Pedestrian‐Vehicle Conflict Identification Method and Safety Evaluation Model Based on an Improved TAdv: An Example of Signal Intersections in China
Bibliographic record
Abstract
Due to the high incidence of pedestrian collisions at intersections and the vulnerable status of pedestrians, an increasing number of researchers are focusing on pedestrian safety. Currently, researchers tend to apply surrogate safety measures (SSMs) in pedestrian safety analysis, with Time to Collision (TTC) and Post Encroachment Time (PET) being the most widely used. However, their application is subject to certain conditions and limitations. Therefore, this study proposes a novel traffic conflict indicator called improved Time Advantage (improved TAdv), designed to identify pedestrian‐vehicle conflicts without collision trajectories. A conflict model for right‐turning vehicles and pedestrians at signalized intersections is developed to analyze the impact of various factors on the probability of pedestrian‐vehicle conflicts. The results indicate that the improved TAdv offers advantages over traditional traffic conflict indicators. Meanwhile, the findings emphasize the spatial disparities of right‐turning pedestrian‐vehicle conflicts and analyze the impact of vehicle speed at different locations on the likelihood of such conflicts, offering new insights into pedestrian safety analysis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".