Artificial Intelligence (AI) is revolutionizing urban transportation by enhancing efficiency, sustainability, and smart city development. With rapid urbanization, cities face increasing traffic congestion, pollution, and transportation inefficiencies. AI-driven solutions, including intelligent traffic management, autonomous vehicles, and predictive analytics, offer promising advancements to address these challenges. AI-powered traffic management systems utilize real-time data from sensors, cameras, and GPS to optimize traffic flow, reduce congestion, and minimize travel delays. Machine learning algorithms analyze traffic patterns to predict congestion hotspots and dynamically adjust traffic signals, improving road network efficiency. Additionally, AI supports the development of autonomous vehicles, enhancing safety and reducing human errors in urban mobility. Sustainable transportation planning benefits from AI-driven optimization techniques.

Public transit systems integrate AI for route planning, demand forecasting, and fleet management, ensuring efficient resource allocation. Ride-sharing platforms leverage AI to match passengers with optimal routes, reducing overall vehicle emissions. Moreover, AI facilitates the adoption of electric and hybrid vehicles by optimizing charging infrastructure and energy consumption. AI- driven predictive analytics play a crucial role in urban mobility planning by analyzing historical and real-time data to enhance decision-making. Smart city initiatives incorporate AI to develop intelligent transport networks, promoting seamless connectivity between different transportation modes. Furthermore, AI enhances pedestrian and cyclist safety by identifying potential hazards and improving infrastructure planning. Despite its advantages, AI implementation in urban transportation presents challenges, including data privacy concerns, cybersecurity risks, and high deployment costs.