A REVIEW OF AI-POWERED APPROACHES IN SMART CITY IN TRAFFIC MANAGEMENT VIA IOT
DOI:
https://doi.org/10.22159/ijet.2025v13.57905Keywords:
Internet of Things, Smart cities, Traffic management, Artificial intelligence, Machine learningAbstract
The growing speed of urban time is a significant challenge to the effective and sustainable transportation system, which resulted in the introduction of Smart Traffic Control solutions on the basis of Internet of Things (IoT) technology to address traffic jams, road safety, and transportation efficiency. IoT and artificial intelligence (AI) integration has become an attractive and complementary method to revolutionize traffic management in the world as smart cities are implemented on a global scale. This paper provides an overview of the latest AI-driven methods for smart city frameworks that are IoT enabled. It lists various technological backgrounds that help with decision-making, such as deep learning, computer vision, predictive analytics, and machine learning. The article discusses AI-enabled advancements in real-time congestion control, traffic signal control, predicting of travel time, incident detection, and video-based vehicle analysis. It also shows that there are vital issues related to the implementation of AI in IoT-based traffic systems such as limitations of data quality, privacy and security risks, intensive calculations, interoperability, and the necessity of strong real-time processing. In general, the survey offers an in-depth outlook of the partnership between AI and IoT in the field of intelligent traffic management by integrating the current research trends, technical knowledge, and barriers to implementation to help researchers and urban planners to design the smart cities with resilient, adaptive, and data-driven transportation networks.
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