👉 The published project, titled "Enhancing Urban Air Quality through AI-Driven Traffic Management," focuses on leveraging artificial intelligence to optimize traffic flow in urban areas, thereby reducing vehicular emissions and improving air quality. Researchers developed a sophisticated AI model that analyzes real-time traffic data, weather conditions, and pollution levels to dynamically adjust traffic signals and reroute vehicles. This system aims to minimize congestion, decrease idling times, and lower the concentration of harmful pollutants in the air. By integrating IoT sensors and machine learning algorithms, the project not only enhances traffic efficiency but also contributes to public health by promoting cleaner urban environments. The findings suggest significant potential for scalable implementation in cities worldwide, offering a data-driven approach to tackling urban pollution challenges.
