The Application of Artificial Intelligence in Optimizing Urban Traffic Management and Reducing Air Pollution Using Smart Sensor Networks

Rapid urbanization and the increasing number of vehicles have posed serious challenges to urban traffic networks, such as congestion, time loss, high fuel consumption, and growing air pollution. In recent years, Artificial Intelligence (AI) and Smart Sensor Networks (SSNS) have emerged as two key technologies driving the development of smart cities. AI, through machine learning, deep learning, expert systems, and intelligent optimization algorithms, enables the analysis of large-scale traffic and environmental data. Meanwhile, smart sensor networks provide real-time data collection from intersections, roads, vehicles, and air quality monitoring stations, forming the foundation for intelligent decision-making. This review paper examines the main applications of AI in urban traffic management and air pollution reduction within the framework of smart sensor network utilization. It first defines the core concepts of artificial intelligence, intelligent traffic management, air pollution, and smart sensor networks. Then, various AI-based methods in traffic light control, smart routing, traffic prediction, incident detection, and pollutant emission management are discussed. Furthermore, common architectures of smart sensor networks in urban environments and their role in supplying accurate, real-time data for AI models are elaborated. The results of the review indicate that integrated use of AI and sensor networks can significantly reduce travel delays, fuel consumption, and concentrations of pollutants such as NOx, CO, and PM۲.۵. Finally, challenges and future opportunities regarding the implementation of these technologies in Iranian cities are presented, along with suggestions for further research.

کلیدواژه ها:

Artificial Intelligence (AI) ، Urban Traffic Management ، Air Pollution ، Smart Sensor Network ، Smart City ، Machine Learning