Bibliographic record
Abstract
Considerable attention has been assigned to Vehicular Cloud towards identifying methods to utilize under-used, available computing and physical resources of vehicles effectively. Most work on vehicular cloud is so far on the taxonomy definition level, and the dynamically changing amount of available resources characterizes vehicular cloud as more cumbersome and complex than the traditional Clouds. In this paper, we define and analyze the expected number of vehicles in a roadway segment. These vehicles can serve as the building blocks for the vehicular cloud, enabling a tremendously large set of applications that benefit the whole traffic system. To contribute with the analysis, two types of traffic scenarios are considered in this work. We use a macroscopic traffic model to investigate the free-flow traffic, and we utilize the queuing theory to observe the queuing-up traffic. The average number of vehicles within a roadway segment is calculated using stochastic models. The results show the boundaries on enabling vehicular cloud, allowing to determine a range of parameters for simulating vehicular clouds.
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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 | 0.001 |
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 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".