A comprehensive review of Digital Twin technologies in smart cities
Bibliographic record
Abstract
As urbanization accelerates globally, the need for smarter, more sustainable cities has become imperative. This review article delves into the realm of Digital Twin (DT) technologies and their role in shaping the future of urban development. By exploring the convergence of DT technologies and smart cities, this article offers a comprehensive analysis of how these technologies are driving the Industry 4.0 (I4.0) revolution. Through an extensive literature review, we examine the pivotal role of DT technologies in diverse domains such as healthcare, wellness, security, safety, transportation, energy, mobility, and communications. Furthermore, the review explores the enabling technologies behind DTs, including Internet of Things (IoT)-based, Machine Learning (ML)-based, Cyber–physical Systems (CPSs)-based, and blockchain technology-based, to name a few. Practical applications of DT technologies are also examined through reviews of case studies across transport, water management and automotive technology, highlighting their transformative impact on smart city development. Lastly, this article addresses key DT research challenges and outlines future directions to unlock the full potential of DT technologies in building safe and sustainable cities. • Role of Digital Twins in smart city development is reviewed. • Enabling Digital Twin technologies in smart cities are presented. • Technologies include Machine Learning, IoT, Cyber–physical Systems, blockchain. • Digital Twin applications in smart cities across multiple domains are discussed. • Digital Twins case studies on transport, water and driving are reviewed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".