Machine Learning-Based Models for Real-time Traffic Flow Prediction in Vehicular Networks
Bibliographic record
Abstract
In recent years, ML-based models are gaining enormous attention from both the automotive industry and academia to support IoVs. Through the accurate prediction of traffic/road conditions, various safety and infotainment applications can efficiently utilize the network entities and enhance the quality of service. Topology control and mobility management protocols in IoVs, among others, would achieve higher efficiency through the support of real-time traffic flow forecasting. However, the current research trend on improving prediction accuracy refrains from answering the essential question of whether ML-based prediction schemes are suitable for real-time traffic prediction. To answer this question, a thorough extensive study to evaluate the efficiency of prediction- based traffic flow schemes is required. In this article, we investigate the effectiveness of various ML-based prediction models by considering both the prediction accuracy and computational time cost. Accordingly, we present rigorous quantitative analysis to identify the important factors that may restrict the use of ML-based prediction models to support real-time services in the IoV environment.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".