Evaluating Emission Reduction Policies along an Urban Arterial Highway Using the AIMSUN Model
Bibliographic record
Abstract
The rapid growth of cities and the increasing traffic congestion have made vehicle emissions worse, especially in developing countries. Governments worldwide are now relying on regulations and policies to manage and reduce these emissions effectively. This change in approach towards emission control is also happening in developing nations. In this study, the effects of these policy measures are quantified using a calibrated integrated traffic and emission model (AIMSUN), and two hypothetical scenarios were analyzed: one scenario is where electric vehicles (EVs) replace traditional internal combustion EVs (ICEVs) in the study area and another scenario is assuming strict implementation of Euro 4/IV emission standards. The results showed that shifting towards a higher proportion of EVs leads to significant reductions in emissions but requires increased battery consumption, highlighting the trade‐off between reducing emissions and higher energy demand. Implementing Euro 4/IV standards could considerably reduce emissions, especially from motorcycles and trucks. It suggests that focusing on these categories with a phased implementation approach could bring significant environmental benefits. Policymakers in developing countries should adopt a rounded approach instead of implementing strict policies. It is crucial for them to carefully weigh the pros and cons of policy instruments before making any decisions. This study shows how traffic micro‐simulation modeling coupled with emission models can be used in evidence‐based decision‐making.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".