Operating Speed Models for Tangent Segments on Urban Roads
Bibliographic record
Abstract
Information about operating speeds is essential to design better roads, model traffic emissions, and ensure design consistency while efficient and safe operations on roads are maintained. Therefore, understanding how different factors affect operating speeds and developing operating speed prediction models are critical research issues. Many studies have developed such models on rural roads and highways, but only a few studies have considered development of such models on urban roads and fewer still on tangential segments. This present study attempted to address these limitations by developing operating speed models with data from 249 tangential road segments in the city of Edmonton, Alberta, Canada. A generalized linear model was developed with panel data, and the primary aim was to explore the relationships between operating speeds on urban roads and features of the road environment. To study the impact of road elements on different road types, three models were created: one that combined arterial and collector locations, one for arterials only, and one for collector roads only. The results revealed that roads with sidewalks that were farther away from the road and with low object density or tree density were all associated with higher operating speeds. Locations with monolithic walks on both sides of the road had lower operating speeds. Furthermore, operating speeds decreased as access increased, while longer roads had higher operating speeds. One major takeaway was that the elements differed between road classes. The two variables, which stood out in that respect, were medians and bus stops.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".