Modeling of pedestrian crash frequency at unsignalized intersections using traffic conflict indicators
Bibliographic record
Abstract
This study develops a predictive model to estimate pedestrian crash frequency at unsignalized intersections by analyzing pedestrian-vehicle conflicts using video data from Cheema Chowk and Duke Chowk in Ludhiana, Punjab. Utilizing AI techniques like YOLO and Deep SORT, key conflict indicators such as post-encroachment time (PET) and time to collision (TTC) were extracted. These indicators informed the construction of extreme value theory (EVT) models, with bivariate Copula models outperforming univariate ones in predictive accuracy, evidenced by low mean absolute percentage error (MAPE) values of 2.164% and 0.60%, respectively. The study demonstrates the efficacy of bivariate Copula models in forecasting pedestrian crashes, providing a proactive tool for enhancing road safety at unsignalized intersections by identifying high-risk areas and facilitating targeted safety interventions. This approach highlights the importance of incorporating multiple conflict indicators to improve predictive accuracy and reduce pedestrian casualties.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".