Scene‐based pedestrian safety performance model in mixed traffic situation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Compared to vehicle‐only collisions, traffic collisions with pedestrians are studied less because of insufficient data. However, with the development of image processing technology, a growing number of road user behavioural analyses have been conducted using video data. This study tries to extract road users’ movements from video data in order to analyse the conflict between pedestrian and vehicle and to evaluate pedestrian safety performance during conflicts. The time difference to collision (TDTC) parameter is used to fit the safety analysis on pedestrian‐involved conflicts. Scene‐based analysis, which evaluates the safety performance of traffic intersections and segments where several pedestrian–vehicle conflicts may happen together, is conducted using 91 groups of scene data. The parameters most related to pedestrian safety are located using a sensitivity test, a quantitative definition of pedestrian–vehicle conflict is then defined, and a scene‐based pedestrian safety performance evaluation model is built. The model can correctly detect nearly 94.4% of possibly dangerous traffic scenes. Other kinds of mixed traffic scenes can also be studied based on this research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 it