Revolutionizing Urban Mobility: A Systematic Review of AI, IoT, and Predictive Analytics in Adaptive Traffic Control Systems for Road Networks

Abstract

Urban mobility has undergone and continues to undergo a profound transformation driven by the convergence of artificial intelligence (AI), the Internet of Things (IoT), and predictive analytics in recent years. These technologies are redefining adaptive traffic control systems, enabling real-time decision-making and increasing the efficiency and safety of road networks. The main questions addressed in the review explore how the integration of advanced technologies such as IoT, AI in traffic systems, are useful in optimizing traffic flows, vehicle coordination and infrastructure adaptability in increasingly complex traffic environments. The integration of IoT-enabled devices and AI-based algorithms has been essential to enable data-driven approaches to urban traffic control. Predictive analytics improves emergency response mechanisms, improves traffic signal operations, and supports the deployment of autonomous and connected vehicles. Among the various methodologies evaluated, AI-based models combined with IoT sensors demonstrated superior performance, reducing average traffic delays by up to 30% and improving safety metrics in various urban environments. This systematic review underscores the transformative potential of integrating AI, IoT, and predictive analytics into urban traffic management, offering a blueprint for smarter, more sustainable urban transportation solutions.