Method for Imputing Missing Data using Online Calibration for Urban Freeway Control
Bibliographic record
Abstract
Real-time traffic control systems are widely implemented on roadways around the world as a measure to improve freeway mobility. However, the systems, which rely on data from road-side and on-road sensors and other electronic equipment, continue to suffer from issues related to missing and erroneous data. While many data imputation methods are documented in the related literature, traffic control systems still lack an imputation method that is applicable in practice, accurate in imputation, and simple in computation. In response, this paper puts forth a linear imputation model that considers both temporal traffic trend and spatial detector correlations. To adapt the model to dynamic traffic variations, the imputation method was equipped with an online calibration module. The proposed imputation method was evaluated with field data from two stations on the Whitemud Drive, a busy urban freeway in Edmonton, Alberta, Canada. The proposed model benefited from its time-of-day temporal trend and outperforms the previous model that considers only spatial correlations. Moreover, the online calibration module was effective in improving imputation accuracy. Finally, the sensitivity of imputation performance was analyzed. The results show that the imputation with online calibration is more sensitive to missing data ratios than that with offline calibration. The sensitivity analysis revealed that imputation with online calibration is more suitable for online imputation in traffic control implementations.
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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 | 0.016 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".