Modelling adolescent pedestrian crossing decision at unmarked roadway
Bibliographic record
Abstract
To address adolescent pedestrian safety problems at unmarked roadway, there is a need to understand adolescent pedestrian crossing decision behavior at unmarked roadway. cloud model, which is an uncertainty conversion model between qualitative knowledge description and quantitative value expression, was used to deal with adolescent pedestrian's cognitive uncertainty in the crossing decisionmaking process. Then the decision table for adolescent pedestrian crossing at unmarked roadway was established. Attribute reduction based on discernibility matrix and value reduction based on induction in the rough set theory were applied to reduce the decision table and extract the decision rules of adolescent pedestrian crossing at unmarked roadway. After the reduction, the conditional attributes were the vehicle speed and the distance between pedestrian and vehicle. Nine crossing decision rules were obtained, including five certainty rules. Finally, the proposed method was compared with the existing method. The results show that the prediction accuracy and area under the receiver operating characteristic (rOc) curve for the proposed method are 91.4% and 0.941 respectively, so it is superior to the logistic regression model, and the simple and intuitive pedestrian crossing decision rules can be obtained, which can lay the foundation for traffic safety simulation.
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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".