## 3 Conceptual Overview of DTs in Transportation Infrastructure
### Six-Dimensional Structure of the DT of Transportation Infrastructure  
The DT of transportation infrastructure typically features a six-dimensional structure, as shown in Figure 2. It integrates physical entities and virtual models through a DT engine to enable real-time data interaction, simulation optimization, and predictive analysis. It represents the physical transportation assets with a very high degree of accuracy in an interactive way. The system allows for the function of real-time data interaction, simulation optimization, and prediction through the DT engine. It covers all aspects of transportation infrastructure, ranging from planning, design, and construction to operation, maintenance, and renewal. In this way, the system provides powerful technical support for the sustainable, intelligent, and efficient development of urban transportation. At the center could be the physical entity, the backbone of transportation infrastructure in the DT. These include all the transportation-related facilities in the real world: roads, bridges, tunnels, vehicles, bus stations, traffic signs, and more. Each physical entity has specific attributes. For example, a bridge has specific materials, load-bearing capacities, and special lifespan characteristics, while a road differs in width, materials, traffic flow, and maintenance history. Vehicles have model types, speeds, passenger capacity, and emission standards. In fact, these are not just static structures but active dynamic elements closely connected to the life and economic vibrancy of the city through essential data input into the digital model. The DT can thus provide a reflection of the real world in real time using sensors, including cameras, infrared sensors, GPS, accelerometers, and temperature sensors. The virtual model constitutes the core of the DT, hence providing a very precise DT of physical infrastructure. The virtual model emulates the behaviors, attributes, and interactions of physical entities that enable dynamic simulations of infrastructure performance. The virtual model is not just a simple static 3D representation but encompasses dynamic behavior, mechanical performance, material properties, and environmental interactions of physical entities. This integration enables simulations ranging from traffic flow predictions and congestion forecasting for specific places and times to the performance and reaction of structures under certain loads, as well as long-term maintenance needs. Furthermore, the virtual model is open to connections with other digital models, data sources, and applications, interlinking to constitute an overall digital ecosystem. Data from twins are basically the lifeblood of this DT system, representing the state, behavior, and environmental context of each physical entity, as well as predictions and feedback that fundamentally drive the whole system. The data also include real-time, continuous input, such as the status of traffic flow, vehicle behavior, and environmental data, which enable the DT to reflect the real world with high precision. Twin data are diverse, heterogeneous, and large-scale in nature since it comes from different sensors and devices, each with different formats and structures. Advanced data processing and analysis capabilities are required to manage and extract meaningful insights from this data, ensuring that the system functions effectively. The DT engine serves as the driving force for the whole system. Essentially, it processes, analyzes, and interprets data from the twins to realize the real-time synchronous operation  
Figure 2: Six-dimensional structure of DT of transportation infrastructure. of physical entities with virtual models. Simulations, traffic flow optimizations, predictions about future conditions, and intelligent insights will be run on this engine.