Comparative analysis of drivers' start‐up time of the first two vehicles at signalized intersections
Bibliographic record
Abstract
Summary The aim of this study is to comparatively analyze drivers' start‐up time (SUT) of the first two vehicles at signalized intersections. Three groups of data, the SUT of the first vehicle, the SUT of the second vehicle, and car‐following SUT of the second vehicle, were collected through a videography technique. The comparative analysis was then conducted. Pearson's correlation was used to determine the correlation between the three groups. Multiple regression was used to analyze and identify which independent variable or variable combination was the best explanation of the SUT of the second vehicle. The combination patterns of start‐up behavior of the first two vehicles were discovered using the fuzzy c‐means clustering. The data obtained show that the mean of the car‐following SUT of the second vehicle is 1.08 seconds. Four combination patterns of start‐up behavior of the first two vehicles, { fast , fast }, { fast , slow }, { slow , fast }, and { slow , slow }, are obtained. The numbers of the first pattern { fast , fast } and the fourth pattern { slow , slow } account for 71.80% and 21.82% of total patterns, respectively. These results suggest that the main factors influencing the SUT of the second vehicle are the type and SUT of the first vehicle. The results seem to indicate that the SUT of the first vehicle plays a crucial role in determining total start‐up lost time. The pattern { fast , fast } is the best for decreasing start‐up lost time. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.000 |
| 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".