A Novel VANET-Assisted Traffic Control for Supporting Vehicular Cloud Computing
Bibliographic record
Abstract
Vehicular Ad hoc Networks (VANETs) allow for vehicle-to-vehicle and vehicle-to-infrastructure communications using wireless local area network technologies. The distinctive features of their candidate applications (e.g., collision warning and local traffic information for drivers), resources (e.g., computational sources), and their ability to collect various data from their environment (e.g., vehicular traffic flow patterns) make VANETs a rich resource for information and resources. In this paper, we propose a new methodology to use VANETs to optimize signal control at traffic intersections as well as to create Vehicular Cloud (VC) computing environments. We theoretically analyze the traffic flow patterns in a given road intersection by using the diffusion approximation model. We calculate the probability of clearing the intersection and demonstrate the effect of the traffic patterns on the optimal choice of the traffic signal control parameters. Then, we employ our theoretical analysis to propose a potential solution to construct VANET-assisted VCs. Experimental results verify the correctness of our analysis.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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".