Comprehensive Review of AI, IoT, and ML in Enhancing Urban Mobility and Reducing Carbon Footprints
Bibliographic record
Abstract
The rise in vehicle ownership and urbanization, which exacerbates traffic congestion and carbon emissions, are significant barriers to sustainable urban development. This study investigates how artificial intelligence (AI), machine learning (ML), and the internet of things (IoT) could enhance urban transport systems, reduce traffic, and lower urban regions' carbon footprints. The study examines how urban infrastructures can be made more efficient and flexible through the use of technologies including demand-responsive transport systems, integrated mobility solutions, predictive maintenance, and personalized trip planning. A recent study of educators, students, and other participants at Canadian University Dubai (CUD) found that people are becoming increasingly accepting of AI-driven solutions that reduce fuel consumption and travel time. These technologies have promise even if there are still challenges to be solved, such as the requirement for ongoing validation in dynamic urban contexts and the reliance on historical data. The study concludes with recommendations for integrating AI, IoT, and ML to develop responsive urban mobility solutions and emphasizes the importance of practical implementations to realize these benefits.
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 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".