INDUSTRIAL INTELLIGENCE FOR SMART CITIES: THE ROLE OF AI AND IOT IN TRANSFORMING URBAN MOBILITY AND INFRASTRUCTURE

Abstract

This review synthesizes research on AI and IoT in urban mobility, focusing on traffic management, public transportation systems, and autonomous vehicles to address escalating urban congestion, environmental impact, and mobility demands. This review aimed to evaluate AI and IoT applications in traffic flow optimization, benchmark integration in public transit, identify autonomous vehicle frameworks, compare predictive models and sensor networks, and analyze adoption challenges. A systematic analysis of global empirical, simulation, and theoretical studies was conducted, emphasizing technological convergence, performance outcomes, data utilization, and barriers. The findings reveal that AI-driven predictive models combined with IoT sensor networks significantly improve traffic efficiency and reduce emissions, whereas AI-IoT integration enhances public transit reliability through predictive maintenance and dynamic scheduling. Autonomous vehicles, supported by IoT-enabled communication and AI decision-making, demonstrate the potential for safety and sustainability gains but face regulatory, infrastructural, and acceptance challenges. Advanced machine learning techniques optimize real-time data analytics but encounter scalability and explainability limitations. Collectively, these findings underscore the transformative potential of AI-IoT in urban mobility, contingent on addressing privacy, infrastructure, and social factors. The synthesis highlights the need for interdisciplinary approaches to advance scalable, secure, and user-centered AI-IoT urban mobility solutions that inform future research and practical implementations.