## 1 Introduction
### Digital Twin Technology in Transportation Infrastructure  
Originally developed for product lifecycle management, DT technology has since evolved into a versatile tool with extensive applications across various sectors, including manufacturing, healthcare, and notably, transportation infrastructure. In recent years, advancements in this technology have facilitated more efficient and data-driven management of complex systems [4]. A DT is, in principle, a virtual twin of a physical object capable of interacting with real-time data to provide simulations and predictive models. This solution upgrades an integrated approach to the entire asset lifecycle management in an active, data-driven style for critical transportation infrastructure, such as roads, bridges, tunnels, and railways [5]. Compared to traditional transportation infrastructure management technologies, DT technology offers advantages such as real-time capabilities, digitization, and proactive features, enabling more efficient optimization of transportation system operations and bringing positive impacts to national economic and environmental sustainability [6; 7]. For instance, most traditional transportation infrastructure relies on routine inspections and manual repairs, which often fail to promptly address sudden failures. For critical structures like bridges and tunnels, DT technology can provide lifecycle data, leveraging historical data analysis and machine learning algorithms to identify potential safety hazards in real time and predict aging and damage trends [8; 9]. This allows for proactive maintenance and early estimation of repair needs, avoiding sudden shutdowns or severe failures. Not only does this extend the service life of transportation infrastructure, but it also further reduces maintenance costs. Additionally, DT technology for transportation infrastructure can provide multi-dimensional information queries and early warnings regarding road network operation status, bridge and road construction status, sudden safety incidents, and air visibility through cloud platforms, helping travelers save time on their journeys [10]. Undoubtedly, this significantly contributes to reducing energy and water consumption, raw material extraction, and greenhouse gas emissions [11]. Hitherto, most of the research on DT technology for transport infrastructure has focused on four key areas, including infrastructure modeling and simulation [12; 13; 14; 15; 16], real-time monitoring and management [17; 18; 19; 20], predictive maintenance [21; 22; 23], and safety management [24; 25; 26; 27]. Based on the literature summarized above, a schematic diagram of the urban transportation infrastructure digital twin platform architecture was developed and is presented in Figure 1. In terms of infrastructure modeling, over the years, 3D models of roads, bridges, tunnels, and railways that can simulate real conditions to support infrastructure design and planning optimization have been developed by researchers [12; 13; 14; 15; 16]. These models allow planners to predict traffic flows, identify bottlenecks, and assess the possible achievements of different construction and maintenance strategies [5; 12]. Equally important is the real-time monitoring and management of transportation systems using DTtechnology. There is increased utilization of sensor networks and the Internet of Things (IoT) to capture data on real-time traffic conditions, vehicle speeds, and the usage of roads [17; 18]. All this information is then integrated into digital models to continuously monitor the performance of transportation infrastructure and provide insights into areas that require maintenance or improvement. Advanced machine learning techniques in predictive analytics allow for proactive decision-making that may reduce downtime and improve overall system efficiency [19; 20]. Another key research area is that of predictive maintenance, whereby models of DTs are used to simulate the condition of diverse transportation assets.