Imputation of Missing Traffic Data during Holiday Periods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Highway and transportation agencies implement large-scale traffic monitoring programs to fulfill the planning, operation and management needs of highway systems. These monitoring programs typically use inductive loops as detectors to collect traffic data. Because of the harsh environment in which they operate, they are highly prone to malfunctioning and providing erroneous or missing data. If this occurs during holiday periods when the increase in highway traffic is often substantial, there is a good chance that traffic peaking and variation will be underestimated. This paper discusses the adaptability of available imputation techniques for holiday traffic and then introduces a new procedure using non-parametric regression – the k-nearest neighbor (k-NN) method. It is found that the performance of the k-NN method is consistent and reasonable for different holidays and types of highway. In addition, it is also concluded that the data requirements for this method are flexible.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it