TOWARDS A DIGITAL TWIN FRAMEWORK FOR REGULATING URBAN VEHICLE TRAFFIC USING SMART TECHNOLOGIES

Аннотация

Urban traffic congestion remains a significant global challenge, hindering mobility, sustainability, and economic growth. With traditional traffic management systems struggling to adapt to dynamic conditions, there is a critical need for innovative solutions leveraging smart technologies. This paper proposes and outlines a conceptual digital twin framework to regulate urban vehicle traffic. The framework is designed with a focus on real-time calibration and predictive control, leveraging Internet of Things (IoT), 5G, and vehicle-to-everything (V2X) communication for data integration. By envisioning the integration of mathematical models and distributed software architectures, this framework aims to enhance traffic flow while reducing computational complexity. Crucially, it is conceived to address the challenge of maintaining model accuracy even in sparse-data environments, thereby laying the groundwork for scalable smart urbanmobility solutions. Index Terms: Digital Twin, Urban Traffic Management, Smart Technologies, Real-Time Calibration, Extended Kalman Filter, Model Predictive Control, Simulation of Urban MObility (SUMO), Internet ofThings (IoT), 5GV2X Communication, Edge-Cloud Computing I. INTRODUCTION Urban traffic congestion remains a widespread and pressing issue globally, generating substantial economic, environmental, and social burdens for cities. According to estimates by the World Bank, congestion can lead to economic losses equivalent to up to 5% of a nation's GDP annually [13], primarily due to diminished productivity, increased fuel consumption, and delays in transportation and logistics. From an environmental perspective, stationary and slow- moving vehicles significantly contribute to greenhouse gas emissions, with urban transportation responsible for approximately 25% of global CO₂ emissions originating from fossil fuel use [14]. Socially, prolonged travel delays negatively affect quality of life, heighten stress levels, and constrain access to essential services and opportunities. Conventional traffic management systems, including fixed-time signal controllers and semi-adaptive schemes such as the Sydney Coordinated Adaptive Traffic System (SCATS), typically operate on predefined schedules or page1590 INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE ISSN:2692-5206,ImpactFactor:12,23 AmericanAcademicpublishers,volume05,issue06,2025 Journal: https://www.academicpublishers.org/journals/index.php/ijai offer limited responsiveness to real-time traffic variations [1]. As a result, they often fall short in managing dynamic and unpredictable traffic events, such as accidents, peak-hour congestion, or spontaneous disruptions caused by construction or public gatherings. These limitations, coupled with the underutilization of real-time data, lead to persistent inefficiencies in urban traffic flow. In the context of accelerating urbanization and increasing vehicle ownership, the imperative for intelligent, adaptive traffic management systems has become more pronounced toensure resilient and sustainableurban mobility. Advancements in smart technologies—such as the Internet of Things (IoT), 5G connectivity, and vehicle-to-everything (V2X) communication—present transformative potential for addressing the complexities of urban traffic management [2], [9]. IoT-enabled devices, including inductive loop detectors, intelligent surveillance cameras, and environmental monitoring sensors, generate real-time data on traffic flow, vehicle speeds, and roadway conditions, facilitating detailed and continuous observation of traffic dynamics. The deployment of 5G networks, characterized by ultra-low latency and high data throughput, enhances the speed and reliability of data transmission between vehicles, roadside units, and central traffic control platforms, thereby enabling latency-sensitive operations [3]. V2X technologies—which encompass both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications—provide critical, real-time information regarding vehicle location, movement patterns, and driving behaviors, supporting coordinated and predictive traffic control strategies. Collectively, these technologies establish a data-rich framework conducive to adaptive and intelligent traffic regulation. Nonetheless, the integration of such diverse and heterogeneous data sources remains a significant challenge, particularly in urban areas with underdeveloped or uneven sensor infrastructure. Many current systems presuppose the availability of dense, high-quality data, which restricts their scalability and effectiveness in varied and resource-constrained urbanenvironments. A digital twin, defined as a real-time virtual replica of a physical system, provides a powerful framework for advancing urban traffic management. By modeling a city’s road network, including intersections, vehicles, traffic signals, and external factors like weather or pedestrian flows, a digital twin enables real-time simulation, predicti