Network Traffic Prediction Model Considering Road Traffic Parameters Using Artificial Intelligence Methods in VANET
Why this work is in the frame
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Bibliographic record
Abstract
Vehicular Ad hoc Networks (VANETs) are established on vehicles that are intelligent and can have Vehicle-to-Vehicle (V2V) and Vehicle-to-Road Side Units (V2R) communications. In this paper, we propose a model for predicting network traffic by considering the parameters that can lead to road traffic happening. The proposed model integrates a Random Forest- Gated Recurrent Unit- Network Traffic Prediction algorithm (RF-GRU-NTP) to predict the network traffic flow based on the traffic in the road and network simultaneously. This model has three phases including network traffic prediction based on V2R communication, road traffic prediction based on V2V communication, and network traffic prediction considering road traffic happening based on V2V and V2R communication. The hybrid proposed model which implements in the third phase, selects the important features from the combined dataset (including V2V and V2R communications), by using the Random Forest (RF) machine learning algorithm, then the deep learning algorithms to predict the network traffic flow apply, where the Gated Recurrent Unit (GRU) algorithm gives the best results. The simulation results show that the proposed RF-GRU-NTP model has better performance in execution time and prediction errors than other algorithms which used for network traffic prediction.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it