MULTI-AGENT SELF-SUPERVISED SIMULATION FOR AUTONOMOUS TRAFFIC FLOW OPTIMIZATION IN SMART CITIES

Abstract

Urban traffic congestion has become one of the major challenges in modern smart cities due to rapid urbanization, increased vehicle density, and inefficient traffic signal coordination. Traditional traffic management systems rely on fixed-time scheduling and centralized control mechanisms, which are unable to adapt dynamically to real-time traffic conditions. This research paper proposes a Multi-Agent Self-Supervised Simulation framework for Autonomous Traffic Flow Optimization in Smart Cities. The proposed framework integrates multi-agent reinforcement learning, self-supervised learning, intelligent simulation environments, and distributed traffic coordination to optimize urban traffic flow. Each traffic intersection acts as an autonomous intelligent agent capable of learning traffic patterns from unlabeled sensor and video data without requiring extensive manual annotation. The system continuously learns through simulation-driven environments and adapts to changing traffic conditions in real time. The proposed architecture improves traffic efficiency, reduces waiting time, minimizes fuel consumption, and enhances emergency vehicle prioritization. Experimental analysis demonstrates that the proposed framework outperforms conventional traffic optimization approaches in terms of congestion reduction, throughput improvement, and adaptive decision-making.