalability while minimizing communication delay, packet loss, routing overhead, and energy consumption. The framework continuously analyzes traffic density, vehicle mobility, communication load, transmission latency, and network congestion to dynamically optimize traffic routing and transmission scheduling decisions. Experimental evaluation demonstrates that the proposed energy-aware metaheuristic framework significantly improves throughput, packet delivery ratio, traffic transmission efficiency, congestion mitigation capability, routing stability, and energy utilization while reducing communication latency, packet loss, and network overhead compared with conventional smart transportation communication systems. The proposed architecture establishes a scalable, adaptive, intelligent, and energy-efficient communication control framework suitable for next-generation smart transportation ecosystems and intelligent vehicular networking infrastructures.