Towards Green Transportation: Predictive Modeling of Intersection Congestion Using Machine Learning for Sustainable Urban Traffic Management
Bibliographic record
Abstract
One of the main consequences of urban pollution is intersection congestion, which occurs due to frequent vehicle stops. These interruptions lead to increased fuel consumption and greenhouse gas emissions (CO2), along with other pollutants such as nitrogen oxides (NOX) and fine particulates. These pollutants can adversely affect the respiratory, cardiac, and neurological health of city residents. To address the growing demand for smart and sustainable transportation systems in large cities, predicting intersection congestion using artificial intelligence offers a promising solution. In this study, we present a predictive modeling approach to classify congestion levels at intersections controlled by traffic lights. Using the CN+ dataset collected in Bremen, Germany, our methodology incorporates vehicle and environmental features to predict congestion levels, optimize traffic flow, and reduce pollutant emissions. We employ data preprocessing, feature engineering, and machine learning techniques, including an innovative feature selection method called Dual Importance Intersection Feature Selection (DIFS), which combines Random Forest (RF) and Chi-square analysis. We tested various classifiers, including RF, XGBoost, LightGBM, CatBoost, and Artificial Neural Network (ANN), utilizing SMOTE balancing to address the class imbalance. Performance metrics such as precision, recall, F1 score, overall accuracy, and Quadratic Weighted Kappa (QWK) demonstrate promising results, with F1 and QWK scores reaching 100%. This makes our approach a robust tool for managing traffic sustainably and efficiently.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".