Urban traffic characterization for enabling Vehicular Clouds
Bibliographic record
Abstract
The accelerated growth of applications and services in intelligent transportation systems (ITS) are driven by interests from the public and private sectors. The intent to utilize the onboard resources, along with the advanced methods of managing the available computing capabilities in the conventional cloud, has led to the high popularity of Vehicular Clouds. Likewise in Vehicular Networks, vehicles provide the building blocks for forming these particular clouds, which can enable a large number of applications and services that can benefit the whole transportation system, as well as the drivers, passengers, and pedestrians. However, due to its high mobility, Vehicular Clouds show several inherent challenges, which increase complexity and restrict the design of solutions. Determining the number of vehicles and their time of availability in a given region through a model works as a critical stepping stone for enabling vehicular clouds, as well as any other system involving vehicles moving over the traffic network. Therefore, by implementing proper traffic models, we present a comprehensive stochastic analysis about the distribution of the number of vehicles inside a road segment in this paper. According to real parameters, we show that certain classes of applications are feasible even for highly mobile scenarios.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".