Modern cities' fast expansion of urban populations has resulted in more vehicle traffic, aggravating congestion and delays as well as environmental damage. Often reactive and rule-based, traditional traffic management systems are not enough to manage the dynamic character of metropolitan transportation networks. This work suggests an artificial intelligence (AI)-driven framework for real-time traffic management targeted at increasing mobility, thus lowering congestion, and so improving general traffic efficiency in smart cities. Leveraging data from sensors, traffic cameras, GPS-enabled vehicles, and IoT infrastructure, the system includes advanced AI techniques—such as deep learning for traffic prediction, reinforcement learning for adaptive signal control, and graph-based models for dynamic routing decisions. Using a simulated urban setting with real-world traffic data, the framework is evaluated showing notable advantages in trip time reduction, traffic flow optimization, and emission management over conventional methods. By allowing data-driven, responsive, and sustainable traffic management solutions for the smart cities of the future, this study emphasizes how artificial intelligence might alter urban transportation.
 

Artificial Intelligence; Smart Cities; Real-Time Traffic Management; Intelligent Transportation Systems (ITS); Urban Mobility; Deep Learning; Traffic Optimization; IoT