Navigating Urban Mobility: A Review of AI-Driven Traffic Flow Management in Smart Cities

In the current period of growing urbanization and aspirations towards smart cities, urban transportation is a considerable difficulty. Conventional traffic management systems are finding it difficult to remain sustainable and efficient in the face of increasing vehicle traffic and population density. In response, a thorough analysis of AI-driven traffic flow management systems designed for smart cities is carried out in this research. These systems use artificial intelligence (AI) to improve overall transportation efficiency, lessen traffic congestion, and optimize traffic flow. The study includes a survey of the literature that has already been written, emphasizing the technology, methodology, and case studies that are influencing the field of AI-driven traffic management. Artificial intelligence (AI) traffic prediction algorithms, realtime data integration, dynamic routing schemes, and control and monitoring user interfaces are important parts. The review delves at issues including interoperability, scalability, and ethical considerations related to the application of AI in urban settings. This study provides important insights into the possibilities and constraints of AI-driven traffic flow management systems, which may help academics, policymakers, and urban planners develop more sustainable and effective urban transportation networks. One of the most important aspects of developing smart cities is the effective control of urban traffic flow. The intricacies of contemporary metropolitan surroundings are frequently too much for traditional traffic management systems to handle, which causes delays, congestion, and environmental issues. To maximize urban mobility in smart cities, this study presents a unique traffic flow management system driven by artificial intelligence.