ETRI Journal Special Issue on Smart City Technologies and Services Based on Artificial Intelligence for Digital Twin Applications

The rapid growth of smart cities presents opportunities and challenges. In 2022, the ETRI journal organized a special issue on smart cities and their applications with a focus on the incorporation of high-level intelligence into urban environments and the exploration of its potential economic impact on associated industrial chains. Although the data collected from sensors offer invaluable insights, managing and extracting actionable intelligence from vast datasets remains a complex task. The convergence of artificial intelligence (AI) and digital twins has emerged as a promising solution to address the current challenges and unlock the full potential of smart city development.
AI technologies allow the integration and analysis of diverse data sources, enabling efficient storage, security, and knowledge extraction. This can lead to enhanced operational efficiency, reduced energy consumption, and overall sustainability in smart cities. It can also strengthen communication and security within smart cities, supporting data-driven decision-making while preserving individual privacy.
Digital twins can be used to create virtual replicas of real-world cities, thereby enabling scenario simulations and problem predictions. Automated feature extraction and accurate model construction can be achieved by incorporating AI models for cost reduction, resource optimization, and improved maintenance strategies. Additionally, digital twins can be integrated with metaverses to create immersive and interactive virtual spaces for citizen engagement and the design of future cities.
This special issue seeks innovative research that explores the synergy between AI and digital twins to empower smart and sustainable urban development. We encourage professionals from academia, industry, and the government to submit original high-quality research papers for this special issue. The topics of interest include (but are not limited) to the following:


· Real-time data analytics for traffic management, energy efficiency, and public safety
· Big-data management and cloud-computing solutions for smart cities
· Machine learning for anomaly detection and predictive maintenance of urban infrastructures
· Data privacy and security in smart-city data management
· AI-powered communication networks for smart city infrastructure (5G and Internet of Things)
· AI-based threat-detection and incident-response systems
· Smart traffic management systems using AI and machine learning
· Public safety applications using AI-powered video analytics
· Privacy-preserving data aggregation and anonymization techniques
· Blockchain for secure and transparent data sharing in smart cities
· Development of digital twins with a focus on city infrastructure and assets for simulation and optimization
· AI and machine learning to create realistic and dynamic digital twins
· Machine learning for urban mobility modeling and traffic prediction
· Social network analysis to understand citizen behavior and sentiments
· AI for energy demand forecasting and energy grid optimization
· Generative adversarial networks (GANs) for the creation of synthetic city data
· Deep reinforcement learning for optimizing resource allocation and decision-making in digital twins
· Transfer learning for adapting pretrained deep-learning models to specific smart city applications
· Explainable AI (XAI) techniques for the interpretation of the behavior of deep-learning models in digital twins
· Applications of digital twins in urban planning, resource management, and disaster preparedness

Important Dates (tentative)
Paper submission: January 10, 2025
First decision: March 20, 2025
Final decision: May 20, 2025
Publication: August 20, 2025

Paper Submission
Papers should be submitted at https://mc.manuscriptcentral.com/etrij and should adhere to the journal’s Author Guidelines.

Guest Editors
Prof. Jin Seek Choi, Hanyang University, Republic of Korea, jinseek@hanyang.ac.kr
Prof. Ming-Ching Chang, University of Albany-Suny, USA, mchang2@albany.edu
Prof. Byoung Chul Ko, Keimyung University, Republic of Korea, niceko@kmu.ac.kr
Prof. Jong Taek Lee, Kyungpook National University, Republic of Korea, jongtaeklee@knu.ac.kr
Prof. Jo Woon Chong, Texas Tech University, USA, J.Chong@ttu.edu

The Editorial Office can be contacted at etrij@etri.re.kr.