Assessment of the Traffic Enforcement Strategies Impact on Emission Reduction and Air Quality
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.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".