Real-Time Urban Traffic Coordination Through Generative Optimization and Intelligent Transmission Systems

Abstract

Urban transportation systems have become increasingly complex due to rapid urbanization, rising vehicular density, connected mobility infrastructures, and the continuous expansion of smart city ecosystems. Modern intelligent transportation systems require real-time traffic coordination, adaptive communication management, congestion-aware routing, and low-latency traffic transmission mechanisms to ensure efficient urban mobility and transportation sustainability. However, traditional urban traffic control frameworks often experience significant limitations related to communication congestion, inefficient traffic scheduling, high energy consumption, delayed transmission coordination, routing instability, and limited adaptability to dynamic transportation conditions. To address these challenges, this research proposes a Real-Time Urban Traffic Coordination Framework Through Generative Optimization and Intelligent Transmission Systems that integrates generative optimization, adaptive traffic orchestration, intelligent communication routing, congestion-aware scheduling, and energy-efficient transmission management into a unified smart urban transportation architecture. The proposed framework utilizes hybrid generative optimization techniques, intelligent transmission control, adaptive route coordination, real-time traffic prediction, and dynamic communication management to optimize traffic flow stability, packet transmission efficiency, communication throughput, congestion mitigation capability, and urban mobility coordination while minimizing communication latency, packet loss, routing overhead, and operational energy consumption. The framework continuously analyzes urban traffic density, vehicular mobility patterns, communication workload, transmission delay, route occupancy, and congestion probability to dynamically optimize traffic transmission and routing decisions.