Assessment of the Traffic Enforcement Strategies Impact on Emission Reduction and Air Quality
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The World Health Organization (WHO) reported that globally 3.7 million deaths were attributable to ambient air pollution (AAP) in 2012. Traffic congestion is one of the significant sources of air pollutants Intelligent Transportation Systems (ITS) are advanced technologies that have been used widely in large cities. They have a potential impact on reducing traffic congestion and then improving environmental quality. Many countries have targeted urban policy traffic enforcement strategies that are ITS-based on improving traffic emission and air quality. Because each strategy has a different impact level, the strategy that positively impacts location and traffic conditions might negatively impact under different conditions. Also, the authorities that take the decision which strategies could be implemented. Therefore, this paper aims to evaluate the potential impact of traffic enforcement strategies on reducing traffic emissions and improving air quality. In our study, three typical traffic enforcement strategies were evaluated: a traffic management regulation for speed limit changes, route changing, and fleet composition changes. The impact of these strategies on air quality was evaluated through evaluating the traffic air quality changes brought by these strategies against a baseline (Base Case) scenario. The results indicate that the impact of these strategies on increasing environmental quality is not always positive. The reduction of CO was the highest in the speed restriction scenario (25.6%) than other scenarios. While reducing the reduction of PM10 was less in speed restriction scenario (25.6%) than other scenarios. The findings can help the decision makers implement the best strategy to reduce traffic emission under different situations.
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.
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 it