## 3 Integrating DT in CPS
### Smart Transportation  
The smart transportation system can be regarded as a CPS, which provides a variety of services for the transportation domain, including traffic management control. Some applications in smart transportation systems include toll payment, vehicle operation, emergency, vehicle control and safety management, and maintenance and construction management [72; 73; 6]. Since the smart transportation system requires real-time data transmission and analysis capabilities to implement time-sensitive services such as autonomous vehicles, it is difficult for traditional models to complete model training under massive data within a short time. It is additionally challenging for traditional smart transportation systems to complete the collection and aggregation of all sensor data within a short time. In this case, the DT model can use high performance cloud/edge servers in the digital system to conduct data aggregation and model training so that real-time processing and transmission can be achieved. Based on the architecture of the DT that we have discussed, DT in the transportation system consists of four layers. The object layer contains the components of the infrastructure and participants (e.g., drivers), including sensors on the vehicle, traffic cameras, and other traffic sensors. The communication layer uses a variety of gateways to process sensor data from the object layer, and handle data transmission between the object and application layers. The application layer maps the gateway from the communication layer to realize data transmission between the communication layer and the application layer. Based on the data and sensor information from the gateway, the DT can use ML/DL technology to train the model and store it in data storage. The end-user layer can navigate based on the data processed by the application layer, assist the driving of autonomous vehicles, and cooperate with smart cities and smart grids to reduce transportation costs, reduce energy consumption, and make the charging process of electric vehicles more efficient. There have been some research efforts on DT-driven smart transportation. For example, [PERSON] et al. [74] proposed a scheme for DT-driven smart transportation that attempts to optimize traffic flows in urban areas. The proposed smart transportation system includes an AI model, a digital replica of the road network, and various traffic control and management services. The system obtains data from the object layer, including traffic lights, sensors, and cameras. After the smart transportation system processes the data, it will build an application at the end-user layer based on the collected data, which is called the city service information system. Then, operators can manage the entire system at a high level based on the end user's application. Additionally, [PERSON] et al. [75] proposed a DT-based adaptive traffic signal control (ATSC) to reduce the waiting time at intersections, thereby improving the driver's driving experience. In that study, the Urban Traffic Simulation (SUMO) was used as the simulation platform. Additionally, [PERSON] et al. [76] designed a DT-based verification platform to perform verification between different metro systems. The object domain includes operation center; trackside and train information is collected, and data is transmitted to the DT system of the application layer through various communication protocols, including 4G, 5G, Ethernet, and NB-IoT. The DT platform uses ML/DL, data fusion, and data modeling to analyze all data and assist the end-user level in scheduling, train monitoring, pedestrian monitoring, etc. [PERSON] et al. [77] proposed a DT structure combined with blockchain to enable DT-based intelligent transportation systems to achieve interoperability, identity verification, distributed ML/decision-making, and scalability and robustness. Likewise, [PERSON] et al. [78] demonstrated a DT-based 3D collaborative vehicle infrastructure system (CVIS) to visualize real-time road data and use LIDAR as road sensors.