Digital Twins in Transportation Infrastructure: An Investigation of the Key Enabling Technologies, Applications, and Challenges

Transportation infrastructure constitutes a significant part of civil infrastructure, such as bridges, tunnels, and roads, enormously promoting economic development. Transportation infrastructure is subjected to a high volume of traffic, damage, and component deterioration, as well as disaster events during the service period. Monitoring and management of the transportation infrastructure is always a critical task. Recently, digital twin (DT) is an emerging topic for transportation infrastructure management, with the advancement of artificial intelligence, Internet of Things, big data, and other smart technologies. Herein, DT aims to build a virtual counterpart of the physical infrastructure that is continually updated with its performance, maintenance, and health status. However, research in DT for transportation infrastructure mainly focused on infrastructure management (e.g., traffic state prediction and passenger flow assessment) and ignored the health status of the infrastructure itself. Challenges and associated opportunities exist in the adoption of DT for transportation infrastructure, such as balancing model fidelity and computation efficiency. Thus, it is necessary to investigate the key enabling technologies of DT in transportation infrastructure and provide a comprehensive reference for ongoing and future research. In this paper, a systematic investigation to identify the development of DTs for transportation infrastructure is presented. The paper starts by explaining the definition of DT and highlighting the various characteristics of DT. Next, the key enabling technologies and applications of DT for transportation infrastructure are discussed. Finally, based on the current development status of DT, challenges and open research are discussed along with their potential solutions.