Effect of missing value imputations on traffic parameters estimations from permanent traffic counts
Bibliographic record
Abstract
ABSTRACT: Studies indicate that usually a large percentage of permanent traffic counts (PTCs) from highway agencies have missing values. It will be difficult to eliminate such a significant portion of the data from the analysis. The literature review shows that many highway agencies estimated missing values for their PTCs. Estimating missing values is known as data imputation. However, only limited research used factor or time series analysis models for predicting missing values. Moreover, studies of the effect of the imputations on traffic parameters estimations are not available. This study used factor models, genetically designed neural network and regression models, and autoregressive integrated moving average (ARIMA) models to update pseudo-missing values of six PTCs from Alberta, Canada. The six PTCs are from roads of different trip pattern groups and functional classes. The influences of these imputations on the estimations of annual average daily traffic (AADT) and design hourly volume (DHV) were studied. It was found that simple models usually resulted in large AADT and DHV estimation errors. For example, AADT estimation error for a simple factor model was 13.54%, and DHV estimation error was 18.46%. As models were refined, resulting
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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 | 0.143 | 0.488 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".