Information Modeling for Data-Driven Digital Twin Simulation: Insights from Case Studies of Port Logistics and Urban Traffic Systems

Digital twins, which enable the creation of virtual replicas of physical entities to simulate temporal changes and forecast future scenarios, have become increasingly vital in sectors such as logistics and traffic management. The deployment of digital twins can significantly enhance the efficiency of maritime operations and cargo handling, while also facilitating the strategic planning of road networks through the optimization of traffic flow and reduction of congestion. Recent advancements in international standards, particularly ISO 23247, have set forth comprehensive guidelines for the reference architecture, digital representation, and data exchange protocols of digital twins within the manufacturing sector. In this context, our study seeks to extend these standards to include applications in port logistics and traffic systems. This paper will offer practical guidelines for each specified domain through detailed case studies. Our approach entails a systematic method in which the necessary information for simulations is categorized. This is followed by a clear definition of the operational mechanisms of these simulations, culminating in the implementation of the simulation models. These models are crafted to optimize the scheduling processes for ships and cargo equipment, as well as to improve the management of traffic flow.