<p>Marine traffic safety and efficiency are critical concerns, especially with the increasing complexity of shipping environments. While automation holds promise for improving these aspects, autonomous ships’ ability to navigate complex areas remains uncertain. The intricate characteristics of marine traffic, including emergency behaviour, harsh environmental conditions, traffic crossing situations, and the potential for human error on traditional ships, pose significant challenges for deploying autonomous ships. Therefore, this study explores the potential Digital Twin (DT) applications in marine traffic environments, aiming to enable safe and efficient Autonomous Ship (AS) navigation. The current rules, navigation, and path-following systems of AS are reviewed to assess their competency. At the same time, macroscopic traffic analysis models are outlined to assess the possibilities of extending navigation systems for operating in complex areas. DT is an emerging technology in the maritime industry, driven by elements of Industry 4.0 such as Artificial Intelligence (AI), machine learning, and big data. It can potentially accelerate the development of AS through the integration of macroscopic traffic information, enhancing operational safety and efficiency, as well as providing decarbonisation opportunities in the marine environment. Hence, DT is likely to facilitate a highly automated and environmentally sustainable maritime transport network, contributing to the realization of smart mobility.</p>