AI-Driven Smart Cities: Improving Urban Infrastructure and Services
Bibliographic record
Abstract
This research explores the transformative role of artificial intelligence (AI) in shaping smarter cities, focusing on its applications across urban infrastructure, public services, and sustainability. AI-driven systems are revolutionizing how cities manage traffic, energy, waste, and public safety, leading to more efficient, responsive, and resilient urban environments. Key case studies, including Singapore, Barcelona, Toronto, and Pune, illustrate the diverse impacts of AI on improving urban mobility, reducing energy consumption, enhancing public safety, and optimizing resource management. However, the integration of AI into city planning and governance also raises important ethical considerations, particularly regarding data privacy, algorithmic bias, and equitable access to technology's benefits. For urban planners and policymakers, balancing innovation with these ethical concerns is essential to building public trust and ensuring that AI contributes positively to urban life. This research underscores the importance of transparent governance, ethical frameworks, and citizen engagement in the successful deployment of AI in smart cities. Ultimately, AI holds significant potential to enhance the liveability and sustainability of cities, but its success depends on how well its implementation is managed in line with broader social and ethical considerations
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".