Empirical Study and Analysis of the Impact of Traffic Flow Control at Road Intersections on Vehicle Energy Consumption
Bibliographic record
Abstract
In modern society, vehicles have become an indispensable means of transportation to ensure people's travel and the circulation of social production materials and living materials. However, while bringing us convenience in life, with the increasing number of vehicles, the corresponding energy consumption and exhaust emission problems have also caused a lot of social wealth loss. Therefore, how to effectively improve the energy efficiency of vehicles to achieve the goal of energy-saving and emission reduction is one of the focuses of current academic and industrial circles. Different from the industrial sector, which mainly achieves energy saving and emission reduction by improving the mechanical performance of vehicles [such as increasing the thermal efficiency of internal combustion engines (ICEs)] or introducing new energy vehicles (such as electric vehicles), we have more choices in the academic world. Among them, through effective traffic signal control, the energy consumption of the vehicle can be improved by achieving a uniform speed of the vehicle as much as possible. We believe that the advantage of this method is that it can improve the energy efficiency of the vehicle within the system without updating the vehicle. In this article, we will prove this assertion and compare some state-of-the-art approaches through the form of an empirical study.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".