Headway Anomaly Detection for Lane-Based Queuing Status Classification Using Detector Presence Data: Comparative Study
Bibliographic record
Abstract
The identification of queuing status in traffic streams is a pivotal task with implications for various applications, such as level of service estimation and signal timing optimization. This study presents a comparative approach for binary classification of vehicles into queuing and non-queuing statuses using machine learning techniques based on vehicle headway anomalies. The proposed models leverage features from vehicle movement data at the stopline as input and queuing status as output. Four machine learning methods, the support vector machine, random forest, logistics regression, and Gaussian mixture model, are used and compared in the proposed headway anomaly detection framework. To validate the models, a simulation environment is constructed in VISSIM 4.3 and calibrated using real-world data. Results indicate that the random forest exhibits superior classification performance, showcasing its effectiveness as an ensemble approach. Notably, the resilience of these models is tested against the missing detection rate, with the random forest showing robust performance across different missing detection rate levels. Furthermore, feature importance analysis within the random forest model reveals “acceleration” and “headway” as significant predictors for classifying queuing status. The results advocate for the efficacy of the random forest model as a method for queuing status detection, indicating its utility for traffic analysis and the optimization of transportation network operations.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".