# Digital Twin Framework for Road Infrastructure Management
## 4 Digital Twin Framework for Road Infrastructure Management
### Proposed Digital Twin Framework for Road Infrastructure Management
#### 4.3.1 Data Acquisition and Transmission Layer  
The data acquisition and transmission layer collects real-time data from physical assets using various sensors and acquisition methods. These include IoT devices, embedded sensors within the infrastructure, and mobile sensors--such as point cloud scanners, laser scanners, and imaging devices--often deployed via versatile robots and UAVs, as well as through manual surveying and inspection efforts [38]. A critical component of DT is real-time data acquisition, which involves gathering raw data using IoT devices, such as sensors, video cameras, RFID systems, and QR codes [37]. These technologies are essential for connecting physical assets to their digital counterparts via the data processing and integration layer. The collected raw data are transmitted wirelessly in real time to a cloud-based database, where they are prepared for further processing, including modeling and analysis. Moreover, IoT-based connections facilitate the integration and transmission of real-time data from multiple sources, enabling more optimized processes.  
Road data can be broadly categorized into five groups: road asset data, asset management data, supplementary data, knowledge data, and other general data. Representative examples of road asset data and their classifications can be found in [30; 31]. Road assets are typically grouped into the following major categories: main facilities (e.g., bridges, tunnels, interchanges), geotechnical and drainage structures (e.g., gutters, retaining walls, cut slopes), safety facilities (e.g., median strips, guardrails, variable signs), additional facilities (e.g., noise barriers, speed humps, rest areas), and other related facilities.  
Furthermore, additional facilities deal with underground utilities located directly beneath or near roadways. These can include public utilities such as electricity, gas, water supply, sewage, and telecommunication cables. Being aware of their presence and exact locations is crucial for maintaining the safety of infrastructure during road maintenance. Additionally, other related facilities pertain to aspects such as land use, roadway area, and road occupancy. The interaction among these factors is significant for the effective operation of DTs in road management.  
Asset management data may include existing legacy data, manual inspection records, mobile surveying data, sensor outputs, surveying data, and other related information. Supplementary data refer to supporting documents and materials in any format relevant to road assets and their management. Knowledge data encompasses the existing information and expertise within a region or country that is essential for analyzing data and informing infrastructure-related decisions. Other general data include transportation-related information spanning the entire life cycle of infrastructure, enabling various forms of analysis and simulation. Location data of assets, along with their spatial and semantic relationships, are also acquired.  
Figure 7 illustrates these categories along with their subcategories. This visual representation is akin to feature modeling, which depicts domain knowledge and the relationships among system features [53]. It is important to note that these categories are not mandatory; rather, they serve as optional references when implementing DTs in the road industry. This approach is applicable to other semantic models discussed in this paper, as well as the proposed model.