Priority based Algorithm for Traffic Intersections Streaming Using VANET
Bibliographic record
Abstract
Intersections play an important role in traffic management. Traditional inte rsecti on management systems do not offer an optimal solution. Vehicular Ad-Hoc Networks (VANETs) offer communications among vehicles which allow the implementation of intelligent intersection management. Num erous techniques have been proposed in the li terature to regulate traffic at intersection intelligently. However, these research works do not provide a general solution based on user-defined priorities and vehicles density. In this paper, we propose a nove 1 priority based algorithm to man age intersection traffic us ing vehicle-to-vehicle communication and coalitions are formed among vehicles based on their priority. Our algorithm, let the vehicl es with higher priority pass the intersection before the lower priority vehicles. Simulation results showed that our algorithm improved throughput by 21.6% and travel time by 40.24% when compared with the traditional-based algorithm.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".