Estimation of Vehicle Stops Based on Modified Canadian Capacity Guide Formula Under Non-Lane Based Road Traffic Condition
Bibliographic record
Abstract
Abstract Vehicle stops estimation is one of the important parameters to assess the performance of a signalized road intersection. Canadian Capacity Guide provides a formula that can estimate number of vehicles that stops at least once due to the traffic signal. This study reviews the applicability of this formula for non-lane based traffic. The formula is segmented into two periods to check the estimation of stops during red period and green period. It is found that the formula underestimates the number of stops during red period. Also, the formula estimated number of stops is significantly higher during the green period. As traffic operation and vehicle maneuver of non-lane based traffic are much different from lane based disciplined traffic, the formula cannot predict vehicle stops accurately. Therefore, the estimation of vehicle stops by Canadian Capacity Guide formula is found to deviate considerably from field observed number of stops of vehicles. Thus, a modified regression formula is provided that can estimate the number of stops of vehicles for non-lane based traffic operation. The modified formula fits fairly good with the local traffic condition.
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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".