## 6 Current Applications of DTs in Transportation Infrastructure
### Chapter Summary  
DT technology has emerged as a strong enabler in the field of transport infrastructure, with applications spilling over into strategic urban planning, real-time traffic management, asset maintenance, and even smart mobility and autonomous vehicle development. Case  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Region/City** & \multicolumn{2}{c}{**Focus**} & **Tech** & \multicolumn{2}{c}{**Achievements**} \\ \hline \multirow{3}{*}{Singapore [103]} & - & Real-time data on roads, transit, environment & IoT, simulation frameworks & - & Enhanced road and transit planning Reduced congestion \\  & - & Urban scenario simulation & & & \\ \hline \multirow{3}{*}{United Kingdom (e.g., London TfL) [112]} & - & Managing congestion and & \multirow{3}{*}{Sensors, cameras, dynamic control} & - & Lowered traffic delays \\  & - & motorway projects & & & - & Improved public transport \\  & - & Real-time traffic info & & & reliability \\ \hline \multirow{3}{*}{China [117]} & - & Bridge/tmunel health monitoring & \multirow{3}{*}{BIM, GIS, IoT, AI, cloud} & - & Early fault detection \\  & - & BIM-based lifecycle management & & & - & Extended asset lifespan (e.g., \\  & - & for rail & & & Chengdu Metro Line 18) \\ \hline \multirow{3}{*}{Germany [118]} & - & High-fidelity simulations (e.g., AC and wheel wear) & \multirow{3}{*}{GIS, user-oriented design} & - & Detailed simulation accuracy \\  & - & Socio-technical rail DT & & & - & Optimized rail operations and asset management \\ \hline \multirow{3}{*}{Australia [119]} & - & 3D dynamic displacement & \multirow{3}{*}{GIS, user-oriented design} & - & Successful in large-scale bridge \\  & - & measurement (no targets) & & & monitoring (Matagarup, \\  & - & Deep learning for damage detection & & & - \\  & - & & & - & Improved modal identification \\ \hline \multirow{3}{*}{United States [120; 121]} & - & Highway DT pilot projects (e.g., real-time sensor data, connected vehicles) & \multirow{3}{*}{IoT, AI, cloud computing, sensors} & - & Reduced congestion via adaptive signal control \\  & - & Integration with state DOTs & & & - & Enhanced safety and incident \\  & - & & & sensors & response (e.g., I-210 Pilot, \\  & - & Integration with state DOTs & & & Colorado I-70) \\ \hline \multirow{3}{*}{Netherlands [122; 123]} & - & Port of Rotterdam DT for traffic and & \multirow{3}{*}{IoT, big data analytics} & - & Enhanced port traffic prediction and \\  & - & Notational-level DT infrastructure initiatives & & & multimodal coordination \\ \hline \hline \end{tabular}
\end{table}
Table 3: Representative case studies of DT applications in transportation.  
studies from Singapore, London, China, Germany, and Australia show how DT technology can help improve decision-making, actuate traffic flow optimization, predict the maintenance of structures, and safely deploy autonomous driving systems. DTs have a significant effect on every aspect of transportation infrastructure, and their gradual infiltration into this industry is underway.  
There are clearly many hurdles that must be overcome to achieve the full potential of this technology. Issues of data accuracy, model complexity, multiple sources of data, and assurance of sensor data's reliability remain very real hurdles. Finally, standardized protocols and further research in data processing techniques are quite indispensable. Nevertheless, the promises of development and improvement in advanced DT technologies can further transform transportation infrastructures into more efficient, sustainable, and safer systems in the future.