Research on Speed Guidance Strategy based on multi-objective optimization in Connected Vehicle Environment
Bibliographic record
Abstract
Green Light Optimal Speed Advisory (GLOSA) is an intelligent transportation system technology that uses real-time information from traffic lights and real-time vehicle location and speed information to advise drivers on the optimal speed, so that it can reach the optimal speed before the next traffic light turns green. It aims to reduce vehicle energy consumption and traffic congestion while improving road safety. In this paper, a two-layer frame is proposed to optimize vehicle speed as to reduce vehicle energy consumption while vehicles pass through intersections without stopping. The upper frame is to calculate the passable time area of vehicles arriving at the intersection without stopping by predicting the queuing information at intersections. The lower frame is to calculate the energy consumption at each point in the passable time area when vehicles arrive at the intersection, and the Dijkstra algorithm is used to solve the path where vehicles pass through continuous intersections without stopping and have the least energy consumption. The simulation results show that, compared with the constant speed strategy through the intersection, the proposed multi-objective optimization strategy can reduce energy consumption by 0.7%, 2.6% and 9.8% under the condition of over-saturated, saturated and under-saturated traffic volumes, respectively. It is an effective vehicle speed guidance optimization strategy.
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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 | 0.000 | 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) | 0.001 | 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".