Logistic Regression Model of Risk of Fatality in Vehicle–Pedestrian Crashes on National Highways in Bangladesh
Bibliographic record
Abstract
Pedestrian fatalities on national highways in Bangladesh account for about 47% of all pedestrian fatalities; therefore, it is important to identify the risk factors involved. Binary logistic models were calibrated with crash data from 1998 to 2006 maintained by the Accident Research Institute of Bangladesh to identify the factors associated with the probability of a fatal outcome. The results showed that the involvement of elderly pedestrians (individuals older than 55 years of age) and young pedestrians (individuals younger than 15 years of age) increased the likelihood of a fatality. A higher risk of fatality was also seen for pedestrians who crossed the road than for those who walked along the edges of the road. Pedestrian collisions with trucks, buses, baby taxis or tempos (auto rickshaws), and tractors had a higher risk of a fatality than collisions with cars. Crashes occurring at locations with no traffic control, stop control, and pedestrian crossings had a higher risk of a fatality than those occurring at locations with traffic signals or police control. Finally, collisions during the rainy season had a higher probability of a fatality. In addition, the influences of implicit attributes on the trend for the risk of a pedestrian injury, temporal confounding, and interaction effects were considered and were incorporated progressively into the model. A trend toward a slight increase in the risk of a pedestrian fatality was found when the model controlled for the influences of demographic factors, the road environment, and other risk factors. The identification of these risk factors for pedestrians provides valuable inputs that will assist with the development of a comprehensive pedestrian safety action plan.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".