## 8 Conclusions  
Digital Twin technology holds transformative potential for the management of transportation infrastructure, yet its current implementation remains in the early stages, particularly in large-scale urban systems. To date, most research and applications have focused on specific components of transportation networks, such as traffic management or infrastructure health monitoring. These efforts are largely isolated and have yet to provide a comprehensive, integrated solution for managing the entire lifecycle of transportation infrastructure. This review delves into the existing gaps in current research and explores a DT system for transportation infrastructure from a full lifecycle perspective, spanning all stages from planning and construction to ongoing operation and maintenance. Through an extensive review of the relevant literature, the key concepts and infrastructure of DT for transportation infrastructure were reviewed and summarized. Subsequently, from a full lifecycle perspective, a taxonomy was analyzed that categorizes DT systems based on functional scope, data integration methods, and application stages. This classification unifies fragmented research efforts by providing a structured approach to DT deployment, enabling stakeholders to identify domain-specific solutions while maintaining interoperability across lifecycle phases. Furthermore, a foundational framework for DT in transportation infrastructure is explored from a full lifecycle perspective, focusing on the \"4 Horizontal + 4 Vertical + N\" framework for urban transportation infrastructure. The four horizontal layers include the data layer, model layer, service layer, and application layer. The data layer is responsible for data collection and integration throughout the lifecycle, providing real-time updates to virtual models. The model layer creates dynamic virtual models to support prediction and simulation. The service layer translates model outputs into actionable decision support, while the application layer directly interacts with users, enabling simulation, monitoring, and optimization. The four vertical phases encompass Planning and Design, Construction Implementation, Operations Management, and Summary Optimization, covering all stages of the infrastructure lifecycle. Additionally, the \"Form-Condition-Mechanism-Tendency\" concept is investigated, which further refines the understanding of infrastructure performance through digital reconstruction, precise mapping, mechanistic deduction, and trend prediction. This framework provides a robust tool for the full lifecycle management of urban transportation infrastructure, supporting its efficient operation and sustainable development. Moreover, from the perspective of application stages, the current applications of DT technology in transportation infrastructure are summarized and analyzed. The analysis reveals that to achieve the full lifecycle application of DT technology, several challenges must be addressed, including data quality and integration, real-time processing requirements, model accuracy and updating, system interoperability, and data privacy and security. Future research priorities will focus on areas such as standardization, advanced analytics and ai, interoperability, policy and privacy considerations, and integration with emerging technologies. For instance, advanced analytical tools, including artificial intelligence and machine learning, can significantly enhance the capabilities of DT systems, enabling more accurate predictions of traffic patterns and infrastructure conditions. Addressing policy and privacy concerns is also crucial for the ethical use of DT and for gaining public trust in these technologies. The integration of emerging technologies such as blockchain and 5G networks can further enhance the functionality of DTs. Blockchain technology can provide robust data management, improving data reliability and transparency, while 5G technology can facilitate real-time communication, enhancing the responsiveness and reliability of DT systems.