Estimation of critical gap on a roundabout by minimizing the sum of absolute difference in accepted gap data
Bibliographic record
Abstract
Estimation of critical gap for a vehicle type under mixed traffic conditions prevailing in developing countries has been always a challenging task. This is due to the poor lane discipline and very limited priority being followed by the vehicles at priority intersections like roundabouts. A simple procedure, which is based on minimization of the sum of absolute difference between a gap value and accepted or rejected gap, is proposed in this paper. The iterative procedure provides a value of gap that is termed as the critical gap under mixed traffic conditions. The method is different from maximum likelihood method (MLM) in two aspects. First, it does not assume any predefined distribution for the critical gap and second, it does not fail even if there is no rejection of gaps which is very common under limited (or no) priority conditions. Field data are collected at a roundabout in India. Prominent methods available in literature to estimate critical gap are compared for different categories of vehicles. Based on the results of consistency test, the MLM and the proposed method are found to be the most acceptable estimation methods. It has been further observed that the proposed method is better than MLM when working with low sample size, as well as, in no-priority conditions, which arise due to heterogeneous traffic flow prevailing in developing countries. The results are validated on another roundabout having similar physical features.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".