Speed Distribution Profile of Traffic Data and Sample Size Estimation
Bibliographic record
Abstract
Sample size estimation is fundamental to traffic engineering analysis. An iterative procedure using standard deviation estimates is the most reliable method of sample size estimation, but practitioners find it cumbersome. In the past, attempts have been made to simplify this process and render it a one-step exercise. To this end, the Institute of Transportation Engineers (ITE) manuals provide standard deviation values for spot speeds for roadways classified by annual average daily traffic volumes. This research obtained new estimates of standard deviation using a more granular, operational-level data collected by traffic detectors. It validated the earlier ITE estimates for moderate traffic conditions on freeways, but found the ITE estimates to be inadequate for arterials and for very low and heavy traffic conditions on freeways. This study also found a consistent U-shaped relationship between the standard deviation of speed and traffic volume, which alludes to an inherent speed distribution profile of traffic data. These speed distribution profiles are robust since they exist across different locations, roadway types, and aggregation intervals (5-, 15-, and 60-minutes). The U-shaped curves are also validated through existing traffic safety literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.046 | 0.209 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".