AI-Powered Digital Twin for Urban Planning: A Scalable and Privacy-Preserving Implementation

Abstract

Urban digital twins (UDTs) are revolutionizing urban planning by providing virtual replicas of city systems for simulation and optimization. This paper introduces a novel implementation methodology for an AI-powered digital twin, integrating agentbased modeling (ABM), deep reinforcement learning (DRL), and federated learning (FL) to address scalability, privacy, and dynamic urban challenges such as traffic management and energy optimization. Unlike traditional UDTs that rely on centralized data and static models, this approach leverages real-time IoT data, decentralized learning, and participatory mechanisms to ensure adaptability and stakeholder engagement. A case study in a mid-sized city demonstrates the methodology’s ability to optimize traffic flow and incorporate citizen feedback. Experimental results show a 20% reduction in traffic congestion and a 30% improvement in planning efficiency compared to conventional methods. The proposed framework offers a scalable, privacy-preserving solution for sustainable urban development.