## 6 Current Applications of DTs in Transportation Infrastructure
### Urban Planning  
It is among the most transformative uses of DTs in transportation, in which the simulations of various infrastructure layouts are conducted to assess their potential effects on traffic flow, air quality, and noise pollution. DTs help urban planners make informed decisions based on evidence. For instance, Singapore has developed a city-wide DT necessary for simulating different urban development scenarios, with projected factors such as population growth, vehicle ownership, and demand for public transport [102]. It helps the planners to simulate and estimate the impact that could be initiated by policy changes, such as road expansions or the introduction of new public transport routes, before these changes are actually implemented. Certain scholars have presented decisions on the intelligent development of transportation infrastructure and made forecasts with regard to functional design and intelligent development, effectively integrating new media, aiming at providing references for smart development and construction with regard to subsequent transportation infrastructure in the smart city [103].  
Digital twins (DTs) in urban planning extend beyond conventional traffic flow simulations by enabling high-fidelity evaluations of environmental and socio-economic impacts. For instance, DTs simulate how new road configurations affect air quality across diverse urban zones, integrating multisource datasets, including real-time traffic patterns, meteorological conditions, and pollutant dispersion models. These simulations assist planners in designing sustainable urban layouts that prioritize public health [62]. High-fidelity simulations require granular datasets, such as high-resolution geographic information system (GIS) layers, demographic statistics, and transportation logs, selected based on their ability to capture spatial heterogeneity and dynamic interactions within urban ecosystems. To ensure accuracy, raw data undergo rigorous preprocessing: GIS data are georeferenced and normalized, traffic logs are cleaned to remove outliers, and demographic data are disaggregated to neighborhood-level resolution. Advanced analytical tools, such as com  
Figure 4: Architecture of the urban transportation infrastructure digital twin platform.  
putational fluid dynamics (CFD) for air quality modeling and machine learning algorithms (e.g., random forests for traffic pattern prediction), process these datasets. These tools are selected for their ability to manage non-linear relationships and adapt to urban complexity. However, the absence of standardized data formats and theoretical frameworks for DT modeling poses significant challenges. For example, merging physical sensor data (e.g., IoT-based air quality monitors) with socio-economic information requires novel data fusion techniques that are still underdeveloped [104]. Additionally, the computational intensity of high-fidelity simulations necessitates scalable cloud infrastructure and adaptive algorithms to optimize precision and processing efficiency. Addressing these gaps through unified data standards and interdisciplinary methodologies is critical for improving predictive accuracy and enabling holistic urban planning models [105].