Advancing Urban Intelligence Through Digital Twins in Smart Cities
Bibliographic record
Abstract
The rapid urbanization of modern cities necessitates intelligent solutions for efficient resource management, sustainability, and improved quality of life. Smart cities leverage technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data analytics to optimize urban infrastructure and services. A key innovation in this transformation is the integration of digital twins dynamic virtual models that replicate physical urban environments and individual behaviors in real time. This paper explores the role of digital twins in smart cities, highlighting their applications in urban planning, transportation, healthcare, and environmental monitoring. The study also examines the interaction between digital twins of cities and individuals, enabling hyper-personalized services and data-driven governance. While digital twins offer significant benefits, their implementation raises challenges related to data privacy, cybersecurity, and ethical considerations. Addressing these issues is critical for fostering sustainable and inclusive urban ecosystems. This paper concludes with future research directions, emphasizing the need for robust AI-driven recommendation systems, secure data frameworks, and policy measures to enhance the effectiveness of digital twins in smart cities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".