Optimizing Emergency Vehicle Navigation in Smart Traffic Grids Using Reinforcement Learning and Precision Traffic Sensing

Abstract

Efficient and timely navigation of emergency vehicles in smart traffic squares is critical for ensuring public safety and the smooth functioning of cities. Existing traffic systems often lack the dynamic adaptability required to facilitate quick and efficient emergency response, leading to delays that can have serious consequences. This paper proposes an enhanced navigation system for emergency vehicles, leveraging a combination of reinforcement learning and precise traffic sensing recommendations. A novel Deep Q-Learning Algorithm was developed and evaluated against state-of-the-art models, including AlexNet, VGG16, VGG19, ResNet50, ResNet101, and ResNet152. The results demonstrated that the proposed algorithm outperformed these models, achieving an accuracy, precision, and recall of 0.98, along with an F1-score of 0.97. The algorithm significantly reduced travel time for emergency vehicles, thereby improving overall traffic flow in smart traffic grids. The integration of accurate traffic sensing recommendations further optimizes real-time navigation, making this approach a key advancement in smart traffic management systems.