Impact of artificial intelligence and internet of things technologies on smart cities and urban planning
Bibliographic record
Abstract
In the ongoing era of rapid developments worldwide, the world’s population is also increasing with a large sector of society desiring to move to urban areas in the hope of a better quality of life. Alongside, new technologies including the application of AI and IoT are also emerging which may help address current and future challenges of smart and sustainable studies through data-driven solutions. Smart cities have been and are being developed to improve the quality of life, boost service efficiency, increase safety and security for their residents, and achieve environmental and economic sustainability in the long run. This paper highlights how the application of artificial intelligence (AI) and Internet of Things (IoT) technologies can help urban planning, urban management, public safety, and service delivery in smart cities. A few examples have been included confirming that by using smart sensors and devices, smart cities can collect real-time data for smart parking, smart waste management, smart traffic control, smart charging, and many other functions, thus making the smart cities of tomorrow a bit smarter. The paper further underscores that AI and IOT can contribute not only to smart cities but also to urban planning in other metro cities and towns. The conclusion underlines the great potential of AI and IoT to create efficient, resilient, and livable urban environments in smart and sustainable cities. Along with the proper policies, governmental support, and collaborative efforts, cities worldwide can leverage AI and IoT technologies to tackle problems being faced in urbanization sustainably. The scope of further research is also included.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".