View article

With the integration of artificial intelligence and traffic systems, intelligent traffic systems are utilizing enhanced perception coverage and computational capabilities to provide data-intensive solutions, achieving higher levels of performance than traditional systems. This paper combines the D3QN algorithm from deep reinforcement learning with practical issues and proposes an intelligent emergency traffic signal control system based on Deep Reinforcement Learning(DRL). The system takes into account pedestrian movement and utilizes real-time traffic data and environmental information to model traffic flow and road conditions within a novel state space. It employs the Dueling Double Deep Q-Network (D3QN) to optimize signal control strategies. The system dynamically adjusts signal timings to enhance operational efficiency at intersections. By using the Weibull distribution to simulate realistic traffic congestion …