Artificial Intelligence: Urban Congestion Challenges and Solutions

In this research, we explore how Artificial Intelligence (AI) can address urban congestion challenges by providing innovative and adaptive traffic management solutions. Urban congestion is driven by factors such as rising last-mile delivery demand, poor traffic coordination, and inefficient route optimization, leading to increased travel time and environmental impact. Through a systematic literature review, we identify key congestion challenges and assess AI-driven approaches, including traffic prediction models, smart traffic lights, adaptive control systems, and IoT-based infrastructure. Our methodology involves analyzing real-time traffic data to optimize traffic flow and mitigate bottlenecks. The findings highlight AI's potential to enhance urban mobility, facilitate autonomous vehicle integration, and support sustainable city planning. This research provides valuable insights for policymakers and urban planners on leveraging AI for efficient and intelligent congestion management.