Eco-Speed Harmonization with Partially Connected and Automated Traffic at an Isolated Intersection
Bibliographic record
Abstract
This paper proposed an eco-speed harmonization method at intersections. It is able to reduce carbon emissions by controlling partially connected and automated traffic and signal timing. It has the following features: (i) traffic emission reduction enhancement at various demand levels; (ii) traffic emission achievement while improving the mobility of entire traffic at intersections; (iii) enhanced traffic emission reduction with the help of a small portion of connected and automated vehicles; and (iv) potential implementations in the near feature. To validate the effectiveness, the proposed method is evaluated against a state-of-the-art strategy. Sensitivity analysis is conducted under various demand levels and market penetration rates (MPRs) of connected and automated vehicles (CAVs). The result shows that the proposed method outperforms and has the benefits of traffic emission reduction, throughput improvement, and stop frequency reduction. The proposed method demonstrates consistent performance across all demand levels and CAVs’ MPR. The proposed approach can achieve a reduction in emissions ranging from 4% to 61%, an average increase in throughput of around 14.91%, and a decrease in the stop frequency of at least 26%. This provides the foundation for future CAVs-based eco-approaching strategies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".