Dynamic traffic signal scheduling system based on adaptive quad agent Double Deep Q -network algorithm
Real-time estimation of vehicle queue lengths at signalized intersections remains a significant challenge, particularly when conventional input–output traffic models fail to capture queues extending beyond detector coverage. Although Deep Q-Networks (DQNs) have demonstrated considerable potential for dynamic traffic signal control, existing approaches often suffer from large state spaces, unstable reward signals, and inefficient utilization of high-quality traffic data. To address these limitations, this study proposes an Adaptive Quad-Agent Double Deep Q-Network (AQDDQN) framework for intelligent traffic signal optimization. The proposed AQDDQN framework improves learning stability and Q-value estimation accuracy through a multi-agent reinforcement learning strategy. The model analyzes the relationship between vehicle queue length and reward values to optimize signal control decisions. Historical traffic data are utilized to establish preconditions, time-based prediction errors are computed, and optimal signal phases are selected based on minimum loss across multiple preconditions. The experimental evaluation includes agent-wise behavioral analysis and comparative assessments against Double Deep Q-Network (DDQN), Fixed Point Techniques, and Improved DDQN methods. Performance is evaluated using metrics such as reward values, queue lengths, predicted overflow delays, and queue length–reward relationships. The proposed adaptive framework demonstrates superior performance compared with existing approaches by improving traffic signal control accuracy, reducing prediction errors, and enhancing overall traffic throughput. Simulation results indicate that the AQDDQN model effectively supports dynamic signal phase adaptation, minimizes congestion, and provides more accurate queue length estimations under complex traffic conditions. The findings confirm the effectiveness and robustness of the proposed AQDDQN framework for real-time intelligent traffic management. By improving learning stability and adaptive decision-making capabilities, the model offers a practical solution for optimizing traffic operations at signalized intersections and has strong potential for deployment in future smart transportation systems.