Intelligent traffic signal optimization algorithm based on multi-source data fusion

Abstract

Urban traffic congestion increases pollution, fuel consumption, and travel time, highlighting the limitations of traditional signal systems that rely on outdated schedules and insufficient sensors. This study proposes the Dynamic Traffic Adaptive Signal Optimization (DTASO) method, which leverages multi-source data fusion to dynamically adjust signal timings based on real-time traffic data from cameras, sensors, and historical trends. Powered by Adaptive Decentralized Deep Reinforcement Learning (ADDRL), DTASO improves traffic flow and system performance by continuously learning and adapting to changing traffic patterns. Experimental results demonstrate that DTASO outperforms conventional signal optimization approaches in reducing congestion and enhancing traffic efficiency, offering a scalable and intelligent solution for urban traffic management.