Open Access Development of Improved Models for Imputing Missing Traffic Counts
Bibliographic record
Abstract
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 fac-tors 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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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 | 0.017 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".