Implementing survival analysis to capture stochastic characteristics of saturation flow rate considering the impacts of adverse road-weather conditions
Bibliographic record
Abstract
Saturation flow rate (SFR) variations were analyzed using the video data collected at a signalized intersection in Winnipeg, Canada, to investigate the implications of adverse road-weather (RW) conditions for SFR distributions and characteristics. Survival analysis was implemented to develop stochastic SFR distribution functions considering censored data and a statistical analysis method was developed for determining the optimal critical vehicle (CV) for measurement of saturation headway. The analysis findings suggest that adverse RW conditions decrease SFR significantly and moves CV to the front of the queue while having little impact on SFR considering heavy vehicles. Furthermore, the findings imply that the conventional SFR estimation method overestimates the probability of saturation at a given flow rate. The proposed analysis method reveals stochastic characteristics of SFR and provides a method to estimate SFR distributions under different RW conditions, which is essential for improving the operation of signalized intersections, particularly in cold regions.
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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 | 0.002 | 0.005 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".