Improving Freeway Network Mobility: A Comparative Study of Vehicle Cloudification and VANET Architectures
Bibliographic record
Abstract
Vehicular ad hoc networks (VANET) are a traditional approach to providing minimal reliance on existing infrastructure, though they can experience high communication overhead and network disruptions. Vehicular micro clouds (VMCs) provide a promising solution to overcome the challenges of VANET by reducing communication latency through collaborative data allocation and data offloading. This paper offers a comparative performance analysis of VANET communications versus stationary and dynamic VMCs. Specifically, it studies incident management through speed and lane-changing advisories and freeway platooning. To further enhance the analysis, the performance of both communication architectures is evaluated using DSRC communication protocols versus cellular technologies (C-V2X, 4G LTE, and 5G NR). The system-level features, such as driving safety and vehicular mobility are measured to evaluate the efficacy of the communication systems under free-flow and incident-induced traffic conditions. The stationary cloud's latency and packet loss ratio are found to be 6.2% and 4.8% higher than those of the dynamic clouds, respectively. In addition, the stationary and dynamic cloud systems show advantages in reducing travel time delay, even at high penetration rates of the connected vehicles. The results suggest a shift towards more reliance on connected vehicular clouds to minimise the risks of message interference and system overload, whilst fostering intelligent freeway traffic management systems.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".