A Digital Twin Architecture for Intelligent Traffic Management Based on Artificial Intelligence

This work presents a concept for an innovative Digital Twin (DT) framework for urban traffic monitoring and management, tailored for the city of Singapore. The proposed architecture leverages real-time traffic and weather data integration, AI processing, and modular design to offer adaptive and versatile traffic insights. By incorporating live information from various sources and integrating real-time weather data, the framework enables proactive traffic management and enhances safety during adverse weather conditions. The paper discusses the implementation of the framework, and its potential impact on urban mobility, and suggests future directions for research and development to facilitate the framework’s implementation.