Abstract
Large-scale events such as sports competitions, concerts, and festivals impose severe and often unpredictable demands on urban traffic networks, leading to congestion, delays, and safety hazards. Traditional traffic management systems rely on historical data and static plans, which are insufficient for dynamic event-induced conditions. This paper proposes a digital twin-driven approach for real-time traffic management during large events, integrating Internet of Things (IoT) sensor networks, machine learning-based traffic prediction, and simulation-optimization. We develop a conceptual framework that synthesizes data from roadside units, connected vehicles, and event ticketing systems to create a living digital replica of the traffic environment. The framework employs pattern-aware regression (Okukubo et al., 2022) and deep learning models for short-term traffic state forecasting, and uses a multi-objective optimization module to generate adaptive signal timing and route guidance strategies. We validate the framework using a simulation case study of a major sporting event in a mid-sized city, leveraging real-world traffic data and event schedules. Results demonstrate that the digital twin reduces average travel time by 18% and intersection delays by 25% compared to baseline static management, while improving emergency vehicle access. The study highlights key challenges in data integration, model fidelity, and computational latency, and outlines a roadmap for practical deployment. Our work contributes to the emerging field of urban digital twins and offers actionable insights for city planners and event organizers.
Keywords
digital twin, traffic management, large-scale events, real-time simulation, machine learning, IoT sensors, adaptive control, urban mobility