An AI-Based Digital Twin Framework for Intelligent Traffic Management in Singapore

Abstract

Urban centers worldwide grapple with the intricate challenge of traffic congestion, necessitating sophisticated solutions grounded in real-time data analytics. This paper presents a cutting-edge Digital Twin (DT) framework tailored for urban traffic management, with a focus on the context of Singapore's technologically advanced landscape. By seamlessly integrating live weather data and on-road camera information, the proposed framework offers insights into traffic dynamics, enabling adaptive decision-making. Leveraging a modular architecture and advanced artificial intelligence (AI) algorithms, the framework aims to optimize traffic flow, mitigate accidents, and ensure resilient commuting experiences, even amidst adverse weather conditions. Evaluation of individual components showcases promising performance metrics, albeit contingent upon data availability and user engagement. Future research endeavors will explore scalability, user-centric design enhancements, and the longitudinal efficacy of the proposed framework, positioning it as a novel solution for urban traffic management.