A Comparative Study of Stationary and Dynamic Vehicular Micro Clouds: A Case of Vehicle Platooning for Incident Management
Bibliographic record
Abstract
Vehicular communication enables advanced incident management to overcome non-recurrent disruptions and severe congestion. While traditional stationary vehicular clouds, fixed to roadside infrastructure are innovative, they could experience extensive data traffic and infrastructure communication delays. Dynamic vehicular clouds could provide a promising solution to overcome these challenges to reduce communication latency through cooperative message dissemination whilst maintaining stable vehicle maneuvers. This study offers a comparative performance analysis of freeway incident management, using speed and lane-changing advisories, deployed with stationary and platoon-based dynamic vehicular micro clouds (VMCs). The system-level communication features, driving safety, and vehicular mobility are measured to evaluate the efficacy of both systems under incident-induced traffic management. The communication latency and packet loss ratio of the stationary cloud is on average 6.2% and 4.8% higher than those of the dynamic clouds, respectively. In addition, the dynamic cloud shows advantages in reducing travel time delay and risk of collisions, even at high connected vehicle penetration rates. Our work also aims to highlight the novel communication features of cellular technologies (4G LTE, 5G), that could reduce communication delay and interference with the deployment of advanced traffic management strategies. The research sets a foundation to deploy novel vehicle communication architectures while quantifying the advantages of vehicular clouds for managing freeway traffic.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".