Data Fusion of Commercial Vehicle GPS and Roadside Intercept Survey Data
Bibliographic record
Abstract
GPS tracking technology produces large amounts of data which represent samples of the commercial vehicle population that are much larger than conventional commercial travel surveys. However, passively collected GPS data lack behavioral detail that a conventional survey offers. This study develops a data fusion method to impute variables of interest for a large GPS data set, by establishing a link to a behaviorally rich commercial travel survey data set. As a case study, this study uses detailed information from the Ministry of Transportation of Ontario’s Commercial Vehicle Survey (CVS), a truck intercept survey conducted in 2012, to enrich a GPS commercial vehicle tracking data set from Xata Turnpike Inc. The enrichment process has three parts: converting raw GPS tracking data into GPS trips, matching CVS trips to GPS trips, and imputing the missing variables for GPS trips. Evaluation of the outcomes concludes that imputation methods can produce a synthetic data set with large sample size (from GPS data) and rich information (from roadside interview data) with good accuracy.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".