Development of Improved Models for Imputing Missing Traffic Counts
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
Estimating missing values is known as data imputation. A literature review indicates that many highway and transportation agencies in North America and Europe use various traditional methods to impute their traffic counts. These methods can be broadly categorized into factor and time series analysis approaches. However, little or no research has been conducted to assess the imputation accuracy. The literature indicates that the current practices are varied, and the methods used by highway agencies are intuitive in nature. Typical imputation methods used by highway agencies are identified and applied to data from six automatic traffic recorders (ATRs) in Alberta, Canada, to evaluate their accuracy. Statistical analysis shows that these traditional methods result in varying accuracy for traffic counts from different types of roads. In some cases, the imputation errors can be unacceptably high. Therefore, improved imputation methods are proposed. Study results indicate that imputation accuracy can be significantly improved by incorporating correction factors and data from both before and after the failure periods into the traditional models. The improved imputations should provide transportation practitioners better information for decision marking purposes.
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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