AI-Powered Solutions for Traffic Management in U.S. Cities: Reducing Congestion and Emissions

Abstract

Traffic congestion and vehicle emissions present substantial challenges in U.S. cities, significantly impacting urban sustainability and public health. Traditional traffic management solutions often fall short in addressing the complex, dynamic demands of modern urban roadways. This study explores the transformative potential of AI-powered traffic management systems in reducing congestion and emissions in urban areas. By integrating predictive analytics, real-time monitoring, and machine learning algorithms, AI technologies can optimize traffic flow, adjust signal timings dynamically, and improve route planning for both individual vehicles and public transit systems. This paper examines case studies from major U.S. cities, highlighting the effectiveness of AI in traffic forecasting, congestion mitigation, and emissions reduction. Results demonstrate that AI-driven systems enhance overall traffic efficiency, lower vehicle idle times, and contribute to reduced carbon footprints. The findings underscore AI's role in enabling smarter, greener, and more resilient urban transportation networks, offering insights into policy development and infrastructure investment priorities for sustainable urban growth.