Transportation infrastructure is central to economic development and the daily lives of citizens. However, rapid urbanization, increasing vehicle ownership, and growing concerns about sustainable development have significantly heightened the complexity of managing these systems. Although digital twin (DT) technology holds great promise, most current research focuses on specific areas, lacking a comprehensive framework that spans the entire lifecycle of transportation infrastructure, from planning and construction to operation and maintenance. The technical challenges of integrating different DT systems remain unclear, which to some extent limits the potential of DT technology in the management of transportation infrastructure. To address this gap, this review first summarizes the fundamental concepts and architectures involved in DT systems for transportation infrastructure, such as roads, bridges, tunnels, and hubs. From a lifecycle perspective, DT systems for transportation infrastructure are categorized based on functional scope, data integration methods, and application stages, and their key technologies and basic frameworks are outlined. Subsequently, the potential applications of DT in various lifecycle stages of transportation infrastructure—planning and construction, operation and maintenance, and decommissioning and renewal—are analyzed, and current research progress is reviewed and discussed. Finally, the challenges and future directions for achieving a full lifecycle DT system for transportation infrastructure, encompassing technical, operational, and ethical aspects, are discussed and summarized. The insights gained herein will be valuable for researchers, urban planners, engineers, and policymakers.

High-precision and rapid detection of apparent defects in subway tunnel linings is crucial for ensuring the structural integrity of tunnels and the safety of train operations. However, current methods often do not adequately account for the spatial characteristics of these defects and perform poorly in detecting and extracting small-scale defects, which limits the accuracy of detection and geometric parameter extraction. To address these challenges, this paper proposes an efficient algorithm for detecting and extracting apparent defects in subway tunnels. Firstly, YOLOv8 was selected as the foundational architecture due to its comprehensive performance. The coordinate attention module and Bottleneck Transformer 3 were then integrated into the model’s backbone to enhance the focus on defect-prone areas and improve the learning of feature relationships between defects and other infrastructure. Subsequently, a high-resolution detection layer was added to the model’s head to further improve sensitivity to subtle defects. Additionally, a low-quality crack dataset was created using an open access dataset, and transfer learning combined with Real-ESRGAN was employed to enhance the detail and resolution of fine cracks. The results of the field experiments demonstrate that the proposed model significantly improves detection accuracy in high-incidence areas and for small-scale defects, achieving a mean average precision (mAP) of 87% in detecting cracks, leakage, exfoliation, and related infrastructure defects. Furthermore, the crack enhancement techniques substantially improve the representation of fine-crack details, increasing feature extraction accuracy by a factor of four. The findings of this paper could provide crucial technical support for the automated operation and maintenance of metro tunnels.