Digital twin (DT) has attracted much attention from the transportation community over the past 6 years. Combining the DT with intelligent transportation system (ITS) forms a digital twin intelligent transportation system (DT‐ITS), which stands as one of the most effective solutions for addressing current complex traffic problems. Due to the rapid advancements in this field and a lack of recent literature reviews, this paper first reviews relevant literature on DT‐ITS architecture design, to comprehend its core structure, methods, potential services and stakeholders, and implementation challenges, and then discusses DT‐ITS core considerations, aiming to provide a general configuration model of DT‐ITS for future development. Second, this paper focuses on reviewing the existing progress of DT‐ITS services within the 32 categories of ITS services, adopting the service‐centred point of view, to explore the potential DT‐ITS services, proposed delivery methods, challenges, and opportunities for various stakeholders. Third, key enabling technologies supporting DT‐ITS are reviewed and discussed, such as data fusion, cooperative perception, multi‐access edge computing (MEC) (including computing offloading and service caching), federated learning, edge‐cloud collaboration, secure and efficient communication (including Blockchain [BC], 5G), virtual modelling, and eXtended reality (XR). Finally, the paper identifies development trends and provides recommendations for future advancements.

The elimination of mixed errors is a key preprocessing technology for the area of digital elevation model data analysis, which is important for further applying data. We associated group sparsity with the low-rank uniqueness of local transformations of mixing errors to effectively remove mixing errors in data from Shuttle Radar Topography Mission 1 (SRTM 1) based on the sparseness of low-rank groups. First, the stripe-error structure that appeared globally in multiple directions was able to be better represented locally using group-sparse regularization and the uniqueness of the data in the low-rank direction of the local range and using variational ideas to constrain the gradient direction of the data to avoid redundant elimination. Second, the nonlocal self-similarity of the weighted kernel norm was used to remove random noise. Finally, the proposed model for eliminating mixed errors was solved using an algorithm based on the multiplier method of alternating direction. Experiments using simulated and real data found that the proposed low-rank group-sparse method (LRGS) eliminated mixed errors in both visual and quantitative evaluations better than the most recent processing methods and existing dataset products.