Heterogeneity Aware Emission Macroscopic Fundamental Diagram (e-MFD)
Bibliographic record
Abstract
Transportation sector is one of the major producers of greenhouse gases which are responsible for climate change. Finding an appropriate emission estimation tool for large-scale networks is essential for developing efficient emission mitigation strategies. This paper presents an advanced version of the emission macroscopic fundamental diagram (e-MFD) which improves the stability and accuracy of the previous model. A bi-modal function is applied to separate free-flow and congested branches of the e-MFD. The accuracy of the proposed e-MFD is evaluated with both a synthetic grid network and a real-world city-level network. The study also assesses the model’s stability under directional traffic demands and road incidents. A comparison with the original e-MFD also verifies the superiority of the proposed model with higher accuracy. Standard deviation of density used in the proposed model to boost the performance. It is worth mentioning the standard deviation can be recorded with the existing hardware, such as loop detectors, and does not impose a considerable computational complexity. The proposed model can be employed for emission measurement in large-scale networks and hierarchical traffic control systems for more homogeneous congestion distribution and emission control.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 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".