Hybrid Approach for Clustering Vehicle Classification Data to Support Regional Implementation of the Mechanistic–Empirical Pavement Design Guide
Bibliographic record
Abstract
This paper develops a hybrid approach for analyzing vehicle classification data and applies the approach to a fused data set from multiple jurisdictions in the Canadian prairie region. Application of the approach results in a set of regional default truck traffic classification groups for use in the Mechanistic–Empirical Pavement Design Guide. The hybrid approach is a conglomeration of three components: statistical clustering procedures, expert judgment, and industry intelligence. By applying the hybrid approach, analysts receive the joint benefits of analytical rigor and industry-oriented pragmatism. Application of this approach results in eight truck traffic classification groups for the Canadian prairie region that exhibit distinct differences from the default distributions developed for national use in the United States. The benefits of applying the hybrid approach on fused data sets include (a) the statistical strength gained from use of additional classification data, (b) the development of truck traffic classification groups that better reflect the diversity of patterns in a region, and (c) the potential for improved ability to capture future shifts in truck traffic characteristics because of experience gained in other jurisdictions. The paper also identifies limitations to the hybrid approach that should be considered. These limitations include varying data quality between jurisdictions, the sensitivity of low-volume sites to changes in industry patterns and the ability to track these changes, and potential shortages of continuous classification sites. When its benefits and limitations are well understood, the hybrid approach can be applied to truck traffic data analyses in any jurisdiction.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".