Abstract

Urban transportation systems face significant challenges due to increasing congestion, inefficient routes, and fluctuating passenger demand. Traditional public transportation networks often struggle to adapt dynamically to these challenges, leading to delays, overcrowding, and environmental inefficiencies. This paper explores how Artificial Intelligence (AI) and IoT technologies can optimize urban mobility by enabling real-time route optimization, demand forecasting, and passenger flow management. By integrating data from GPS trackers, fare collection systems, and environmental sensors, cities can reduce travel times, enhance commuter satisfaction, and promote sustainable transportation. Experimental results demonstrate improvements in route efficiency, passenger load balancing, and operational costs, offering a blueprint for AI-driven smart urban mobility.