Assessing Robustness of Imputation Models Based on Data from Different Jurisdictions
Bibliographic record
Abstract
The literature indicates that many highway and transportation agencies in North America and Europe estimate missing values in their collected traffic data records. Estimating missing values is known as data imputation. Such a convention can be traced back to early traffic monitoring systems in the 1930s; however, no studies have been found to assess the accuracy of imputations carried out by transportation practitioners. The imputation methods used by highway agencies are varied and intuitive in nature. Some of them could result in large imputation errors in certain circumstances. Those errors can lead to significant deviations in the resulting operation plans and designed structures. Therefore, it is necessary to evaluate the accuracy of various imputation methods that highway agencies use. This study identifies and tests typical imputation methods on automatic traffic recorder data from Alberta and Saskatchewan in Canada. With assessment of imputation methods based on the data from different highway agencies, it is possible to evaluate their robustness and suitability for use across jurisdictions. The accuracy of individual imputation models was statistically analyzed, and comparisons and recommendations were made. Study results clearly indicate that models using additional observations as input and more sophisticated prediction techniques consistently produce better imputations. It is believed that this study would be helpful for traffic engineers in reviewing their imputation practices and, hence, in improving their data quality.
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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 | 0.263 | 0.529 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".