Improved Vehicular Congestion Classification using Machine Learning for VANETs
Bibliographic record
Abstract
Vehicular Ad-hoc Networks (VANETs) emerge as an inevitable element for autonomous driving, smart cities and intelligent transportation systems. The vehicular traffic density classification plays a crucial role in making important traffic routing and data transfer decisions between vehicles and surrounding infrastructure. However, vehicular density in a given area vastly varies depending on the environment (urban, rural, highway etc.), the day and the specific time of the day. There can also be unpredictable density variations due to traffic incidents or social events. Therefore, accurate classification of traffic density is essential to properly plan data communication in VANET. This paper studies a number of machine learning (ML) algorithms to accurately classify the traffic condition based on the data collected from intelligent sensors. First, the traffic flow and average speed data is collected for each vehicle. In the second step, vehicular density is estimated using speed and flow relationship in a given area. In the third step, traffic state is classified as "Free-Flow", "Dense", and "Congested" based on the congestion cost report by Victoria Transport Policy Institute. Finally, we utilized a range of ML approaches, including Decision Tree (DT), Na¨ıve Bayes (NB), K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Classifier (SVC), Logistic Regression (LR), and Multilayer Perceptron (MLP) to categorize instances of traffic congestion. The results are studied based on classification accuracy, recall and precision metrics. The experimental results indicate that RF and subsequently Ensemble Soft Voting classifiers exhibit the best performance among all other classifiers, including the MLP model.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".