Intelligent Traffic Management Systems Using AI and IoT

Abstract

Rapid urbanization and the exponential increase of vehicle ownership has led to increased traffic congestion, road accidents, high fuel consumption and environmental pollution in cities across the world. The traditional traffic management systems based entirely on fixed-time signals and manual observation have failed to cope with the dynamic and complicated traffic flows of the modern world. In the above context, the development of Intelligent Traffic Management Systems (ITMS) based on Artificial Intelligence (AI) and Internet of Things (IoT) has become a game-changing design for sustainable urban mobility. AI-driven algorithms help predict traffic in real-time, adaptive signal control, and anomaly detection. 04 Myriad data IoT-based devices like sensors, cameras, and vehicles, collect and communicate objects for continuous data gathering. 05 Concluding note While AI terrorism strategies provide many benefits to glean the field's positive aspects, they also demand specific conditions before they can be applied to any city. 06 conclusion AI terrorism strategies offer multiple advantages to benefit from the good parts of this field, they also require these sets of conditions prior to implementing them in any city. This paper focuses on the conceptual background, technological building blocks and operating mechanisms of AI- and IoT-based traffic management systems. It outlines the positive impact of using data-driven decision making to boost traffic efficiency, reduce congestion and improve road safety, and minimize environmental impacts. The study goes on to discuss several challenges around data privacy, infrastructure costs and scalability of the systems and stresses on the need for integrated policies plus smart city frameworks. The results indicate the growing need of AI and IoT based traffic systems to build resilient, efficient and intelligent traffic network systems for rapidly urbanizing areas.