Enhancing Freeway Safety: LSTM-Based Detection of Traffic Anomalies
Bibliographic record
Abstract
Early detection of freeway traffic anomalies, such as accidents and congestion, is crucial for improving traffic management and reducing response time to critical incidents. Traditional methods, relying on manual reporting or surveillance systems, suffer from delays and inaccuracies. Deep learning has emerged as a powerful alternative, using time-series sensor data for real-time incident detection. In this study, we propose an LSTM-based deep learning model for freeway anomaly detection using the Freeway Traffic Anomalous Event Detection (FT-AED) dataset. To address severe class imbalance, we employ a hybrid resampling strategy that integrates Random Undersampling (RUS) and the Synthetic Minority Over-sampling Technique (SMOTE), ensuring a balanced dataset for training. Experimental results demonstrate the effectiveness of our approach, achieving a test accuracy of 91.18 percent, recall of 0.83 for normal traffic, recall of 0.99 for anomalies and an overall F1-score of 0.91. Our model significantly reduces false positives while maintaining high recall for anomalies, making it a strong candidate for real-time freeway monitoring applications. The findings highlight the potential of LSTM-based models in automated traffic incident detection, improving decision-making for transportation authorities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
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".