Smart Urban Traffic Management: Leveraging Automatic Control and Intelligent Systems for Improved Safety in Commercial Vehicle Road Banning Operations

Urbanization presents formidable challenges to global urban transportation systems, accentuating safety concerns amidst burgeoning vehicular traffic. Commercial vehicles, owing to their prevalence and size, significantly contribute to congestion and accidents. Statistics from authoritative bodies such as Malaysia Highway Authority (LLM) underscore this severity, with large trucks notably implicated in a significant share of traffic fatalities. Asia, among rapid urbanization, grapples with heightened road safety issues, as emphasized by the World Health Organization (WHO). The alarming statistics reveal substantial involvement of commercial vehicles in traffic-related fatalities, necessitating urgent innovative interventions. In response, this research advocates for the Smart Urban Vehicular Traffic Management System, harnessing automatic control and intelligent systems integrated with Artificial Intelligence of Things (AIoT) in a cloud computing environment to boost safety and efficiency in commercial vehicle operations. Addressing challenges inherent in the traditional cloud computing paradigm, particularly with the increasing AIoT devices, we propose an edge-cloud architecture. Leveraging by the federated learning techniques, this architecture ensures privacy while optimizing service quality for AIoT devices essential to commercial vehicle management. Central to this proposal is the pursuit of scalability and adaptability, promising transformative impacts on urban traffic safety. Through meticulous analysis, this research underscores the imperative for innovative transportation management solutions, with proposed applications extending to the development of smart sensor devices for data-driven urban road traffic bans management, signalling a paradigm shift towards smarter cities.