Controlling the Vehicular Traffic Around Tunnels and Bridges Road Architectures Using VANETs
Bibliographic record
Abstract
Tunnels and bridges are installed as efficient context over the road network. Adding extra layers to an existing road network reduces the traffic congestion on that road. This is due to the distribution of traffic among the layers. However, bottleneck problems may appear at the entrance or exit points due to some driving behaviors. Moreover, the several exit points around these road contexts usually lead toward different trajectories. Taking a wrong exit leads to drastically expanding the trip traveling time, the fuel consumption, and the gas emission of any vehicle. This work aims to introduce an efficient driving assistance protocol that recommends drivers' optimal speed and behavior around tunnels and bridges. This mainly reduces the bottleneck constructions and enhances the movement's smooth-ness. Moreover, it accurately recommends the exit point there for each driver toward his/her targeted destination. The experimental study shows that the proposed protocol reduces the percentage of bottlenecks around the tackled road context. It also reduces vehicles' traveling time and distance during their trips toward the targeted destinations compared to the scenario of the absence of this protocol.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".