New data-driven approach to generate typologies of road segments
Bibliographic record
Abstract
The main objective of this study is to put forward a typology to describe better associations between road segments and driving patterns as reflected by driving speed. As the first step, a regression model is developed to examine the association between road segments and driving speed. Then, various unsupervised machine learning techniques, including k-means, AHC, and k-proto, are used to develop typologies of road segments. Speed data from a fleet of taxis operating in Montreal, Quebec, are used to validate the discrimination power of the various typologies. Results demonstrate that combining k-means and Gower distance produces the most accurate road typology. Various statistical tests, including ANOVA, Leven, and post hoc analyses, confirmed that the speed values of the various road types are significantly different. Finally, R2 of regression models developed for various road types demonstrated that the generated road types better elucidate the variability of driving speed.
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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 | 0.001 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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".