Advancements in Artificial Intelligence and RFID for Enhancing Smart City Traffic Monitoring

Abstract

This study explores the advancement and deployment of sophisticated traffic monitoring systems within smart city frameworks, utilizing cutting-edge technologies. It delves into the synergistic use of RFID, artificial intelligence (AI), and machine learning (ML) to enhance traffic management capabilities and predictive analytics. Initially, the research underscores the critical nature of traffic monitoring in urban settings and situates it within the expansive realm of smart city endeavors. A thorough review of existing scholarly articles on smart city infrastructure is conducted, particularly emphasizing the contributions of RFID, AI, and ML to the efficacy of traffic management systems. The paper describes a comprehensive architectural framework that amalgamates these technologies to facilitate robust data acquisition, transmission, and analytical processes. Further, the paper illuminates the advantages of employing RFID for vehicle identification alongside the diverse implementations of AI and ML algorithms for traffic predictions, vehicle classification, anomaly detection, and system optimization. The concluding sections summarize key insights, underscore the study's contributions, and outline prospective avenues for fortifying and expanding traffic monitoring systems in smart cities. The integration of these technologies plays a pivotal role not only in traffic management but also in enhancing communication, transportation efficiency, healthcare services, environmental sustainability, and energy management within smart cities.