AI Powered Reinforcement Learning Algorithm for Reducing Traffic Accidents

Traffic and accidents continue to plague urban transit networks. These factors increase travel time, fuel consumption, and safety risks. Rule-based routing and fixed-timing signal control become less effective at controlling congestion and more dangerous when traffic circumstances vary. DQN and Q-learning, recently developed machine learning-based traffic control methods, struggle with training instability, real-time adaption, and scalability. Due to these limits, a smart, self-learning technology to improve traffic flow, reduce congestion, and make roadways safer is needed now. These models are better than rule-based systems, but they can't respond in real time, making them ineffective in unpredictable or accident-prone traffic. Current techniques include drawbacks such a lack of adaptability, difficulty with high-dimensional environments, and delayed convergence while learning ideal traffic regulations. Due to exponential increase in state-action spaces, value-based reinforcement learning models like Q-learning are computationally expensive for large-scale traffic management. To solve these challenges, this research proposes the Hybrid Advantage Actor-Critic (A2C) Algorithm, which combines policy-based and value-based reinforcement learning for real-time traffic optimization. The critic network evaluates the actor network's traffic control operations, which the actor network dynamically selects, to ensure optimal decision-making. This combination allows the system to learn faster, adapt in real time, and perform consistently and scalable. The trials show that the suggested strategy reduces waiting times by 35%, accidents by 30%, and traffic congestion by 40%. A2C has faster convergence, more flexibility, and better stability than conventional methods. This study shows that AI-driven reinforcement learning models work in traffic control, paving the way for smart transport systems.