Systematic Evaluation of a Multi-Lane Green-Driving Algorithm in a Mixed Connected Environment
Bibliographic record
Abstract
Vehicles traveling under oscillated traffic have low energy efficiency and high air pollutant emissions. Green driving with the help of connected vehicles (CVs) attracts a lot of research effort to improve vehicle energy efficiency. However, it is very challenging to perform green driving on multi-lane freeways under a mixed connected environment. In the researchers’ previous work, one innovative green-driving algorithm was proposed to solve the multi-lane problem with only one CV. In this study, a systematic analysis of the algorithm is conducted to understand its benefits and limitations on smoothing traffic oscillations. The effect of the steady states and the number of lanes is also analyzed. In addition, the algorithm is extended to a more general scenario with multiple CVs. The extended system coordinates multiple CVs to form multiple moving bottlenecks to mitigate traffic oscillation more efficiently as well as providing more realistic instructions to CVs. The evaluation of the extended system concludes with the most effective strategies to control CVs to smooth oscillations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".