A Comparative Study of Artificial Intelligence Algorithms for Network Traffic Prediction in VANET
Bibliographic record
Abstract
Increasing the number of vehicles and their communications in smart cities is a critical issue that leads to road and network traffic. Traffic prediction with high accuracy and less complexity is a challenge in Intelligent Transportation System (ITS). In Artificial Intelligence (AI), Machine learning (ML) algorithms are promising solutions to prediction problems, and Deep Learning (DL) algorithms are used for more complicated issues. In this paper, we propose a comparative analysis of the prediction performance of the most five common AI algorithms used to solve classification problems. Different evaluation metrics are employed to analyze algorithms and get the most accurate one for selecting such problems. Simulation results on the Vehicular Ad-Hoc Network (VANET) dataset revealed that Random Forest (RF) as a traditional ML algorithm performed better than other algorithms in terms of accuracy (96%) and execution time (0.73 minute) for traffic prediction.
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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 | 0.000 |
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 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".