Traffic Management with AI-Powered Vehicle Recognition: Implications and Strategies
Doan Van Hieu
Department of Electric Engineering, Bac Lieu University, 126C Cao Van Lau Street, Ward 3, Bac Lieu City, Bac Lieu Province, Vietnam
Nguyen Van Khanh
Department of Computer Science, Tra Vinh University, 114 Nguyen Thien Thuat Street, Ward 5, Tra Vinh City, Tra Vinh Province, Vietnam
Abstract
Traffic management and surveillance play crucial roles in contemporary urban planning and transportation systems. As urbanization continues to expand, effective traffic regulation becomes increasingly essential. This document examines how artificial intelligence (AI)-driven vehicle identification can revolutionize traffic monitoring and control. We investigate the deployment of advanced smart camera systems combined with AI algorithms, including deep learning and computer vision techniques, for the automatic real-time identification and classification of vehicles. Our research demonstrates the efficacy of AI-based vehicle recognition in improving traffic management. By analyzing data gathered from a network of smart cameras in-depth, we illustrate significant enhancements in traffic flow analysis, congestion detection, and incident handling. AI-powered systems provide unmatched precision, allowing for precise vehicle categorization, detection of irregularities, and adaptive signal control. Additionally, this paper addresses the ethical and privacy concerns linked to AI in traffic monitoring, discussing approaches to guarantee data security and transparency. It also underscores the regulatory environment and emerging industry standards governing the application of AI in traffic management. Our findings highlight the potential of AI-driven vehicle recognition as a potent tool for traffic engineers, urban planners, and policymakers. We conclude by emphasizing the transformative potential of this technology and its contribution to more efficient, safer, and environmentally friendly urban transportation systems.
