Exploring vehicle probe data as a resource to enhance network-wide traffic volume estimates
Bibliographic record
Abstract
Annual average daily traffic (AADT) is a fundamental input for numerous civil engineering applications, yet generating reliable estimates of AADT at a network-wide level poses challenges. This article explores the potential use of vehicle probe data to enhance conventional traffic monitoring practice for generating network-wide estimates of AADT by exploring relationships between site-specific traffic volume data and vehicle probe data collected in Manitoba, Canada. The analysis revealed that mean travel speed cannot be used to predict traffic volumes on Manitoba highways, since the mean travel speed did not deviate from the free-flow speed regardless of the volume measured. The quantity of probe data observations showed moderate correlation with traffic volume at some sites (R-squared up to 0.65), but these correlations were stronger (R-squared up to 0.9) when considering trucks only. These findings suggest that probe data could be used to estimate truck volumes at certain locations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".