ASENet: A Deep Learning Approach to Predict Traffic Speed Compliance During Automated Speed Enforcement
Bibliographic record
Abstract
Speed enforcement is one of the most widely used countermeasures to curb speed violations. Automated Speed Enforcement (ASE) not only supplements traditional traffic enforcement but also provides valuable data on traffic violations for safety research. Numerous evaluations have been performed to assess the effectiveness of ASE programs in reducing speed violations. However, there is a significant gap in the research community regarding the prediction of traffic speed compliance during different ASE enforcement periods. To address this gap, this paper presents the first study to predict traffic speed compliance during various automated enforcement periods using a novel deep learning architecture. We collected traffic data from different ASE sites in the Durham Region of Ontario, Canada, covering the warning period, the active-ASE period, and the post-ASE period across multiple rotations. We categorized the features influencing changes in traffic speed compliance using Analysis of Variance and Ordinary Least Squares Regression. Performance evaluations demonstrate that the proposed deep learning approach can predict traffic speed compliance during different periods of automated speed enforcement with a very high degree of confidence.
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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 | 0.000 |
| 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 | 0.002 |
| 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".